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Dedication
Foreword
Acknowledgements
Contents
List of Figures
1: Don’t Believe the Hype
2: Why Now?
3: AI Capabilities Framework
4: Associated Technologies
5: AI in Action
6: Starting an AI Journey
7: AI Prototyping
8: What Could Possibly Go Wrong?
9: Industrialising AI
10: Where Next for AI?
Index

THE EXECUTIVE GUIDE TO

ARTIFICIAL INTELLIGENCE

How to identify and implement applications

for AI in your organization

ANDREW BURGESS

ANDREW BURGESSANDREW BURGESSANDREW BURGESS

THE

THETHE

THE

EXECUTIVE GUIDE

EXECUTIVE GUIDEEXECUTIVE GUIDE

EXECUTIVE GUIDE

EXECUTIVE GUIDEEXECUTIVE GUIDE

EXECUTIVE GUIDE

EXECUTIVE GUIDEEXECUTIVE GUIDE

EXECUTIVE GUIDE

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCEARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCEARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

The Executive Guide to Artiﬁcial Intelligence

AndrewBurgess

The Executive Guide

to Artiﬁcial

Intelligence

How to identify and implement

applications for AI in your organization

ISBN 978-3-319-63819-5 ISBN 978-3-319-63820-1 (eBook)

https://doi.org/10.1007/978-3-319-63820-1

Library of Congress Control Number: 2017955043

is work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the

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tion storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology

now known or hereafter developed.

e use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does

not imply, even in the absence of a specic statement, that such names are exempt from the relevant protective

laws and regulations and therefore free for general use.

e publisher, the authors and the editors are safe to assume that the advice and information in this book are

believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors

give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions

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and institutional aliations.

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Printed on acid-free paper

is Palgrave Macmillan imprint is published by Springer Nature

e registered company is Springer International Publishing AG

e registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

AndrewBurgess

AJBurgess Ltd

London, United Kingdom

is book is dedicated to my wonderful wife, Meg, and our two amazing

children, James and Charlie.

vii

I remember well the AI work I did whilst at college studying computer

science, how dierent and fascinating it was and still is. We were set a very

open challenge to write an AI programme on any subject. I decided to

write mine so that it could tell you if the building in a photo was a house,

a at or a bungalow. Somewhat impractical, but a great learning experi-

ence for me, particularly in understanding how AI is dierent from tradi-

tional software.

Although my college days were a number of years ago, since that time the

concept of computers learning has always intrigued me and I have since won-

dered how long it will take for AI to have a truly widespread impact. In recent

years, we’ve seen massive improvements in processing power, big data collec-

tion via sensors and the Internet of ings, cloud services, storage, ubiquitous

connectivity and much more. ese technological leaps mean that this is the

right time for AI to become part of the ‘here and now’ and I strongly believe

we will see a dramatic increase in the use of AI over the next few years.

e AI in use today is known as narrow AI because it can excel at thousands

of relatively narrow tasks (e.g. doing internet searches, playing Go or looking

for fraudulent transactions). ings will certainly get even more exciting when

‘general AI’ can outperform humans at nearly every task we do, but we simply

don’t know when this might be, or what the world will then look like. Until

then, what excites me most is how we can apply AI now to solve our day-to-

day problems at home and work.

So why is AI important and how can we use it? Firstly, if you are impatient

(like I am), doing small manual, repetitive tasks on computers simply takes

too much time. I want the computer to do a better job of anticipating my

needs and to just get on with it. If I could, I would prefer to talk to Alexa or

Foreword

viii Foreword

Google Assistant and just tell the computer what to do. For example, I would

love to be able to ask Alexa to buy the most convenient train ticket and take

the money out of my account. Compare this to buying a train ticket on any

website, where after something like 50 key strokes you might have bought a

ticket. I don’t think future generations, who are becoming increasingly impa-

tient, will put up with doing these simple and time-consuming tasks. I see my

children and future generations having more ‘thinking time’ and focusing on

things that are outside the normal tasks. AI may in fact free up so much of my

children’s time that they can nally clean up their bedrooms.

In the workplace, how many of the emails, phone calls and letters in a call

centre could be handled by AI? At Virgin Trains, we used AI to reduce the time

spent dealing with customer emails by 85% and this enabled our people to focus

on the personable customer service we’re famous for. Further improvements will

no doubt be possible in the future as we get better at developing conversational

interfaces, deep learning and process automation. One can imagine similar

developments revolutionising every part of the business, from how we hire peo-

ple to how we measure the eectiveness of marketing campaigns.

So, what about the challenges of AI? One that springs to mind at Virgin is

how to get the ‘tone of voice’ right. Our people are bold, funny and empa-

thetic, and our customers expect this from us in every channel. Conversational

interfaces driven by AI should be no dierent.

Today it may be a nuisance if your laptop crashes, but it becomes all the

more important that an AI system does what you want it to do if it controls

your car, your airplane or your pacemaker. With software systems that can

learn and adapt, we need to understand where the responsibility lies when

they go wrong. is is both a technical and an ethical challenge. Beyond this,

there are questions about data privacy, autonomous weapons, the ‘echo cham-

ber’ problem of personalised news, the impact on society as increasing num-

bers of jobs can be automated and so on.

Despite these challenges, I am incredibly excited about the future of tech-

nology, and AI is right at the heart of the ‘revolution’. I think over the next

ve to ten years AI will make us more productive at work, make us more

healthy and happy at home, and generally change the world for the better.

To exploit these opportunities to the full, businesses need people who

understand these emerging technologies and can navigate around the chal-

lenges. is book is essential reading if you want to understand this transfor-

mational technology and how it will impact your business.

John Sullivan, CIO and Innovation at Virgin Trains

I would like to thank the following people for providing valuable input, con-

tent and inspiration for this book:

Andrew Anderson, Celaton

Richard Benjamins, Axa

Matt Buskell, Rainbird

Ed Challis, Re:infer

Karl Chapman, Riverview Law

Tara Chittenden, e Law Society

Sarah Clayton, Kisaco Research

Dana Cue, Aldermore

Rob Divall, Aldermore

Gerard Frith, Matter

Chris Gayner, Genfour

Katie Gibbs, Aigen

Daniel Hulme, Satalia

Prof. Mary Lacity, University of Missouri-St Louis

Prof. Ilan Oshri, Loughborough University

Stephen Partridge, Palgrave

Mike Peters, Samara

Chris Popple, Lloyds Bank

John Sullivan, Virgin Trains

Cathy Tornbaum, Gartner

Acknowledgements

x Acknowledgements

Vasilis Tsolis, Congnitiv+

Will Venters, LSE

Kim Vigilia, Conde Naste

Prof. Leslie Willcocks, LSE

Everyone at Symphony Ventures

Contents

1 Don’t Believe theHype 1

2 Why Now? 11

AI Capabilities Framework 29

4 Associated Technologies 55

5 AI inAction 73

Starting anAI Journey 91

7 AI Prototyping 117

What Could Possibly GoWrong? 129

9 Industrialising AI 147

Where Next forAI? 165

Index 177

xiii

List of Figures

Fig. 2.1 Basic neural network 21

Fig. 2.2 Training a neural network 21

Fig. 2.3 A trained neural network 22

Fig. 3.1 AI objectives 30

Fig. 3.2 Knowledge map 43

Fig. 3.3 e AI framework 51

Fig. 4.1 Human in the loop 68

Fig. 4.2 Training through a human in the loop 69

Fig. 4.3 Crowd-sourced data training 69

Fig. 6.1 Aligning with the business strategy 93

Fig. 6.2 AI maturity matrix 100

Fig. 6.3 AI heat map rst pass 102

Fig. 6.4 AI heat map 104

Fig. 9.1 AI Eco-System 148

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_1

Don’t Believe theHype

Introduction

Read any current aairs newspaper, magazine or journal, and you are likely to

nd an article on articial intelligence (AI), usually decrying the way the

‘robots are taking over’ and how this mysterious technology is the biggest risk

to humanity since the nuclear bomb was invented. Meanwhile the companies

actually creating AI applications make grand claims for their technology,

explaining how it will change peoples’ lives whilst obfuscating any real value

in a mist of marketing hyperbole. And then there is the actual technology

itself—a chimera of mathematics, data and computers—that appears to be a

black art to anyone outside of the developer world. No wonder that business

executives are confused about what AI can do for their business. What exactly

is AI? What does it do? How will it benet my business? Where do I start? All

of these are valid questions that have been, to date, unanswered, and which

this book seeks to directly address.

Articial Intelligence, in its broadest sense, will have a fundamental impact

on the way that we do business. Of that there is no doubt. It will change the

way that we make decisions, it will enable completely new business models to

be created and it will allow us to do things that we never before thought pos-

sible. But it will also replace the work currently being done by many knowl-

edge workers, and will disproportionally reward those who adopt AI early and

eectively. It is both a huge opportunity and an ominous threat wrapped up

in a bewildering bundle of algorithms and jargon.

But this technological revolution is not something that is going to happen in

the future; this is not some theoretical exercise that will concern a few businesses.

Articial Intelligence is being used today in businesses to augment, improve and

change the way that they work. Enlightened executives are already working out

how AI can add value to their businesses, seeking to understand all the dierent

types of AI and working out how to mitigate the risks that it inevitably brings.

Many of those eorts are hidden or kept secret by their instigators, either because

they don’t want the use of AI in their products or services to be widely known,

or because they don’t want to give away the competitive advantage that it

bestows. A persistent challenge for executives that want to get to grips with AI is

where to nd all the relevant information without resorting to fanciful articles,

listening to vendor hyperbole or trying to understand algorithms. AI is rmly in

the arena of ‘conscious unknowns’—we know that we don’t know enough.

People generally experience AI rst as consumers. All our smartphones

have access to sophisticated AI, whether that is Siri, Cortana or Google’s

Assistant. Our homes are now AI enabled through Amazon’s Alexa and Google

Home. All of these supposedly make our lives easier to organise, and generally

they do a pretty good job of it. But their use of AI is actually pretty limited.

Most of them rely on the ability to turn your speech into words, and then

those words into meaning. Once the intent has been established, the rest of

the task is pretty standard automation; nd out the weather forecast, get train

times, play a song. And, although the speech recognition and natural lan-

guage understanding (NLU) capabilities are very clever in what they achieve,

AI is so much more than that, especially in the world of business.

Articial Intelligence can read thousands of legal contracts in minutes and

extract all the useful information out of them; it can identify cancerous

tumours with greater accuracy than human radiologists; it can identify fraud-

ulent credit card behaviour before it happens; it can drive cars without drivers;

it can run data centres more eciently than humans; it can predict when

customers (and employees) are going to desert you and, most importantly, it

can learn and evolve based on its own experiences.

But, until business executives understand what AI is, in simple-enough

terms, and how it can help their business, it will never reach its full potential.

ose with the foresight to use and exploit AI technologies are the ones that

need to know what it can do, and understand what they need to do to get

things going. at is the mission of this book. I will, over the course of the ten

chapters, set out a framework to help the reader get to grips with the eight

core capabilities of AI, and relate real business examples to each of these. I will

provide approaches, methodologies and tools so that you can start your AI

journey in the most ecient and eective way. I will also draw upon inter-

views and case studies from business leaders who are already implementing

AI, from established AI vendors, and from academics whose work focuses on

the practical application of AI.

1 Don’t Believe theHype

Introducing theAI Framework

My AI Framework was developed over the past few years through a need to be

able to make sense of the plethora of information, misinformation and

marketing- speak that is written and talked about in AI.I am not a computer

coder or an AI developer, so I needed to put the world of AI into a language

that business people like myself could understand. I was continually frus-

trated by the laziness in the use of quite specic terminology in articles that

were actually meant to help explain AI, and which only made people more

confused than they were before. Terms like Articial Intelligence, Cognitive

Automation and Machine Learning were being used interchangeably, despite

them being quite dierent things.

rough my work as a management consultant creating automation strate-

gies for businesses, through reading many papers on the subject and speaking

to other practitioners and experts, I managed to boil all the available informa-

tion down into eight core capabilities for AI: Image Recognition, Speech

Recognition, Search, Clustering, NLU, Optimisation, Prediction and

Understanding. In theory, any AI application can be associated with one or

more of these capabilities.

e rst four of these are all to do with capturing information—getting

structured data out of unstructured, or big, data. ese Capture categories are

the most mature today. ere are many examples of each of these in use today:

we encounter Speech Recognition when we call up automated response lines;

we have Image Recognition automatically categorising our photographs; we

have a Search capability read and categorise the emails we send complaining

about our train being late and we are categorised into like-minded groups

every time we buy something from an online retailer. AI eciently captures

all this unstructured and big data that we give it and turns it into something

useful (or intrusive, depending on your point of view, but that’s a topic to be

discussed in more detail later in the book).

e second group of NLU, Optimisation and Prediction are all trying to

work out, usually using that useful information that has just been captured,

what is happening. ey are slightly less mature but all still have applications

in our daily lives. NLU turns that speech recognition data into something

useful—that is, what do all those individual words actually mean when they

are put together in a sentence? e Optimisation capability (which includes

problem solving and planning as core elements) covers a wide range of uses,

including working out what the best route is between your home and work.

And then the Prediction capability tries to work out what will happen next—

if we bought that book on early Japanese cinema then we are likely to want to

buy this otherbook on Akira Kurosawa.

Introducing theAI Framework

Once we get to Understanding, it’s a dierent picture all together.

Understanding why something is happening really requires cognition; it

requires many inputs, the ability to draw on many experiences, and to con-

ceptualise these into models that can be applied to dierent scenarios and

uses, which is something that the human brain is extremely good at, but AI,

to date, simply can’t do. All of the previous examples of AI capabilities have

been very specic (these are usually termed Narrow AI) but Understanding

requires general AI, and this simply doesn’t exist yet outside of our brains.

Articial General Intelligence, as it is known, is the holy grail of AI researchers

but it is still very theoretical at this stage. I will discuss the future of AI in the

concluding chapter, but this book, as a practical guide to AI in business today,

will inherently focus on those Narrow AI capabilities that can be implemented

now.

You will already be starting to realise from some of the examples I have

given already that when AI is used in business it is usually implemented as a

combination of these individual capabilities strung together. Once the indi-

vidual capabilities are understood, they can be combined to create meaningful

solutions to business problems and challenges. For example, I could ring up a

bank to ask for a loan: I could end up speaking to a machine rather than a

human, in which case AI will rst be turning my voice into individual words

(Speech Recognition), working out what it is I want (NLU), deciding whether

I can get the loan (Optimisation) and then asking me whether I wanted to

know more about car insurance because people like me tend to need loans to

buy cars (Clustering and Prediction). at’s a fairly involved process that

draws on key AI capabilities, and one that doesn’t have to involve a human

being at all. e customer gets great service (the service is available day and

night, the phone is answered straight away and they get an immediate response

to their query), the process is ecient and eective for the business (operating

costs are low, the decision making is consistent) and revenue is potentially

increased (cross-selling additional products). So, the combining of the indi-

vidual capabilities will be key to extracting the maximum value from AI.

e AI Framework therefore gives us a foundation to help understand what

AI can do (and to cut through that marketing hype), but also to help us apply

it to real business challenges. With this knowledge, we will be able to answer

questions such as; How will AI help me enhance customer service? How will

it make my business processes more ecient? And, how will it help me make

better decisions? All of these are valid questions that AI can help answer, and

ones that I will explore in detail in the course of this book.

1 Don’t Believe theHype

Deﬁning AI

It’s interesting that in most of the examples I have given so far people often

don’t even realise they are actually dealing with AI.Some of the uses today,

such as planning a route in our satnav or getting a phrase translated in our

browser, are so ubiquitous that we forget that there is actually some really

clever stu happening in the background. is has given rise to some tongue-

in- cheek denitions of what AI is: some say it is anything that will happen in

20years’ time, others that it is only AI when it looks like it does in the movies.

But, for a book on AI, we do need a concisedenition to work from.

e most useful denition of AI I have found is, unsurprisingly, from the

Oxford English Dictionary, which states that AI is “the theory and develop-

ment of computer systems able to perform tasks normally requiring human

intelligence”. is denition is a little bit circular since it includes the word

‘intelligence’, and that just raises the question of what is intelligence, but we

won’t be going into that philosophical debate here.

Another denition of AI which can be quite useful is from Andrew Ng, who

was most recently the head of AI at the Chinese social media rm, Baidu, and

a bit of a rock star in the world of AI.He reckons that any cognitive process

that takes a human under one second to process is a potential candidate for

AI.Now, as the technologies get better and better this number may increase

over time, but for now it gives us a useful benchmark for the capabilities of AI.

Another way to look at AI goes back to the very beginnings of the technol-

ogy and a very fundamental question: should these very clever technologies

seek to replace the work that human beings are doing or should they augment

it? ere is a famous story of the two ‘founders’ of AI, both of whom were at

MIT: Marvin Minsky and Douglas Engelbart. Minsky declared “We’re going

to make machines intelligent. We are going to make them conscious!” To

which Engelbart reportedly replied: “You’re going to do all that for the

machines? What are you going to do for the people?” is debate is still raging

on today, and is responsible for some of those ‘robots will take over the world’

headlines that I discussed at the top of this chapter.

The Impact ofAI onJobs

It is clear that AI, as part of the wider automation movement, will have a

severe impact on jobs. ere are AI applications, such as chatbots, which can

be seen as direct replacements for call centre workers. e ability to read thou-

sands of documents in seconds and extract all the meaningful information

The Impact ofAI onJobs

from them will hollow out a large part of the work done by accountants and

junior lawyers. But equally, AI can augment the work done by these groups as

well. In the call centre, cognitive reasoning systems can provide instant and

intuitive access to all of the knowledge that they require to do their jobs, even

if it is their rst day on the job—the human agent can focus on dealing with

the customer on an emotional level whilst the required knowledge is provided

by the AI.e accountants and junior lawyers will now have the time to prop-

erly analyse the information that the AI has delivered to them rather than

spend hours and hours collating data and researching cases.

Whether the net impact on work will be positive or negative, that is, will

automation create more jobs than it destroys, is a matter of some debate.

When we look back at the ‘computer revolution’ of the late twentieth century

that was meant to herald massive increases in productivity and associated job

losses, we now know that the productivity benets weren’t as great as people

predicted (PCs were harder to use than rst imagined) and the computers

actually generated whole new industries themselves, from computer games to

movie streaming. And, just like the robots of today, computers still need to be

designed, manufactured, marketed, sold, maintained, regulated, xed, fuelled,

upgraded and disposed of.

e big question, of course, is whether the gains from associated activities

plus any new activities created from automation will outweigh the loss of jobs

that have been replaced. I’m an optimist at heart, and my own view is that we

will be able to adapt to this new work eventually, but not before going through

a painful transition period (which is where a Universal Basic Income may

become a useful solution). e key factor therefore is the pace of change, with

all the indicators at the moment suggesting that the rate will only get faster in

the coming years. It’s clear that automation in general, and AI in particular,

are going to be huge disruptors to all aspects of our lives—most of it will be

good but there will be stu that really challenges our morals and ethics. As

this book is a practical guide to implementing AI now, I’ll be exploring these

questions in a little more detail toward the end of the book, but the main

focus is very much on the benets and challenges of implementing AI today.

A Technology Overview

e technology behind AI is endishly clever. At its heart, there are algo-

rithms: an algorithm is just a sequence of instructions or a set of rules that are

followed to complete a task, so it could simply be a recipe or a railway time-

table. e algorithms that power AI are essentially very complicated statistical

1 Don’t Believe theHype

models—these models use probability to help nd the best output from a

range of inputs, sometimes with a specic goal attached (‘if a customer has

watched these lms, what other lms would they also probably like to watch?’).

is book is certainly not about explaining the underlying AI technology; in

fact, it is deliberately void of technology jargon, but it is worth explaining

some of the principles that underpin the technology.

One of the ways that AI technologies are categorised is between ‘supervised’

and ‘unsupervised’ learning. Supervised learning is the most common

approach out of the two and refers to situations where the AI system is trained

using large amounts of data. For example, if you wanted to have an AI that

could identify pictures of dogs then you would show it thousands of pictures,

some of which had dogs and some of which didn’t. Crucially, all the pictures

would have been labelled as ‘a dog picture’ or ‘not a dog picture’. Using

machine learning (an AI technique which I’ll come on to later) and all the

training data the system learns the inherent characteristics of what a dog looks

like. It can then be tested on another set of similar data which has also been

tagged but this time the tags haven’t been revealed to the system. If it has been

trained well enough, the system will be able to identify the dogs in the pic-

tures, and also correctly identify pictures where there is no dog. It can then be

let loose on real examples. And, if the people using your new ‘Is ere a Dog

in My Picture?’ app are able to feed back when it gets it right or not, then the

system will continue to learn as it is being used. Supervised learning is gener-

ally used where the input data is unstructured or semi-structured, such as

images, sounds and the written word (Image Recognition, Speech Recognition

and Search capabilities in my AI Framework).

With Unsupervised Learning, the system starts with just a very large data

set that will mean nothing to it. What the AI is able to do though is to spot

clusters of similar points within the data. e AI is blissfully naive about what

the data means; all it does is look for patterns in vast quantities of numbers.

e great thing about this approach is that the user can also be naive—they

don’t need to know what they are looking for or what the connections are—all

that work is done by the AI.Once the clusters have been identied then pre-

dictions can be made for new inputs.

So, as an example, we may want to be able to work out the value of a house

in a particular neighbourhood. e price of a house is dependent on many

variables such as location, number of rooms, number of bathrooms, age, size

of garden and so on, all of which make it dicult to predict its value. But,

surely there must be some complicated connection between all of these vari-

ables, if only we could work it out? And that’s exactly what the AI does. If it

is fed enough base data, with each of those variables as well as the actual price,

A Technology Overview

then it uses statistical analysis to nd all the connections—some variables may

be very strong inuencers on price whilst others may be completely irrelevant.

You can then input the same variables for a house where the price is unknown

and it will be able to predict that value. e data that is input is structured

data this time, but the model that is created is really a black box. is appar-

ent lack of transparency is one of AI’s Achilles’s heels, but one that can be

managed, and which I’ll discuss later in the book.

As well as the above two types of training, there are various other terms

associated with AI, and which I’ll cover briey here, although for business

executives they only need to be understood at a supercial level. ‘Neural

Networks’ is the term used to describe the general approach where AI is mim-

icking the way that the brain processes information—many ‘neurons’ (100

billion in the case of the brain) are connected to each other in various degrees

of strength, and the strength of the connection can vary as the brain/machine

learns.

To give an over-simplied example, in the dog picture app above, the ‘black

nose’ neuron will have a strong inuence on the ‘dog’ neuron, whereas a ‘horn’

neuron will not. All of these articial neurons are connected together in lay-

ers, where each layer extracts an increasing level of complexity. is gives rise

to the term Deep Neural Networks. Machine Learning, where the machine

creates the model itself rather than a human creating the code (as in the exam-

ples I have given above), uses DNNs. So, think of these terms as concentric

circles: AI is the over-arching technology, of which Machine Learning is a core

approach that is enabled by DNNs.

ere are obviously many more terms that are in common use in the AI

world, including decision tree learning, inductive logic programming, rein-

forcement learning and Bayesian networks, but I will cover these only when

absolutely necessary. e focus of this book, as you will now hopefully

appreciate, is on the business application of AI rather than its underlying

technologies.

About This Book

My working experience has been as a management consultant, helping organ-

isations cope with the challenges of the time, from productivity improvement,

through change management and transformation to outsourcing and robotic

process automation, and now AI.I rst came across AI properly in my work

in 2001. I was working as Chief Technology Ocer in the Corporate Venturing

unit of a global insurance provider—my role was to identify new technologies

1 Don’t Believe theHype

that we could invest in and bring into the rm as a foundation client (it was

what we used to call the ‘incubator’ model). One of the technologies we

invested in was based around the idea of ‘smart agents’ that could be used as

an optimisation engine—each agent would have a specic goal and would

‘negotiate’ with the other agents to determine the ideal collective outcome. So,

for example, the system could determine the most eective way for trucks to

pass through a port, or the best way to generate the most revenue from the size

and arrangement of advertisements in newspapers. Although we didn’t call it

AI at the time, this is eectively what it was—using computer algorithms to

nd optimal solutions to real problems.

Fast forward to 2017, and my work is focused almost exclusively on AI.I

work with enterprises to help create their AI strategy—identifying opportuni-

ties for AI, nding the right solution or vendor and creating the roadmap for

implementation. I don’t do this as a technologist, but as someone that under-

stands the capabilities of AI and how those capabilities might address business

challenges and opportunities. ere are plenty of people much cleverer than

me that can create the algorithms and design the actual solutions, but those

same people rarely understand the commerciality of business. I see myself as a

‘translator’ between the technologists and the business. And with AI, the chal-

lenge of translating the technology is orders of magnitude greater than with

traditional IT. Which is why I wanted to write this book—to bring that

understanding to where it can be used the best: on the front line of business.

So, this is not a book about the theoretical impact of AI and robots in 10

or 20years’ time and it is certainly not a book about how to develop AI algo-

rithms. is is a book for practitioners of AI, people who want to use AI to

make their businesses more competitive, more innovative and more future-

proof. at will happen only if the business leaders and executives understand

what the capabilities of the technology are, and how it can be applied in a

practical way. at is the mission of this book: be as informed as possible

about AI, but without getting dragged down by the technology, so that you

can make the best decisions for your business. And it is also a heartfelt appeal:

whatever you read or hear about AI, don’t believe the hype.

About This Book

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_2

Why Now?

A Brief History ofAI

For anyone approaching AI now, the technology would seem like a relatively

new development, coming o the back of the internet and ‘big data’. But the

history of AI goes back over 50 years, and includes periods of stagnation

(often referred to as ‘AI Winters’) as well as acceleration. It is worth providing

a short history of AI so that the developments of today, which really is AI’s big

momentin the sun, can be put into context.

I’ve already mentioned in the previous chapter the two people that are con-

sidered some of the key founders of AI, Marvin Minsky and Douglas

Engelbert, who both originally worked at the Massachusetts Institute of

Technology in Boston, USA.But the person who coined the phrase ‘articial

intelligence’ was John McCarthy, a professor at Stanford University in

California. McCarthy created the Stanford Articial Intelligence Lab (SAIL),

which became a key focus area for AI on the west coast of America. e tech-

nology driving AI at that time would seem rudimentary when compared to

the neural networks of today, and certainly wouldn’t pass as AI to anyone with

a basic understanding of the technology, but it did satisfy our earlier deni-

tion of “the theory and development of computer systems able to perform

tasks normally requiring human intelligence”, at least to a very basic level.

Much of the work in the early development of AI was based around

‘expert systems’. Not wanting to disparage these approaches at all (they are

still being used today, many under the guise of AI), they were really no

more than ‘if this then that’ workows. e programmer would lay out the

knowledge of the area that was being modelled in a series of branches and

loops, with each branch depending on an input from the user or a rule. So,

in a system designed to model a bank account recommendation process, the

user would be asked a series of questions (employment status, earnings, sav-

ings, etc.) with each answer sending the process down dierent branches

until it came to a conclusion. And, because that was essentially performing

a task ‘normally requiring human intelligence’ it was, at the time, consid-

ered AI.Today it wouldn’t really pass muster because it doesn’t have any

self-learning capability, which is a key facet of how AI is perceived to be

dened.

Interestingly, even now, this same approach is being used in many of the

chatbots that have proliferated across the internet. Most of these claim to use

AI, and some do, but most are passive decision trees. ere are a number of

online chatbot platforms (most are free to use) that allow you to create your

own bots using this approach and they do a reasonable job for simple pro-

cesses (I actually built my own a while back—it took about half a day and was

very basic—but it proved to me that it could be done by a non-technical

person and that there was very little, if any, AI actually involved).

ere were a couple of ‘AI Winters’, when advances in AI stagnated for a

good number of years. Both of these were the result of over-inated expecta-

tions and the withdrawal of funding. e rst occurred between 1974 and

1980, triggered by three events: a 1973 report by Sir James Lighthill for the UK

Government which criticised the ‘grandiose objectives’ of the AI community

and its failure to get anywhere near reaching those objectives; and in the United

States where the Manseld Amendment required the Advanced Research

Projects Agency (ARPA, now known as DARPA) to only fund projects with

clear missions and objectives, particularly relating to defence, which AI at the

time couldn’t satisfy; and the perceived failure of a key project for ARPA that

would have allowed ghter pilots to talk to their planes. ese events meant

that much of the funding was withdrawn and AI became unfashionable.

e second AI Winter lasted from 1987 to 1993 and was chiey due to the

failure of ‘expert systems’ to meet their over-inated expectations in 1985,

when billions of dollars were being spent by corporations on the technology.

As with my own chatbot experience that I described above, expert systems

ultimately proved dicult to build and run. is made them overly expensive

and they quickly fell out of favour in the early 1990s, precipitated by the

simultaneous collapse of the associated hardware market (called Lisp

machines). In Japan, a 1981 program costing $850m to develop a ‘fth gen-

eration computer’ that would be able to ‘carry on conversations, translate lan-

guages, interpret pictures, and reason like human beings’ failed to meet any of

its objectives by 1991 (some remain unmet in 2017). And, although DARPA

had started to fund AI projects in the United States in 1983 as a response to

2 Why Now?

the Japanese eorts, this was withdrawn again in 1987 when new leadership

at the Information Processing Technology Oce, which was directing the

eorts and funds of the AI, supercomputing and microelectronics projects,

concluded that AI was ‘not the next wave’. It dismissed expert systems as sim-

ply ‘clever programming’ (which, with hindsight, they were pretty close on).

I talk about these AI Winters in a little detail because there is the obvious

question of whether the current boom in AI is just another case of over-

inated expectations that will lead to a third spell of the technology being left

out in the cold. As we have seen in the previous chapter, the marketing

machines and industry analysts are in a complete froth about AI and what it

will be capable of. Expectations are extremely high and businesses are in dan-

ger of being catastrophically let down if they start to believe everything that is

being said and written. So, we need to understand what is driving this current

wave and why things might be dierent this time.

From a technology point of view, the only two words you really need to

know for now are ‘machine learning’. is is the twenty-rst century’s version

of expert systems—the core approach that is driving all the developments and

applications (and funding). But, before I describe what machine learning is

(in non-technical language, of course), we need to understand what all the

other forces are that are contributing to this perfect storm, and why this time

things could be dierent for AI.ere are, in my mind, four key drivers.

The Role ofBig Data

e rst driver for the explosion of interest and activity in AI is the sheer vol-

ume of data that is now available. e numbers vary, but it is generally agreed

that the amount of data generated across the globe is doubling in size every

two years—that means that by 2020 there will be 44 zettabytes (or 44 trillion

gigabytes) of data created or copied every year. is is important to us because

the majority of AI feeds o data—without data this AI would be worthless,

just like a power station without the fuel to run it.

To train an AI system (like a neural network) with any degree of accuracy

you would generally need millions of examples—the more complex the model,

the more examples are needed. is is why the big internet and social media

companies like Google and Facebook are so active in the AI space—they simply

have lots of data to work with. Every search that you make with Google—there

are around 3.5 billion searches made per day—and every time you post or like

something on Facebook—every day 421 billion statuses are updated, 350 mil-

lion photos are uploaded and nearly 6 trillion Likes are made—more AI fuel is

generated. Facebook alone generates 4 million gigabytes of data every 24hours.

The Role ofBig Data

is vast volume of data is then consumed by the AI to create value. To

pick up again on the simple example I used in the previous chapter, to train a

DNN (essentially a machine learning AI) to recognise pictures of dogs you

would need to have many sample images of dogs, all labelled as ‘Dog’, as well

as lots of other images that didn’t contain dogs, all labelled ‘No Dog’. Once

the system had been trained to recognise dogs using this set of data (it could

also go through a validation stage where the algorithm is tuned using a sub-set

of the training data), then it will need to be tested on a ‘clean’, that is, unla-

belled, set of images. ere are no strict guidelines for how much testing data

is required but, as a rule of thumb, this could represent around 30% of the

total data set.

ese huge amounts of data that we create are being exploited every minute

of the day, most of the time without our knowledge (but implicit acceptance).

Take, for example, your Google searches. Occasionally, as you type in your

search term you may spell a word incorrectly. Google usually oers you results

based on a corrected or more common spelling of that word (so for example

if I search for ‘Andrew Durgess’ it shows me results for Andrew Burgess), or

you can choose to actually search for the uncommonly spelled version. What

this means is that Google is constantly collecting data on commonly misspelt

versions of words and, most importantly, which corrections that it suggests are

acceptable or not. All the data is then used to continually tune their AI-powered

spell checker. If, in my example, there was actually someone called Andrew

Durgess who suddenly became famous tomorrow so that many people were

searching for his name, then the correction that Google used on my name

would be quickly phased out as less and less people accepted it and instead

clicked on ‘Search instead for Andrew Durgess’.

But it’s not just social media and search engines that have seen exponential

increases in data. As more and more of our commercial activities are done

online, or processed through enterprise systems, more data on those activities

will be created. In the retail sector our purchases don’t have to be made online

to create data. Where every purchase, whilst not necessarily connecting it to

an identied buyer, is recorded, the retailers can use all that data to predict

trends and patterns that will help them optimise their supply chain. And

when those purchases can be connected to an individual customer, through a

loyalty card or an online account for example, then the data just gets richer

and more valuable. Now the retailer is able to predict what products or ser-

vices you might also want to buy from them, and proactively market those to

you. If you are shopping online, it is not only the purchase data that is

recorded—every page that you visit, how long you spend on each one and

what products you view are all tracked, adding to the volume, and the value,

of data that can be exploited by AI.

2 Why Now?

After the purchase has been made, businesses will continue to create, harvest

and extract value from your data. Every time you interact with them through

their website or contact centre, or provide feedback through a third- party rec-

ommender site or social media, more data is created that is useful to them.

Even just using their product or service, if it is connected online, will create

data. As an example, telecoms companies will use data from your usage and

interactions to try and predict, using AI, whether you will leave them soon for

a competitor. eir ‘training data’ comes from the customers that have actually

cancelled their contracts—the AI has used it to identify all of the dierent

characteristics that make up a ‘churn customer’ which it can then apply to the

usage and behaviours of all of the other customers. In a similar way, banks can

identify fraudulent transactions in your credit card account simply because

they have so much data available of genuine and not-so- genuine transactions

(there are roughly 300 million credit card transactions made every day).

Other sources of ‘big data’ are all the text-based documents that are being

created (newspapers, books, technical papers, blog posts, emails, etc.), genome

data, biomedical data (X-rays, PET, MRI, ultrasound, etc.) and climate data

(temperature, salinity, pressure, wind, oxygen content, etc.).

And where data doesn’t exist, it is being created deliberately. For the most

common, or hottest, areas of AI complete training sets of data have been

developed. For example, in order to be able to recognise handwritten num-

bers, a database of 60,000 samples of handwritten digits, as well as 10,000 test

samples, has been created by the National Institute of Standards (the database

is called MNIST). ere are similar databases for face recognition, aerial

images, news articles, speech recognition, motion-tracking, biological data

and so on. ese lists actually become a bellwether for where the most valu-

able applications for machine learning are right now.

e other interesting aspect about the explosion and exploitation of data is

that it is turning business models on their heads. Although Google and

Facebook didn’t originally set out to be data and AI companies, that is what

they have ended up as. But what that means now is that companies are being

created to harvest data whilst using a (usually free) dierent service as the

means to do it. An example of this, and one which uses data for a very good

cause, is Sea Hero Quest. At rst sight this looks like a mobile phone game,

but what it actually does is use the data from how people play the game to

better understand dementia, and specically how spatial navigation varies

between ages, genders and countries. At the time of writing 2.7 million peo-

ple have played the game which means it has become the largest dementia

study in history. Commercial businesses will use the same approach of a ‘win-

dow’ product or service that only really exists to gather valuable data that can

be exploited elsewhere.

The Role ofBig Data

The Role ofCheap Storage

All the data that is being created needs to be stored somewhere. Which brings

me on to the second driver in favour of AI: the rapidly diminishing cost of

storage, coupled with the speed that data can be accessed and the size of the

machines that store it all.

In 1980 one gigabyte of storage would cost on average $437,500. Five years

later that had dropped to around a quarter, and by 1990 it was a fortieth of

the 1980 price, at $11,200. But that was nothing compared to the subsequent

reductions. At the turn of the century it was $11.00, in 2005 it was $1.24 and

in 2010 it was 9 cents. In 2016, the cost stood at just under 2 cents per giga-

byte ($0.019).

Because of all that data that is generated through Facebook that I described

above, their data warehouse has 300 petabytes (300 million gigabytes) of data

(the amount of data actually stored is compressed down from that which is

originally generated). Accurate numbers are tricky to come by, but Amazon’s

Web Services (its commercial cloud oering) probably has more storage

capacity than Facebook. It is this sort of scale that results in a sub–2 cent

gigabyte price.

It’s not just costs that have shrunk; size has too. ere is a photograph from

1956 that I use in some of the talks I give of an IBM hard drive being loaded

onto a plane with a fork-lift truck. e hard drive is the size of a large shed,

and only has a capacity of 5Mb. Today that would just about be enough to

store one MP3 song. Amazon now has a eet of trucks that are essentially

huge mobile hard drives, each capable of storing 100 petabytes (the whole of

the internet is around 18.5 petabytes). At the time of writing IBM have just

announced that they have been able to store information on a single atom. If

this approach can be industrialised it would mean that the entire iTunes

library of 35 million songs could be stored on a device the size of a credit card.

The Role ofFaster Processors

All this cheap storage for our massive data sets is great news for AI, which, as

I have hopefully made very clear, lives o large amounts of data. But we also

have to be able to process that data too. So, the third driver in AI’s perfect

storm is fasterprocessor speeds.

is is where the often-cited Moore’s Law comes in. e founder of Intel,

Gordon Moore, predicted in 1965 that the number of transistors that could

2 Why Now?

t onto a transistor would double every year. In 1975, he revised this to a

doubling every two years. It was actually David House, an Intel executive,

who came up with the most commonly used version which is that chip per-

formance (as an outcome of more and faster transistors) would double every

18months. Although there have been deviations from this trend, particularly

in the past few years, it means that the processors being used today are orders

of magnitude faster than during the last AI Winter.

One of the curious things to come out of technology for AI is that tradi-

tional computer chips (central processing units, or CPUs) are not ideally

suited to the crunching of large data sets, whereas GPUs (graphical processing

units), which had been developed to run demanding computer visualisations

(such as for computer games), were perfect for the job. erefore NVidia, a

maker of GPUs, has taken most of the market for computer chips in the

world of AI.

So, faster AI-friendly processors mean that more complex problems, that

use more data, can be solved. is is important because managing and pro-

cessing all that data does take time—the systems are ne at the second part of

the process, that of scoring and making a decision based on the learnt data,

but the training part can be a slog. Even relatively simple training sessions

need to run overnight, and more complex ones can take days. So any improve-

ment in processor speed will help enormously in the usefulness of AI systems

both in the original development and design of the models but also on a day-

to- day basis so that the systems can use the most up-to-date models. Being

able to provide real-time training as well as real-time decision making is one

of the last frontiers of really useful AI.

The Role ofUbiquitous Connectivity

e nal driver working in AI’s favour is connectivity. Clearly the internet has

had a huge enabling eect on the exploitation of data, but it is only in the past

few years that the networks (both broadband and 4G) have become fast

enough to allow large amounts of data to be distributed between servers and

devices. For AI this means that the bulk of the intensive real-time processing

of data can be carried out on servers in data centres, with the user devices

merely acting as a front end. Both Apple’s Siri (on the iPhone) and Amazon’s

Alexa (on the Echo) are prime examples of very sophisticated AI applications

that exploit processing power in data centres to carry out the bulk of the hard

work. is means there is less reliance on the device’s processor but it does put

a burden on having the network available and eective enough.

The Role ofUbiquitous Connectivity

It’s not only the real-time processing where the internet can provide bene-

ts. e sheer eort in training an AI model, with each training run taking

days or weeks on ‘standard’ hardware, can be signicantly sped up using a

cloud-based solution that has specialised hardware available.

Better communication networks can also help AI systems in other ways.

e data sets that I mentioned in the previous section are huge but are made

available, usually publicly, to many users to help train their systems—that

wouldn’t have been nearly as easy previously.

Also, AI systems can be connected to each other using the internet so that

they can share learnings. A program run by a consortium of Stanford

University, University of California at Berkeley, Brown University and Cornell

University, called Robobrain, uses publicly available data (text, sounds,

images, lm) to train AI systems which can be accessed by other AI systems.

And, of course, like any good AI system, the ‘recipient’ systems will feed back

everything that they learn back into Robobrain. e challenge for Robobrain,

which mirrors the overall challenge of AI, is that systems tend to be very nar-

row in their focus whereas Robobrain wants to be all things to all robots (or

‘multi-modal’ to use the lingo).

About Cloud AI

Nowhere has all four of these drivers come together more eectively than in

the concept of Cloud AI.e idea of AI-as-a-service, where the heavy-lifting

of the AI is done on the cloud and on-demand, is now a key catalyst for the

democratisation of AI.Many of the large technology rms, such as Google,

IBM and Amazon, all have cloud solutions for AI that oer easily accessible

APIs (Application Programming Interfaces, which are essentially standardised

access points to programming capabilities) for developers to create AI ‘front-

ends’ out of them. IBM’s Watson—its heavily branded and lauded AI capabil-

ity—is ‘just’ a series of APIs, with each one carrying out a specic function,

such as speech recognition or Q&A.Google’s TensorFlow is an open-source

AI platform that oers similar capabilities, as well as additional features such

as pre-trained models.

What this means for business, and specically any entrepreneur wanting to

start an AI business, is that the value of the AI is not going to be where every-

one assumes it be: in the algorithm. If every new customer-service focused AI

business used the open-source speech recognition algorithm from, say,

Amazon, then the competitive advantage would have to lie with the quality of

the training data, the way that the algorithm is trained or how easily the

2 Why Now?

resulting system is to use. When you connect to an IBM Watson API, for

example, there is still a lot of work to do training it before you can derive any

real value.

Clearly some AI businesses will create competitive advantage from their

algorithm, but they must compete with the other rms who use an o-the-

shelf one. It’s fairly straightforward, as I have done myself, to download a free,

open-source Named Entity Recognition algorithm (which is used to extract

names such as people, places, dates, etc., from a body of text) from a univer-

sity website (in my case it was Stanford’s), feed it some sample text and get it

to return a reasonable answer. But that is not yet a viable AI solution, let alone

a business. To make it commercial I need to train and tune the algorithm

using as much data as I can get my hands on and create a user-friendly inter-

face for it, and that is where the real skills—in data science, in optimisation,

in user experience—come into play. Put all that together and you may have

the seed of an AI business.

For the executive looking to use AI in their business (and the fact that you

are reading this book would suggest that is the case) the provenance of the

value in an AI company is important to understand. Why should you choose

one vendor over another, if they say they can do exactly the same thing?

Getting to the heart of the dierences—is it down to the algorithm, the data,

the ease of implementation, the ease of training, the ease of use and so on?—

will allow you to make the right choice for your business. At the moment,

there are far too many smoke-and-mirrors AI businesses that are based purely

on open-source algorithms and built by very clever but inexperienced

20-somethings. ey will be riding the hype of AI but will oer little in the

way of long-term value. My point being that you can create a successful AI

business based on great open-source algorithms, but you need all the other

aspects to be great too. As I said right at the start of this book: don’t believe

the hype.

I’ll be covering these selection considerations, as well as the buy-versus-

build question, in more detail later in the book.

What Is Machine Learning?

ese four developments then—big data, cheap storage, faster processors and

ubiquitous networks—are what is driving the acceleration and adoption of AI

in business today. If you were to take any one of those away, AI would struggle

to perform, and we’d probably still be in an AI Winter. Each one has also been

helping drive development in the others—if we didn’t have such cheap storage

What Is Machine Learning?

then we couldn’t keep all that data which means there would be no need for

faster processors, for example. But, at the centre of all this activity, feeding o

each of the drivers, is machine learning, one of the most popular AI approaches.

It’s important to have an appreciation of what machine learning is and

what it does, even if you don’t need to understand how it actually works. As

the name suggests, it is the machine that does all of the hard computational

work in learning how to solve a problem—the machine, rather than a human

being, essentially writes the ‘code’. e human developer plants the seed by

dening the algorithm or algorithms that are to be used, and then the machine

uses the data to create a solution specic to it. We have already seen that the

problem could be undened initially (as in unsupervised learning, where pat-

terns or clusters are found in data) or dened (and therefore trained with large

data sets to answer a particular question, which we know as supervised learn-

ing). For machine learning it is usually easier to think about a supervised

learning example, such as my go-to case ofbeing able to tell the dierence

between pictures of dogs and pictures of cats.

In the previous chapter, I introduced the idea of DNNs, which is the ‘archi-

tecture’ that machine learning uses. A DNN will consist of a number of dif-

ferent layers; the more complex the problem being solved, the more layers are

needed. (More layers also mean that the model is much more complicated,

will require more computing power and will take much longer to resolve

itself.) e Input Layer takes the data in and starts to encode it. e Output

Layer is where the answer is presented—it will have as many nodes as there are

classes (types) of answer. So, in my dog versus cat example, there would be

two output nodes, one for dog and one for cat (although we could dene

three if we had pictures that had neither a dog nor a cat in) (Fig.2.1).

In between the Input Layer and the Output Layer are the Hidden Layers.

It is here that all the hard work happens. Each Hidden Layer will be looking

for dierent features within the data with increasing complexity, so with

image recognition dierent layers will look for outlines, shadows, shapes,

colours and so on. In these Hidden Layers, there will be more nodes (‘neurons’)

than the Input or Output Layers, and these nodes will be connected to each

other across each layer. Each of these connections will have a certain ‘weight’

or strength which determines how much of the information in one node is

taken into the next layer: a strong link, which had, through training, led to a

‘right’ answer at the Output Layer, will mean that information is propagated

down through to the next layer. A weak link with a low weighting, which had

led to a ‘wrong’ answer in training, will not pass as much information down

(Fig.2.2).

2 Why Now?

As the model is being trained with more and more data, the weights are

continually adjusted (this is the machine learning) until it reaches an optimal

solution. e more data we have fed into the model, the more chances the

machine has to rene the weightings (but the harder it has to work), and

therefore the more accurate the solution will be. e ‘matching function’ (the

Input

Layer

Hidden

Layer

Hidden

Layer

Output

Layer

Fig. 2.1 Basic neural network

Input

Layer

Hidden

Layer 1

Hidden

Layer 2

Output

Layer

Fig. 2.2 Training a neural network

What Is Machine Learning?

nal version of the model) can then be used to solve for a new piece of data;

for example, I give it a picture of a dog that it hasn’t seen before and it should

then be able to correctly identify it as a dog (Fig.2.3).

From this brief description of machine learning you should be able to see

that it is wholly dependent on the four drivers I have described—we need lots

and lots of data to train the hidden layers to come up with the right weight-

ings, but that means we need to be able to store our data cheaply and process

our models as quickly as possible, as well as needing access to data sets from

as many sources as possible. Without any one of these, machine learning just

wouldn’t be viable, either because it wouldn’t be accurate enough or it wouldn’t

be easy enough to design and implement (and it’s already dicult enough in

the rst place).

It’s not just machine learning that benets from this ‘perfect storm’ of tech-

nology. Other types of AI, the main one being ‘symbolic AI’, have also accel-

erated and found new life through faster, cheaper and connected computers.

The Barriers toAI

So, with all this data, cheap storage, whizzy processors and inter-connectivity,

we are certain to never see another AI Winter, right? Well, there are a few

things that could spoil the AI party.

DOG

Input

Layer

Hidden

Layer

Hidden

Layer

Output

Layer

Fig. 2.3 A trained neural network

2 Why Now?

e biggest barrier to AI achieving escape velocity in my opinion is the

over-ination of expectations. Far too much is being written and said about

what AI might be capable of, especially when Articial General Intelligence is

considered and not the Narrow AI that we have today. Both the previous AI

Winters have been precipitated by excessive and unrealistic expectations, and

therefore they need to be managed carefully. A key motivation for me to write

this book was to ‘bring AI down to earth’ so that it could be assessed for its

real value today and tomorrow, and not in 10 or 20years’ time.

Another factor that will aect the uptake of AI, which has been fuelled by

the hype, is a general fear of what changes AI might bring, especially when it

could mean fundamentally changing the way that people work. ere have

been many other seismic shifts in ways of working, including the introduction

of personal computers and outsourcing, but AI has the potential to do this on

a much greater scale: speculative reports by academics and institutions repeat-

edly report the decimation of ‘white collar’ jobs and the ‘hollowing out’ of the

middle classes. All of this may or may to be true, but the fear that it generates

leads to a natural defensiveness from those in businesses that may be aected

by the implementation of AI.

A third aspect, which is connected to the rst two, is ignorance. How can

people expect to get real value from implementing AI if they don’t have a good

enough understanding of what it is? e hype doesn’t help, but AI is inher-

ently a complex subject, and it’s not an easy task for a non-technical business

person to understand it. e maxim that a little knowledge is a dangerous

thing is certainly true for AI, but also having all the knowledge is not useful

unless you want to be a data scientist or AI developer. ere is a ‘Goldilocks’

level of understanding AI that this book aims to provide—it should be just

enough that you can make the most out of AI but not too much that you

become overwhelmed with technical detail and jargon.

e nal thing that could put the brakes on AI is regulation. As I’ve men-

tioned already, much of the computational work that is done by an AI system

is hidden from the developer or user—there is (generally) no audit trail that

details how it came to a certain decision. If I fed a complexcredit decisioning

AI system with lots of loan applications as training data and told it which had

been approved and which hadn’t, it could then make a recommendation of

whether to approve or reject a new loan. But no one would really know why,

and that causes a problem for regulators who need to see that decision process

written down. ere are some approaches which can mitigate this but the

opaqueness of AI can be a real challenge to its usefulness.

I’ll cover these aspects in more detail in Chap. 8, but I think it’s important

that you have an early appreciation of them at this point.

The Barriers toAI

Some AI Case Studies

Although it may be fuelled by perhaps too much hype, there is certainly

momentum in the AI market right now. But who is beneting from all these

advancements? Is it just research labs and some start-ups that have had success

playing computer games, or is there something more? Is AI providing value to

businesses today?

In my work, I see plenty of examples of AI being used in businesses to pro-

vide insights and eciencies, adding value that would be beyond the means

of humans to create.

One of the sweet spots for AI at the moment is in customer services. At a

train operating company in the United Kingdom they use AI to categorise

emails that come in from their customers. e challenge, of course, is for the

AI to be able to ‘read’ the email, which has been written freeform, and deter-

mine what the customer actually wants. For example, if I couldn’t get a seat

on the 08:06 from Euston Station in London to Glasgow Central, for exam-

ple, I would email the train company, saying something like “I can’t believe

it– I paid all that money for a ticket on the 08:06 from London to Glasgow

and I couldn’t even get a seat. You’ve really excelled yourselves this time!” All

the salient information is extracted from my unstructured text and validated

(Was there a train at 08:06? Have other people complained about the same

service? Is the customer a regular complainer? etc.) so that the AI can imme-

diately route my query to the relevant person in the customer service organ-

isation. Of course, my wording in the second sentence is sarcastic in tone, so

the AI needs to understand the dierence between my complaint and an

actual compliment. What it all means is that when the human agent logs in,

they are the right person to deal with my query and they have everything at

hand to respond.

In a similar vein, a UK-based insurance rm that provides a claims process-

ing service to their own insurance customers has automated the input of

unstructured and semi-structured data (incoming claims, correspondence,

complaints, underwriters’ reports, cheques and all other documents relating to

insurance claims) so that it is directed into the right systems and queues. Using

an AI solution, a team of four people process around 3,000 claims documents

per day, 25% of which are paper. e AI automation tool processes the scanned

and electronic documents automatically, identies claim information and

other metadata, and deposits the results in databases and document stores

ready for processing by the claims handlers and systems. It also adds service

metadata so performance of the process can be measured end to end. Some

2 Why Now?

documents can be processed without any human intervention, and others

need a glance from the human team to validate the AI’s decisions or ll in

missing details.

A good example of AI in the legal sector is ROSS, a system built around

IBM’s Watson. ROSS actually uses a number of AI capabilities including

NLU, Search and Optimisation. So, when a lawyer has a matter that requires

some specic research (e.g. what is the precedent when an employee is red

for gross misconduct within a few days of the end of their probationary

period?), she can turn to ROSS and type in that query in plain English. ROSS

will then interrogate the whole corpus of employment law and provide

answers ranked by relevancy back to the lawyer within seconds. e alterna-

tive would be hours of research by the lawyer, a junior lawyer and/or a parale-

gal, and likely without being able to examine every relevant document

available. Like all good AI systems, ROSS is self-learning—the lawyer can

evaluate the quality of the responses that ROSS returns and therefore allow it

to provide better answers in the future.

Some companies have used AI to create whole new lines of business. Charles

Schwab, the US bank and brokerage rm, has created an investment vehicle,

called Schwab Intelligent Portfolios, which uses AI to manage its clients’ port-

folios (these are sometimes called ‘robo-advisors’). It focuses on low-cost

exchange-traded funds and has no advisory fees, account service fees or com-

mission fees. Since it launched in 2015, a few other rms have appeared with

a similar model, including Betterment, Wealthfront and FutureAdvisor

(although these charge a very small admin fee). e attractiveness of the low

or zero fees and the simplicity of dealing with an AI rather than a person have

meant that these sorts of services have proved popular amongst beginner

investors, thus providing new clients for the banks that they normally wouldn’t

have been able to access.

Conclusion

So, AI is being used successfully by businesses around the world. e perfect

storm of big data, cheap storage, faster processors and ubiquitous connectivity

has allowed researchers to exploit the near-magic that is machine learning.

But AI has managed to escape the lab and is being commercialised by both

start-ups and internet giants. Real opportunities exist for executives to use AI’s

capabilities to deliver new sources of value into their business as well as to

challenge and disrupt their existing business models. In order to do that they

need to understand what those capabilities are and how they can be used.

Conclusion

The Academic’s View

is is an extract from an interview with Dr Will Venters, Assistant Professor

in Information Systems at the London School of Economics.

AB What’s the basis of all the fuss around AI right now?

WV ere are a few trajectories that are particularly important. e underly-

ing algorithms have become much smarter and are used more eectively,

but that’s not actually that signicant. What has really made the dier-

ence is the processing capacity of the computer chips as well as the ability

to handle much bigger volumes of data at scale.

Previously we saw IBM’s Big Blue beat Kasparov at chess, then Watson

won at Jeopardy!, but essentially these are just systems that can search

massive databases very quickly. But what AlphaGo is doing (beating the

world’s number one player in the Chinese game of Go) is amazing—it

watched, then played millions of games of Go against itself. e complex-

ity and the sheer quantity of data that it was able to analyse is phenomenal—

I’m sure the future of AI will be around this sort of capability.

It’s interesting if you look at data complexity versus task complexity

for processes that computers want to take on: we are now able to manage

situations that are complex in both of these aspects. Managing elevators

is a complex task, but the data is relatively simple. Self-driving cars are

not doing complex things (just driving and turning and stopping) but the

data they need to do that is vast. But, we are now able to manage situa-

tions that are complex in both data and task aspects. Suddenly we have

the mechanisms to do it.

AB You also mentioned processing power as being a key factor?

WV Yes, GPUs [Graphical Processing Units] have made a signicant contri-

bution to this. But also the widespread use of Cloud Computing, which

allows businesses to manage big data without the associated infrastruc-

ture investments and risks.

Lots of companies have invested in data infrastructure, either their

own or through the cloud, and AI allows them to utilise this to the full.

AB Do you think the hype helps or hinders AI?

WV AI is certainly hyped, but it is also productive. It leads to new innovations

and forces businesses to discuss big questions. Hype shouldn’t only be

seen in the negative.

AB What do you see as the most valuable use cases for AI?

WV AI can oer the opportunity to deal with ‘messy’ data– data that is fuzzier,

such as photographs, natural language speech and unstructured documents.

2 Why Now?

Big Data promised to deliver all of this but ultimately it didn’t because it

required armies of statisticians. Robotic Process Automation is great for

dealing with processes that are clean and structured– as soon as it becomes

messier then AI can step in and overcome that.

AB What do you think businesses need to do if they are to exploit AI to the full?

WV ey rst need to look at the business case, and not the technology. But

they also need to consider the governance and to understand how they

manage the inherent risks. Humans have an ethical compass which, of

course, AI doesn’t. It will need some level of oversight because of the lack

of transparency with the AI models. ey must also make sure the system

is not biased– it’s very easy to bake the bias into the company’s business.

And these risks scale very quickly because the underlying data is so much

bigger.

AB What do you see for the future for AI?

WV I think we will see even better algorithms to deal with the bigger volumes

of data. But, of course, AI is a victim of its own success– there is an

inherent disillusionment as each exciting step forward then becomes

common place. You just need to look at advances like [Apple’s] Siri and

[Amazon’s] Alexa. e term AI will eventually become redundant and

people will cease to be questioning about it.

But until that day companies will invest in AI wisely as well as inap-

propriately. ere is so much value to be gained that companies should

think deeply about their approach, take expert advice and make sure they

are not left behind.

The Academic’s View

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_3

AI Capabilities Framework

Introduction

It’s impossible to get value out of something if it is not understood, unless it’s

by some happy accident. In the world of AI there are no happy accidents—

everything is designed with meticulous detail and with specic goals in mind.

erefore, the only way to truly make the most of AI in business is by having

a reasonable understanding of it. e challenge, of course, is that is endishly

complicated and full of complex mathematics, and is certainly not within the

bounds of what an ‘ordinary’ business person would be expected to grasp.

My approach in this book is to understand AI from the perspective of what

it can do, that is, what are its capabilities in the context of real world problems

and opportunities? In order to do this, I have developed a framework that

‘boils down’ all of the complexity into eight capabilities. In theory, any AI

application should t into one of these, which allows the AI layman to quickly

assess and apply that application into something that relates to their business.

Conversely, if you have a specic business challenge, the AI framework can be

used to identify the most appropriate AI capability (or capabilities) that could

address that need. Having said that,I’m sure any AI scientist or researcher

would be able to picksome holes in the framework. It’s certainly not bomb-

proof: it is intended to be a useful tool for business executives to help them get

the most value possible out of AI.

ere are many ways to look at AI, some of which I have already discussed in

earlier chapters: Supervised versus Unsupervised Learning; Machine Learning

versus Symbolic Learning; Structured Data versus Unstructured Data;

Augmentation versus Replacement. All of these are valid perspectives, and all can

be understood to a reasonable enough degree within the capability framework.

So, with each of the capabilities I will explain whether it will require

supervised or unsupervised learning (or both), the type of AI that is usually

used to deliver the capability (e.g. machine learning), whether it processes

structured or unstructured data and, most importantly, how it benets busi-

nesses. In subsequent chapters I will draw out use cases for each, grouped into

the key themes of enhancing customer service, business process optimisation

and enhanced decision making.

Another perspective for AI, which I introduced in Chap. 1, is really the

meta-view, and probably the most important to understand rst: these are the

three basic things that AI tries to achieve: capturing information; determining

what is happening and understanding why it is happening. Each of my eight

capabilities ts into one of these ‘AI Objectives’ (Fig.3.1):

Capturing information is something that our brain does very well but

machines have historically struggled with. For example, the ability to recognise

a face is something that has evolved in humans since our earliest existence—it

is a skill that allows us to avoid danger and create relationships, and therefore

a great deal of brain power and capacity is used to work on this problem. For

machines, the process is very complex, requiring huge volumes of training

data and fast computer processors, but today we have this capability on our

desktop computers and even mobile phones. It may not be very fast or that

accurate yet but AI has been fundamental in achieving this.

Most of the examples of capturing information are where unstructured

data (such as a picture of a face) is turned into structured data (the person’s

name). Capturing information is also relevant to structured data, and AI

comes into its own when that data is big. Again, the human brain is very good

at identifying patterns in data (the impact of a manager on the success of a

football team, for example) but when there are hundreds of variables and mil-

lions of data points we lose sight of the forest and can only see the trees. AI is

able to nd patterns, or clusters, of data that would be invisible to a human.

ese clusters have the ability to provide insights within the data that have

real value to a business. For example, AI can nd patterns between purchases

and customer demographics that a human would either take years to unearth,

or never have thought of in the rst place. (is associated idea of the AI

being ‘naive’ to the data is something that I will return to later, but is an

important concept to bear in mind.)

CAPTURE INFORMATIONWHAT IS HAPPENING? WHY IS IT HAPPENING?

Fig. 3.1 AI objectives

3 AI Capabilities Framework

e next AI Purpose is where it tries to determine what is happening, and

is usually a consequence of where information has already been captured by

AI.For example, we could have used Speech Recognition to extract (from a

sound le or a live conversation) the words that someone was speaking, but at

that point we would only know what all the individual words were rather than

what the person was actually trying to say. is is where Natural Language

Understanding (NLU) comes in—it takes the words and tries to determine

the meaning or intent of the complete sentences. So, we have gone from a

digital stream of sound to a set of words (e.g. ‘I’, ‘want’, ‘to’, ‘cancel’, ‘my’,

‘direct’, ‘debit’, ‘for’, ‘mortgage’, ‘protection’) to working out that this person

‘wants to cancel her Direct Debit for her mortgage protection insurance’.

We can then use other capabilities in this category to take that request fur-

ther. For example, we could use an Optimisation approach to help the cus-

tomer understand that if they were to cancel that Direct Debit they would

also probably need to cancel the insurance policy that it is associated with.

And then we could use a Prediction capability to work out that this customer

may be about to leave the bank and go to a competitor (because the AI has

determined from lots of other similar interactions that cancelling Direct

Debits is an indicator of potential customer churn).

So, from a simple interaction with a customer we are able to use dierent

AI ‘capturing information’ and ‘what is happening’ capabilities to build up a

useful picture as well as satisfying the customer’s request. But, in the example

above, although the AI has been able to identify this customer as a ‘churn risk’

it really doesn’t understand what that means. All it has done is correlate one

set of data (customer requests) with another (customers who leave) and then

apply that to a new data point (our customer’s Direct Debit cancellation

request). To the AI system the data might as well be about ice cream avours

and the weather. It has no idea of the concept of Direct Debits just as much as

it has no idea of the concepts of banks or customers. e AI capabilities that

we have today and will have in the near future (and maybe even ever) do not

have the ability to understand. It is really important to be able to dierentiate

between the very narrow things that dierent AIs can do (usually better than

humans) and the general intelligence that comes with understanding and

relating dierent concepts—something our brain is brilliant at doing.

Now that we have the three AI purposes dened, and we understand that

only the rst two are relevant to us today, we can start to look in more detail

at each of the eight AI capabilities in the Framework.

Introduction

Image Recognition

One of the most active areas of research in AI at the moment is that of Image

Recognition. is is a prime example of where the four key drivers that I

described in Chap. 2 have aligned to catalyse the technology. Image

Recognition is based on Machine Learning and requires thousands or mil-

lions of sample images for training; therefore, it also needs lots of storage for

all the data, and fast computers to process it all. Connectivity is also impor-

tant to enable access to the data sets, many of which are publicly available (in

Chap. 2 I mentioned image data sets for handwritten numbers and faces, but

there are many more including aerial images, cityscapes and animals).

Images, of course, fall under the category of unstructured data. So, what

sort of applications would you use Image Recognition for? ere are three

main types:

Probably the most popular application is identifying what is in the image,

sometimes referred to as tagging. I’ve used this example a few times already—

nd out if the picture contains a dog or a cat. It is often used to moderate

photographs by trying to identify pornographic or abusive images, but can

also be used to group photos with similar tags (‘these are all pictures taken at

the beach’). is photo tagging is a prime example of supervised learning—

the AI is trained on thousands, or millions, of tagged photographs—which is

why companies that have access to large volumes of images, such as Google

and Facebook, have some of the most advanced systems in this area.

Another use of Image Recognition is to nd images that are similar to other

images. Google’s Reverse Image Search is a popular use of this approach; you

simply upload a picture and it will search for pictures that look similar to your

original (this technique is often used to identify the use of news photos that

have deliberately been used out of context). Unlike photo tagging, this is

mainlyan example of unsupervised learning; the AI doesn’t need to know

what is in the picture, just that it looks like another picture. (A simple way to

think about this is to understand that the AI converts the image le into a

long series of numbers—it then looks for other images that have a similar

series of numbers.)

e nal application for Image Recognition is to nd dierences between

images. e most common, and benecial, use of this is in medical imaging.

AI systems are used to look at scans of parts of the body and identify any

anomalies, such as cancerous cells. IBM’s Watson has been a pioneer in this

space and is used to support radiologists in their work. e approach uses

supervised learning to tag, for example, X-rays as healthy or unhealthy. Based

3 AI Capabilities Framework

on the algorithmic model that is built up, new images can be evaluated by the

AI to determine whether there is a risk to the patient. Interestingly, it has been

reported that Watson is more accurate at spotting melanomas than when it is

done manually (Watson has 95% accuracy compared to an average of between

75% and 84% for humans).

Image Recognition, probably out of all the capabilities I describe in this

chapter, is the most data hungry. Images are inherently unstructured and very

variable and therefore require very large amounts of data to train them eec-

tively. Pinterest, the website that allows users to create ‘boards’ of their favou-

rite images, has used all of the data from their hundreds of millions of users to

help develop their systems further (it does a good job of nding similar images

to ones that you have posted, even if they are not tagged and are pretty

abstract) but also to develop new AI applications such as Pinterest Lens which

enables you to point your phone camera at an object and it will return pic-

tures of objects that are visually similar, have related ideas or are the same

object in other environments.

Other applications of Image Recognition are not so altruistic. A website in

Russia, where privacy laws are more lax than most Western countries, enables

its users to identify people in the street using their phone camera. Find Face,

as the website is called, exploits the fact that all 410 million prole pictures on

the country’s most popular social media site, VK, are made public by default.

erefore, it is able to match, to an apparent accuracy of 70%, a face that you

point your camera at with a VK prole. e intended, and rather bawdy, uses

of this application are clear from the pictures of women on the website’s

homepage, but it does demonstrate how well image recognition technology

can work if it has a large enough training set to work from.

Image recognition is still at a relatively immature stage—many useful

things are possible now, but the potential is much greater. e use of still and

moving images in our daily lives and in the business world is increasing expo-

nentially and therefore the ability to index and extract meaningful data from

these will be an increasingly important application.

Speech Recognition

Speech recognition, sometimes referred to as speech-to-text, is usually the rst

stage in a string of AI capabilities where the user is providing instructions by

voice. It takes the sounds, whether live or recorded, and encodes them into

text words and sentences. At this point other AI capabilities such as NLU

would be needed to determine the meaning of the encoded sentences.

Speech Recognition

Speech recognition has beneted enormously from the development of

DNNs although some of the more ‘traditional’ AI approaches (usually using

something called a Hidden Markov Model or HMM) are still widely used

today, mainly because they are much more ecient at modelling longer sec-

tions of speech. Just like for the images, the input data is unstructured, with

the technology using supervised learning to match the encoded words with

tagged training data (as such there are a number of publicly available speech-

related training sets).

ere are many challenges to an ecient and accurate speech recognition

system, as most readers will have experienced themselves whilst trying to get

their smartphone to understand a voice command from them. One of the

main challenges is the quality of the input—this could be because of a noisy

environment or because the voice is on the other end of a telephone line—

accuracy on the phone drops from a best-in-class Word Error Rate (WER) of

around 7% to more than 16%. (Human accuracy is estimated to be around

4% WER.)

Other challenges include obvious things like dierent languages and

regional accents but a key consideration is the size of the vocabulary that will

be required. For a very specic task, say checking your bank balance, the

vocabulary will be very small, perhaps ten or so words. For systems that are

expected to answer a wide range of questions, such as Amazon’s Alexa, then

the vocabulary will be much larger, and therefore present a greater challenge

for the AI.

A wider vocabulary requirement also means that context becomes more

challenging to determine. Context is important in speech recognition because

it provides clues to what words are expected to be spoken. For example, if we

hear the word ‘environment’ we would need to understand its context to

determine whether the speaker meant ‘our immediate surroundings’ or ‘the

natural world’. DNNs, and a particular type of DNN called a Recurrent

Neural Network, are very good at looking backwards and forwards through

the sentence to constantly rene the probability that it is one meaning or

another.

It is worth pointing out that speech recognition is a slightly dierent con-

cept to voice recognition. Voice recognition is used to identify people from

their voices and not to necessarily recognise the words they are saying. But

many of the concepts used in the two applications are similar.

Speech recognition, and its cousin NLU, are seeing plenty of development

activity as people become more used to the technology through their smart-

phones, and therefore more comfortable and condent with talking to

machines. As a ‘user interface’, speech recognition will likely become the

dominant input method for many automated processes.

3 AI Capabilities Framework

I use the word ‘Search’ in a specic sense here (another common term for this

is ‘Information Extraction’). What this AI capability is all about is the extrac-

tion of structured data from unstructured text. So, just as the Image

Recognition and Speech Recognition capabilities worked their ‘magic’ on pic-

tures and sounds, so the Search capability does with documents.

ere are strong elements of Natural Language Processing (NLP) used in

this capability to extract the words and sentences from the passages of text, but

I prefer to use the term ‘Search’ because it better describes the overall capabil-

ity. Later on in this chapter you will hear about Natural Language Understanding,

which is generally described as a sub-set of NLP, but, in my mind, isa capabil-

ity in itself which complements the outputs of Speech Recognition and Search.

AI Search, which is almost exclusively a supervised learning approach,

works on both unstructured and semi-structured data. By unstructured data

I mean something like a free-form email or a report. Semi-structured docu-

ments have some level of consistency between dierent instances but will

have enough variability to make it extremely dicult for a logic-based system

(such as a Robotic Process Automation [RPA] robot) to process.

A good example of a semi-structured document is an invoice. An invoice

will generally have the same information as another invoice, such as the sup-

plier’s name, the date and the total value. But one may have a VAT amount on

it, another may have a sub-total and a total, another may have the address

written in the top right-hand corner rather than the top left, another may

have the supplier’s name written slightly dierent and another may have the

date written in a dierent format.

e traditional way of getting the information o the invoice and into the

appropriate ‘system of record’ would be to use an Optical Character

Recognition (OCR) system and a template for each dierent version of the

invoices (which could run into the hundreds) so that it knew where to nd

each piece of data. An AI system, once trained on a sample of invoices, is able

to cope with all of the variabilities I have described. If the address is in a dif-

ferent place it will be found; if there is usually a VAT line and it’s not there

then that doesn’t matter; if the date is in a dierent format it will be recog-

nised (and converted into the standard format).

Interestingly, the AI works in the opposite way to the template approach—as

more and more ‘versions’ of the document are found, rather than becoming

more uncertain and having to create more and more templates, the AI become

more condent and is able to cope with even more variability. is is because it

is essentially matching the ‘patterns’ of one document with the learned patterns

of the documents used in training.

In the case of unstructured text such as a free-form email, the AI can do two

things. e rst is to categorise the text by matching the patterns of words

with what it has already learnt. For example, if you gave the AI a random news

article it could work out whether that article was about politics, business,

sport and so on, as long as it had been trained on many other tagged news

articles. It would do more than simply look for the word ‘football’ to identify

a sports article (there are many business articles about football, for example)

but would instead look at the article holistically, creating an algorithmic

model that represents ‘sports articles’. Any new article with a similar model,

or pattern, would likely be about sports.

e second main thing that an AI search capability can do is to extract

‘named entities’. A named entity could be a Proper Noun—a place or a per-

son, for example—or even a date or quantity. So, in the passage of text,

“Andrew Burgess, who lives in London, wrote his second book in 2017 which

was published by Palgrave Macmillan”, the named entities would be ‘Andrew

Burgess’, ‘London’, ‘second’, ‘2017’ and ‘Palgrave Macmillan’.

ere are specic Named Entity Recognition (NER) algorithms that carry

out this task, but they all need to be trained and tuned to make them as accu-

rate as possible. e best NER systems for English currently produce near-

human performance. Accuracy for NER can be improved by training them on

specic domains of information, so one could be trained on legal documents

and another on medical documents, for example.

e categorisation and entity extraction tasks can be combined to enable

free-form, that is, unstructured, text to be ‘read’ and categorised, and then

have all the meta-data extracted. is would mean that, for example, a cus-

tomer who emailed a request to a company could have that email categorised

so that it can be automatically forwarded to the right person in the organisa-

tion along with all the relevant meta-data that has been extracted. is meta-

data can be automatically entered into the company’s Case Management

System so that the customer service agent has all the information available

when the case is received.

Search, or Information Extraction, is probably one of the most developed

capabilities in the Framework. ere are established software vendors with

relatively mature products, as well as many start-ups all busy building

applications in this space. As you will nd in subsequent chapters, the princi-

pal attraction of this capability right now is that it provides a useful comple-

ment to RPA; the robots need structured data as their inputs and AI Search

can turn unstructured text into structured data, thus opening up many more

processes that can be automated through RPA.

3 AI Capabilities Framework

Clustering

All the capabilities I have described so far work on transforming unstructured

data (images, sounds, text) into structured data. In contrast, Clustering, our

fourth capability, works on structured data, and looks for patterns and clusters

of similar data, within that data; that is, it is a ‘classier’. e other aspects of

this capability that are dierent from the others is that it can (but by no means

has to) learn unsupervised, and it takes a very statistical approach. But just

like the others, it still requires large amounts of data to be truly valuable.

Clustering is usually the rst part of a series of stages that usually end with

a prediction, for example extracting insights from new data based on align-

ment with the original patterns, or identifying anomalous new data where it

doesn’t match the expected patterns. In order to be able to make those predic-

tions or identify the anomalies, the patterns/clusters must rst be discovered.

In its simplest form, this type of AI uses statistical methods to nd the ‘line

of best t’ for all the data (in mathematical terms it is minimising the square

of the distance from all the points to the line). It is then just a case of making

those statistical methods more and more complex in order to cope with more

and more features of the data. If there isn’t enough data, then the solution can

suer from what is called ‘over-tting’—this is where a theoretical line of best

t is calculated but it bears little resemblance to any real trends in the data.

e more data you have, the more condent you can be in the patterns that

are found.

A good example of the use of clustering is to be able to identify similar

groups of consumers within customer buying-behaviour data. Humans will

usually be able to identify patterns within small data sets and will often use

past experience to help them shape those patterns. But where there are thou-

sands or millions of data points with multiple characteristics and features,

humans will nd it impossible to process all that information, and the AI will

come into its own.

e benet of the approach, apart from the ability to use sheer computing

power to analyse data quickly, is that the AI is eectively naive to the data. It

is only looking for patterns in numbers, and those numbers could relate to

anything, including a person’s height, their salary, their eye colour, their post-

code, their gender, likes, dislikes and previous buying history. If you were in

charge of the loyalty card data for a retailer, you may not have realised that

there is a correlation between eye colour and propensity to buy yoghurt (I’m

making this up) but, if it exists, the AI will nd it. No one would have thought

of trying to match these two features and so, if they were using a traditional

Clustering

Business Information tool, wouldn’t have thought to ask the question in the

rst place. It is this insight that AI can bring that provides much of its value.

Just like the other capabilities in the ‘Capturing Information’ group, the

Clustering approaches are reasonably mature and are being used in business—

it is the basis of the eld of Predictive Analytics. You only have to buy any-

thing online to see how you have been pattern-matched against other

consumers and then presented with an oer to buy something that they have

bought in the past. Or you may have received a special oer from your mobile

phone provider because some of your usage patterns and behaviours have

indicated you may be a ‘ight risk’ and they want to keep you as a customer.

Or you may have received a call from your bank because of some of your

spending behaviour doesn’t t into the normal patterns raising the possibility

that you may have had your credit card skimmed.

It is this Clustering capability that benets more than any of the others

from the sheerpropensity of data in the world today.

Natural Language Understanding

Now we are ready to move on to the next type of AI objective—‘determining

what is happening’—and its rst capability, Natural Language Understanding,

or NLU.

NLU is an important part of the AI world because it provides some mean-

ing to all the text we are bombarded with, without having to resort to humans

to read it all. It acts as a ‘translator’ between humans and machines, with the

machine having to do the hard work. NLU is closely related to the Search

capability I discussed earlier: some people would actually group Search under

the wider banner of NLP, but I prefer to split them out as they are generally

used for dierent objectives in business. e same can be said of Speech

Recognition, but again, using these capabilities in the context of the business

setting, they provide dierent objectives. NLU is still, to a certain degree,

turning the unstructured data of a sentence into the structured data of an

intent, but its primary purposes are to work out what is the right structure

(syntactic analysis) and what is the meaning (semantic analysis) of the words

and sentences.

NLU has a special place in the history of AI because it forms the basis of

the Turing Test. is is the test that English polymath Alan Turing devised in

the 1950s to dene whether a machine could be classed as articially intelli-

gent or not. In the test, an evaluator would have a typed conversation with a

computer and a human, both hidden behind screens—the computer would

3 AI Capabilities Framework

pass the test if the evaluator could not tell which of the conversations was with

a computer or a human. e Turing Test is now an object of some debate—it

is not only highly inuential, particularly when discussing the philosophy of

AI, but also highly criticised. A number of systems, most notably ‘Eugene

Goostman’, a Russian chatbot, have claimed to have passed the test, but the

real question, now that AI has developed so much further, is whether it is a

valid test of intelligence in the rst place.

NLU uses supervised learning with machine learning in order to create a

model of the input text. is model is probabilistic, meaning that it can make

‘softer’ decisions on what the words mean. NLU is a very complex research

eld and I won’t begin to go into the details here, suce it to say that there are

many challenges, such as being able to cope with synonymy (dierent words

that have similar meanings) and polysemy (words that have several meanings).

e phrase ‘Time ies like an arrow. Fruit ies like a banana.’ perfectly sums

up the challenge that NLU researchers face.

We all see NLU in action most days. Siri or Cortana or Alexa all have

usable natural language interfaces. e majority of the time they will under-

stand the words you are saying (using Speech Recognition) and convert that

into an intent. When these systems don’t perform well it is either because the

words weren’t heard correctly in the rst place (this is the most common rea-

son) or the question that has been asked doesn’t make sense. ey will be able

to understand dierent versions of the same question (“What was the football

score?”, “Who won the football match?”, “Please can you tell me the result of

the football?”, etc.) but if you ask “Score?” they will struggle to come up with

a relevant answer. Most systems will have default answers if they can’t work

out what you want.

Chatbots, where the questions and answers are typed rather than spoken,

use NLU but without the ‘risk’ of Speech Recognition getting the word inter-

pretation wrong. ey therefore tend to be the simpler and preferred choice

when businesses are looking to create NLU interfaces with their customers,

but the use of speech recognition shouldn’t be discounted if the environment

and benets are good enough.

And, of course, these personal assistants can talk or type back to you, but

the answers tend to be stock phrases. For more bespoke responses a specic

sub-set of NLP called Natural Language Generation (NLG) is required. NLG,

which can be considered as the ‘opposite’ of NLU, is probably the hardest part

of NLP, which is why it has only recently become commercially available.

Rather than the limited set of response phrases used in the majority of chat-

bots, creating whole new phrases and even articles is possible with

NLG. Applications include creating hyper-local weather forecasts from

Natural Language Understanding

weather data, or nancial reports from company’s earnings data or stock mar-

ket data. Each of these provides a short narrative in a natural language that

can be hard to dierentiate from one generated by a person.

NLU is also used to try and understand the emotion behind the sentences,

an area known as sentiment analysis. In its simplest form sentiment analysis

looks for ‘polarity’, that is, is the text positive, negative or neutral. Beyond

that it will look for the type of emotion that is being expressed—is the person

happy, sad, calm or angry, for example. e AI will, of course, look for specic

words within the text but it will try and put these in context, as well as try to

identify sarcasm and other tricky idiosyncrasies of language.

Sentiment analysis is already being used widely to assess online text from

customers, such as through Twitter and TripAdvisor. is allows businesses to

evaluate how their products or services are being perceived in the market

without the expense of focus groups or surveys, and allows them to respond

to potential issues in real time. Specic models, with the associated terminol-

ogy and jargon, are usually generated for particular needs; understanding how

customers perceive a hotel room will be quite dierent to how they feel about

their mobile phone.

Another common application for natural language technologies is machine

translation. is is where a computer will translate a phrase from one lan-

guage to another. It is a complex problem to solve but recent developments in

deep learning have made this much more reliable and usable. e key to suc-

cess, as with most AI challenges, is the availability of data, and companies like

Google have used the transcriptions from the European Parliament, where the

proceedings are translated by humans between 24 dierent languages, as its

training set. at means that translating from, say, English to French is now

highly accurate, but for less common languages, such as Khmer, the ocial

language of Cambodia, translations are generally done through an intermedi-

ary stage (usually the English language). Chinese is notoriously dicult for

machines to translate, mainly because it is more of a challenge to edit sen-

tences as a whole—one must sometimes edit arbitrary sets of characters, lead-

ing to incorrect outcomes.

e natural evolution of machine translation is real-time translation of

speech (epitomised by the Babel Fish in e Hitchhiker’s Guide to the Galaxy).

Already smartphone apps are able to read and translate signs (using Image

Recognition, NLU and machine translation) and can act as interpreters

between two people talking to each other in dierent languages. Currently

these capabilities are less reliable than typed translations and only work for

common language pairs, but the potential uses for this technology in a business

environment are huge.

3 AI Capabilities Framework

NLU is leading the charge for AI in business: it allows us to communicate

with computers in the way we feel most comfortable with—we don’t all have

to be computer wizards to be able to work them eectively. But NLU is inher-

ently a dicult technical challenge and will always be compared with, and

evaluated against, humans (unlike other AI capabilities such as Clustering

which can easily surpass human performance). erefore, until it can be said

to be indistinguishable from us (i.e. truly pass the Turing Test) NLU will

always be open to criticism. Applied in the right environment and with the

right processes though, it can deliver substantial benets to businesses.

Optimisation

In all the capabilities I have discussed so far, we have been manipulating data

to transform it from one form to another (images to descriptions, sounds to

words, words to meanings, text to information and big data to meta-data).

But even though the transformed data is much more useful to us than the

source data was, we haven’t yet done anything really meaningful with it. at’s

where the AI capability of Optimisation comes in.

Optimisation is at the heart of what people generally think AI does. It is the

closest analogy we get to mimicking human thought processes without having

to call on true cognitive Understanding.

I use the title ‘Optimisation’ for brevity, but it actually includes problem

solving and planning, which makes it quite a broad subject that has a lot of

science behind it all. A broad denition of this capability would be that if you

know a set of possible initial states as well as your desired goal and a descrip-

tion of all the possible actions to get you there, then the AI can dene a solu-

tion that will achieve the goal using an optimum sequence of actions from any

of the initial states.

Historically, as I discussed at the start of Chap. 2, optimisation and prob-

lem solving were achieved through ‘expert systems’, which were really nothing

more than decision trees which had to be designed and congured up-front

by humans. With the advent of machine learning, much of that design and

conguration is carried out by the AI itself using an extreme version of trial-

and- error. (ere is still, by the way, a place for knowledge-based AI systems

in the world today, as I shall discuss later.)

A useful way to understand the Optimisation capability is by looking at where

AIs are taught to play computer games. Scientists and researchers use computer

games as a kind of test and benchmark for how clever their systems are: can this

Optimisation

particular AI beat the best human at playing that particular game? But it also

provides a very useful way of explaining what the dierent avours are.

So, for example, to play chess and beat Gary Kasparov required brute

force—these systems ‘simply’ analysed every possible move much further

ahead than a human can do. at is more logic than it is AI.To learn and win

at Space Invaders or Breakout, however, needs DNNs and what is called ‘rein-

forcement learning’ (more of which later). From only being given the objec-

tive to maximise the score, and no other information on how to play, these AIs

perceive the state of the screen, trying lots of dierent approaches until the

score increases (shooting aliens, not getting bombed, etc.). en, through

reinforcement, they start to develop strategies (such as getting the ball behind

the wall in Breakout) that get higher scores. It’s a great example of self- learning,

but in a relatively simple environment.

In order to learn the Chinese game of Go, things are a lot trickier. Until

very recently, a computer being able to play Go was a Holy Grail for AI

researchers, mainly because the game relies more on ‘intuition’ than logic, but

also because the number of possible move combinations is trillions of times

greater than a game of chess (there are more possible Go moves than there are

atoms in the universe). In 2016, an AI designed by DeepMind, a British com-

pany owned by Google, beat the best Go player in Europe four games to one.

AlphaGo, as the system is called, uses the same concept of reinforcement

learning, this time based on studying 30 million moves from human Go

matches. It then plays dierent versions of itself thousands of times to work

out the best strategies, which it uses to create long-range plans for the actual

games. Apparently, during the second match, the human player, Lee Sedol,

‘never felt once in control’ (a general fear of AI that is shared by many people,

it must be admitted). Since then, AlphaGo has beaten the world’s best Go

player, Ke Jie, three games to zero.

So, the key characteristic of the Optimisation capability is that there is a

goal to be achieved—an idea that needs to be reasoned, a problem to be

solved or a plan to be made. At its simplest form this is done through iterative

trial-and error—small changes are made to the environment or small actions

are taken, after which the situation is evaluated to see whether it is closer to

the goal or not. If it is closer it carries on and makes further changes, if not it

tries something dierent.

As I’ve hinted with the gaming examples above, there are some subtleties to

this overly simplied description, which all come together to provide the AI

Optimisation capability. I’ve described a number of the most common

approaches below, most of which are inherently inter-connected.

3 AI Capabilities Framework

Cognitive reasoning systems are the modern equivalent of the old Expert

Systems (which is why some people are reluctant to describe them as true AI

today). ey work by creating a ‘knowledge map’ of the specic domain that

is being modelled, which means that they do not require any data to be trained

on, just access to human subject matter experts. ese knowledge maps con-

nect concepts (e.g. food and person) with instances (e.g. chips and Andrew

Burgess) and relationships (e.g. favourite food). Dierent relationships can

have dierent weights or probabilities, depending on their likelihood, which

means that the system can be interrogated to make recommendations. Unlike

decision trees, the system can start from any point on the map as long as a goal

has been dened (‘what is Andrew’s favourite food?’, ‘which person likes

chips?’) and can handle much greater complexity: its ‘clever’ bit is in the way

it comes to a recommendation using the shortest route through the map

rather than asking a linear series of questions. Compared to machine learning

approaches, cognitive reasoning systems have the major advantage that the

recommendation that has been made is fully traceable—there is none of the

‘black box’ characteristics that machine learning systems suer from. is

makes these sorts of systems ideal for regulated industries that ‘need to show

their working’ (Fig.3.2).

Beyond Cognitive Reasoning, the majority of AI optimisation approaches

are based around algorithms. A key AI concept involves the idea of breaking

a big problem down into much smaller problems and then remembering the

solutions for each one. is approach, known as Dynamic Programming, is

Place

Establishment

Qualification

Person

Subject

located in

studied at

awarded

type

studied

specialises in

Fig. 3.2 Knowledge map

Optimisation

epitomised in the Coin Change Problem, which asks: how can a given amount

of money be made with the least number of coins of given denominations? If

my coins are in units of 1,4,5,15,20 and the total amount I need to make is

23, then a system which looked at it as a series of independent, ‘blind’ deci-

sions would start with the biggest coin rst and then add smaller coins until

the total was reached: this would be 20+1+1+1, so four coins. A dynamic

system though would have broken the problem down into smaller problems,

and stored each of the optimal solutions. It would have come up with an

answer of 15+4+4, so just three coins.

e system works by looking ahead at lots of dierent ways that the end

objective could turn out to be based on, analysing a large sample of the dier-

ent individual steps that can be taken. ose sample actions that lead to, or

get close to, the desired goal are remembered as favourable and therefore more

likely to be chosen.

A common AI approach that exploits this method is called the Monte

Carlo Tree Search (MCTS). When, for example, playing a game it can ‘play

out’ lots of dierent versions of potential moves of both players, with each

move creating a branching tree of child moves. Using back propagation (a

form of feedback loop), those moves that achieve the desired goal have the

connections between each of the nodes that led to that result strengthened so

that they are more likely to be played.

ese MCTS-like approaches do have a couple of drawbacks: they need to

balance the eciency of a small sample size of moves with the breadth of cov-

ering as many moves as possible (this leads to some randomisation being built

into the choice of child nodes), and they can be slow to converge on to the

optimal solution.

Reinforcement Learning, an area of AI that is seeing a great deal of activity

at the moment, helps to alleviate some of these ineciencies. Reinforcement

learning is really an extension of dynamic programming (it is sometimes

referred to as ‘approximate dynamic programming’) and is used where the

problems are much more complex than, for example, the coin change problem

or simple board games, and where there are more unknowns about the states

of each move that is taken. Reinforcement learning uses extreme trial- and- error

to update its ‘experience’ and can then use that machine-learned experience to

determine the most optimal step to take next, that is, the one that will get it

closer to achieving the goal. is approach is slightly dierent to the super-

vised learning approaches I described in Chap. 1 in that the ‘right answer’ is

never given in the training, nor are errors explicitly corrected: the machine

learns all of this itself through the trial-and-error iterations.

3 AI Capabilities Framework

A tactic that is usually employed in reinforcement learning is to have the AI

systems play against each other. DeepMind’s AlphaGo, once it had been

trained on human games, then played itself (or, to be exact, two slightly dif-

ferent versions of AlphaGo played each other) over thousands of games in

order to rene its game even further. If it had only learnt from human games

it could only ever be as good as humans, whereas if it plays itself, it is possible

for it to surpass human skill levels. is combative approach, known as

Generative Adversarial Networks, as well as allowing the algorithms to tune

themselves, is a great way to run simulations to test the viability of AI systems

and to run dierent war-gaming scenarios.

Another ‘weakness’ in AI Optimisation approaches has been that the sys-

tems tend to look at the short-term gains at the expense of the longer-term

strategy. Recent advances in AI have seen a number of dierent systems com-

bined to provide both these perspectives, especially where the domain is par-

ticularly complex: a ‘policy’ algorithm will look at the next best move, whilst

a ‘value’ algorithm will look at how the problem, or game, might nish up.

e two algorithms can then work together to provide the best outcome.

Facebook has already been able to train chatbot agents to negotiate for

simple items, in some cases doing it as well as humans. Facebook Articial

Intelligence Research rst used supervised learning to train the agents on a

large number of human-to-human negotiation scripts. is stage helped them

imitate the actions of the humans (mapping between language and meaning),

but didn’t explicitly help them achieve their objectives. For this they used

reinforcement learning, where two AI agents would ‘practise’ negotiating with

each other (interestingly they had to x the language model in place at this

stage because they found that, if the agents were allowed to continue learning

the language elements, they would start to create their own private language

between them). At the end of each negotiation, the agent would be ‘rewarded’

based on the deal it had managed to achieve. is was then fed back through

the model to capture the learning, and make the agent a better negotiator.

Following training, it could then negotiate for the same types of items with

humans, matching the capabilities of other human negotiators.

Similar ideas of combining a number of AI techniques were used when an

AI system, called Libratus, was able to beat experienced players of poker—this

system used three dierent types of AI: the rst relied on reinforcement learn-

ing to teach itself the game of poker from scratch; a second system focused on

the end game, leaving the rst system to concentrate on the immediate next

moves; and, because some of the human players were able to detect trends in

how the machine bet, a third system looked for these patterns overnight and

introduced additional randomness to hide its tracks.

Optimisation

Optimisation AI can be applied to many situations where there is a specic

goal to be achieved, such as winning a hand of poker or negotiating for items.

It can, as I mentioned earlier, provide performance that exceeds human capa-

bilities. Other typical uses for the Optimisation capability include route plan-

ning, designing rota for shift workers (such as nurses) and making

recommendations.

All the approaches described in this section are just a sample of how AI can

attempt to solve problems, but should give you a feel for the general strategies

that can be employed. At the heart of it is the idea that big decisions can be

broken down into many small decisions, and that these can then be optimised

using trial-and-error so that a dened goal can be achieved, or a specic

reward maximised.

Prediction

Prediction employs one of the core ideas of AI in that it uses lots of historical

data in order to match a new piece of data to an identied group. us, pre-

diction generally follows on fromthe Clustering capability described earlier in

the chapter.

I’ve already mentioned one of the more common uses of prediction, that of

recommending related online purchases (‘you bought this book therefore you

will probably like this other book’). So, in this case, what the retailer calls a

‘recommendation’ is actually a prediction they make in order to sell you more

stu.

Some decisions can be described as predictions—if you apply for a loan

that is assessed by a machine, then the AI will try and predict whether you will

default on the loan or not. It will try and match your prole (age, salary, regu-

lar spending, other loans, etc.) with other customers that have similar proles.

If those other matching customers generally default on their loans then you

will likely be refused the credit line.

Prediction can also follow on from the Search capability. Because Search

can look for patterns within text, it can match these with patterns from some

pre-denedrequirements. For example, CVs (aka résumés) can be matched

with job descriptions in order to predict good candidates for the role.

e prediction capability diers from Optimisation in that it doesn’t have

a specic goal to achieve. ere are no steps to determine to achieve a specic

objective—we are ‘just’ matching a new data point with our historical data.

At a simple level, predictions can be made from just a few ‘features’—these are

the specic characteristics that are being measured, such as the number of bedrooms

3 AI Capabilities Framework

in a house and the size of the garden. If you had a table of this data and the actual

price of each of the houses, then you could use this capability to predict the price of

another house if you knew the number of bedrooms and size of the garden. And

you wouldn’t necessarily need AI to do this, or even a computer.

But there are generally more factors (or features) which determine the price

of a house than just these two things. You might also consider how many stories

it has, whether it is detached, semi-detached or terraced, whether it has parking,

a utility room, a swimming pool, its postcode and so on. All this makes it much

more dicult to predict the house value without using some AI ‘magic’.

Clearly, the more features that are considered, the more training data needs

to be used, so that as many ‘variations’ of all the features can be captured and

built into the model. is model will have considered all the dierent inu-

ences (or ‘weights’) that each of the features has on the house price. By enter-

ing in the values of a selection of features of a dierent house, the model ts

(using Regression Analysis in this case) those features as closely as possible and

can therefore predict the house price. It is normal for the system to give a

gure for its level of condence (i.e. the probability that it is correct).

Once the number of features is in hundreds or thousands then things become

more complex—this just means that more algorithms, more training data and

more computing power are needed. At the extreme end of this, the systems that

are used to predict our weather are some of the most powerful in the world.

An important aspect of the Prediction capability is to remember that it is

essentially naive. By this I mean that it is only manipulating numbers and has

no underlying understanding of what that data actually means. ose house

features could be car features or weather features or people features—to the

computer they are just numbers.

is naivety, whilst seeming like a virtue, is also one of Prediction’s biggest

challenges: that of unintended bias. Normally AI is lauded because it is not

inuenced by the prejudices that humans inherently suer from (there are

many studies that show that, although people may claim not to be biased in,

say, recruiting sta, there is usually some sort of unconscious bias involved).

But the unbiasedness of AI is only as good as the data that is used to train it—if

the training set contains CVs and hiring decisions that have been made by

humans, the biases that were in the original data will be ‘trained into’ the AI.

Another key issue for AI Prediction is the opaqueness of the decision- making

process. When an AI makes a prediction, for example that a person is more than

likely to default on a loan and therefore their application should be rejected, that

prediction is based on all the training data. e training data has been used by the

machine to build an algorithmic model that it then uses to predict new cases from.

For the more complex algorithmic models this is just a matrix of numbers that

Prediction

makes no sense to a human trying to read it, and therefore the reason (or, more

likely, reasons) that the person had their loan refused is not easily available to scru-

tinise. (Simpler algorithmic models, such as Classication and Regression Trees,

do provide some transparency and are therefore more popular). Of course, we

don’t need to understand the workings of every prediction that is made (it would

be interesting to know why the house price predictor came up with that valuation,

but it is the actual house price we are most concerned with), but some industries,

especially those that are regulated, will require it. Also, if you are the person that

has been refused the loan, you should have the right to know why.

AI is being used to predict many things, including, in some US courts, a

defendant’s risk prole. When the consequences of those predictions could

have an inuence on whether someone is found innocent or guilty, then the

challenges I have described above become very serious indeed. As Melvin

Kranzberg’s rst law of technology succinctly states: technology is neither

good nor bad; nor is it neutral. ese challenges of ‘algorithmic transparency’,

naivety and unintended bias will be discussed in more detail in Chap. 8.

AI Prediction is one of the most active areas in the eld at the moment.

Where there are lots of good data, predictions can generally be made from

that data. at doesn’t necessarily mean that predictions need to be made or

that they will be useful in any way, but there are lots of cases where this can be

very benecial indeed, including predicting valuations, yields, customer

churn, preventative maintenance requirements and demand for a product.

Understanding

I include this section on AI Understanding in the book only as a way to

describe what is not currently available in the AI world to businesses, or to

anyone outside of a research lab. It should be seen as a countermeasure to all

of the hype that can be put out by over-excited marketing departments.

By ‘understanding’, I am generally referring to the ability of a machine to

have conscious awareness of what it is doing or thinking (or to act like it

does—see next paragraph). is implies it can understand the intent and

motivations of people, rather than just blindly crunching numbers about

them. It is usually described by the concept of Articial General Intelligence

(AGI), where the AI is able to mimic all of the capabilities of the human

brain, not just the very narrow ones we have discussed so far.

ere is an interesting subtlety to the description of AGI.John Searle, a

philosopher, dierentiated ‘strong AI’ and ‘weak AI’. A strong AI system can

think and have a mind, whereas a weak AI system can (only) act like it thinks

3 AI Capabilities Framework

and has a mind. e former assumes that there is something special about the

machine that goes beyond the abilities that we can test for. Ray Kurzweil, the

futurologist, simply describes strong AI as when the computer acts as though

it has a mind, irrespective of whether it actually does. For our purposes, we

will stick to this denition as we are dealing with practicalities rather than

philosophical debate.

So, what sort of tests would we use to claim strong AI? I have already men-

tioned the Turing Test as a (limited) test of AI.Other tests that people have

proposed are:

• e Coee Test (Wozniak)—A machine is given the task of going into an

average home and guring out how to make coee. It has to nd the coee

machine, nd the coee, add water, nd a mug and brew the coee by

pushing the proper buttons.

• e Robot College Student Test (Goertzel)—A machine is given the task

of enrolling in a university, taking and passing the same classes that humans

would, and obtaining a degree.

• e Employment Test (Nilsson)—A machine is given the task of working

an economically important job, and must perform as well or better than

the level that humans perform at in the same job.

No AI systems are anywhere near passing these tests, and any systems that

are even close will be a combination of many dierent types of AI.As I’ve

described in the previous sections of this chapter, there are a number of dier-

ent types of AI capabilities, each one very specialised in what it does. is

means, for example, an AI capability that is being used to recognise images

will be useless at processing language. is is the concept of Articial Narrow

Intelligence (ANI). Even within the capability groups I have described there

is little or no crossover between specic uses—if I have a system that extracts

data from invoices, it will not be able to do the same for remittance advices,

without training the system from scratch. e same could even be true if I

wanted to take a trained invoice extractor from one business to the another—

there may be enough variation between businesses to mean that retraining

would be required.

Where our brain is so much cleverer than AI is where it is able to use dif-

ferent cognitive approaches and techniques in dierent situations and, impor-

tantly, take learnings from one situation and apply them to a completely

dierent one. For example, I may know that the value of a house generally

increases with the number of bedrooms it has, but I can then apply the same

concept to other objects (computers with more hard drive capacity cost more)

Understanding

but also know that this shouldn’t be applied to everything (cars with more

wheels are generally not more expensive). AI is not able to do this right now.

ere is work being done to try and create AGI.At a national scale, there

is the Blue Brain Project in Switzerland (which aims to create a digital recon-

struction of the brain by reverse-engineering mammalian brain circuitry), and

the BRAIN Project in the United States, which is also looking to model real

brains. Organisations such as OpenCog (an open-source AGI research plat-

form), the Redwood Center for eoretical Neuroscience and the Machine

Intelligence Research Institute are all at the forefront of researching various

aspects of AGI.

Interestingly, there has been some progress in getting neural networks to

remember what they have previously been taught. is means they could, in

theory, be able to use learnings from one task and apply it to a second task.

Although that may sound like a simple thing for a human to do, ‘catastrophic

forgetting’ (when new tasks are introduced, new adaptations overwrite the

knowledge that the system had previously acquired) is an inherent aw in neu-

ral networks. DeepMind, the UK AI company owned by Google, is developing

an approach it calls Elastic Weight Consolidation which allows the algorithm to

learn a new task whilst retaining some of the knowledge from learning a previ-

ous task (they actually use dierent Atari computer games as their test tasks).

is is showing promise but is a long way from being put to practical use.

Despite the research and small steps that are being taken, the ability for a

computer to fundamentally understand what it is doing is still a long way o

(some argue that it could never be possible). Even using Searle’s denition of

weak AI, there are major hurdles to overcome. Some of these are very techni-

cal (such as the challenge of catastrophic forgetting) and some are simply

down to the fact that the necessary computing power doesn’t exist at the

moment. But, as described throughout this book, there are huge advance-

ments being made in ANI that provide real benets to people and businesses.

Some of these make our lives simpler whilst others are able to far exceed our

own competencies in specic tasks. By understanding each of these capabili-

ties, and their respective limitations, we are able to benet from AI technolo-

gies today and tomorrow.

Using theAI Framework

e AI Capability Framework is my attempt to bring some clarity and order

to the diverse, complex and often confusing eld of AI.By ‘boiling it down’

into a set of discrete capabilities, AI hopefully becomes accessible to those

3 AI Capabilities Framework

who want to benet from the technology but do not have the skills, experience

(or desire) to understand the technical aspects beyond the high-level apprecia-

tion I have provided in this chapter.

I have tried to dierentiate each of the capabilities as much as possible, but

there are inevitably some overlaps between each of them, for example between

speech recognition and NLU, and clustering and prediction. e boundaries

between these are blurry; some people would describe both speech recogni-

tion and NLU as a sub-category of NLP but I think they sit better as discrete

capabilities; and some people might separate out planning and optimisation,

but I think they are close enough aligned to keep them as one. So, please don’t

get too hung up on some of the nuances—AI is a complex subject, with many

dierent viewpoints and opinions, and is constantly changing. e frame-

work should be treated as your guide rather than a technical manual (Fig.3.3).

So, the knowledge you now have should enable you to do three things:

• Identify the right AI capabilities for your business need. What are your

business objectives and could AI provide all or part of the solution? Are you

looking to capture information or understand what is happening, or both?

Do you want to replace existing capability (computer or human) with AI,

or do you want to augment their capabilities further? Which specic capa-

bilities will you need to create a solution? Will that require a supervised

learning approach, and what data do I have available?

CAPTURE INFORMATIONWHAT IS HAPPENING? WHY IS IT HAPPENING?

Image Recognition

Speech Recognition

Natural Language

Understanding

Data Analysis /

Clustering

OptimisationSearch

Prediction

Understanding

stru

ctured

data

unstructured

data

Fig. 3.3 The AI framework

Using theAI Framework

• Look beyond the hype. What capabilities do the AI vendors actually have,

rather than those they claim they have? How do these capabilities match

my requirements? Do their assertions about their product make sense? Are

there gaps that will need to be lled with dierent AI solutions or tradi-

tional technology? Will it be as easy to implement as they claim?

• Be realistic. What are the limitations of the AI capabilities that I might need?

Is AI the most appropriate solution for this or are there simpler or more eec-

tive solutions? Do I have the necessary data available to train the system

adequately? Will I need to bring in external support to help me get going?

Chapter 5 provides real examples of the dierent AI capabilities being used

in businesses today. ey are focused on common strategic objectives (enhanc-

ing customer service, optimising business processes and generating insights)

and combine dierent capabilities to achieve these. Each of the examples ref-

erences the capabilities that are being used and, where appropriate, what chal-

lenges and limitations were experienced by those responsible for delivering

the solutions.

Some of the approaches and examples that will be described required addi-

tional technologies (such as cloud computing and RPA) to enable them to

extract their full value. e following chapter outlines what these associated

technologies are and why they are such a good t with AI.

3 AI Capabilities Framework

The AI Start-Up’s View

is is an extract from an interview with Vasilis Tsolis, founder and CEO of

Cognitiv+, an AI start-up in the legal sector.

AB What got you into the world of AI in the rst place?

VT My personal background is not an obvious path for a co-founder of an AI

start-up. I am a chartered civil engineer who switched my career by

undertaking a law degree. After graduation, my career was moving

between law, engineering and commercial management in various sectors

including infrastructure, construction and energy. After a few years,

something became very clear to me: people spend substantial amounts of

time reading contracts, and most of the time there is a tremendous repeti-

tion of those tasks. And that is where the problem lies: rather than focus

on advanced solution discovery, professionals consume their daily lives

with data gathering. e solution was an obvious one; using AI will free

the professionals to focus on what they are good at. at is why we started

Cognitiv+ in 2015, which has been a fantastic trip so far.

AB What is it like building an AI start up at the moment?

VT is is a period where people apply AI or attempt to use AI in almost all

aspects of our lives, where creativity sometimes trumps technology. ere

are major projects in process, such as autonomous cars and it very clear

now to everyone that AI is here to stay. It is a very exciting period, but

there are quite a lot of challenges towards the road of maturity.

Working with a number of professional clients, we see numerous

opportunities and dozens of use cases, some possible now, some reachable

within the mid-term future, some look close to science ction.

Technology advancements advertise extraordinary abilities and this goes

throughout the food chain, where clients are constantly bombarded with

innovative ideas where the success rate is variable. is could be because they

are early in their roadmap and suer teething problems, or because of a lack

of technical or subject matter depth which is sometimes not an easy x.

As with any early adoption, it is the clients and investors that get the

pressure to isolate the true technologists and innovators with clear busi-

ness skills, and some in-house knowledge on how technology works will

denitely bring certainty and a quick return on investment.

AB So what value do your customers get out of your software?

VT Reading text takes time, even Romans were complaining about this and

AI can help that. Cognitiv+ targets legal text, contracts and theregulation

corpus.

Using AI should be seen as an extrapolation of automation, a technol-

ogy that improves our lives, makes things faster, simpler and enables us to

explore tasks in a way that was not possible before. For example, clients

can use our software to analyse contractual risk on their whole portfolio

The AI Start-Up’s View

within hours in a way that was never possible before. Time-consuming

tasks can be delegated and free up professionals’ schedules, where tech-

nology can provide them with holistic views of their third-party risk to

levels that have been unprecedented.

AB What do customers or potential customers need to focus on if they are

going to get the maximum value from AI?

VT While many companies hit the ground by starting to write code, there is a

lot of preparatory work that projects need to undertake and ensure success.

e rst focus will be the data. Data scientists require massive data sets

and the new techniques, supported by hardware innovation, command

new levels of data quantities. e good news is that we all produce more

data every day, but this is not necessarily for all the aspects of professional

services. Some data custodians can move faster to this gold rush because

they have better access to data from others.

Quantity is not enough though—it is also the quality that will deter-

mine if the data can be used. But this is a synthetic challenge that can be

tackled only if it is isolated and understood on a case-by-case basis.

Second, teams need to make an early decision on what success would

look like, making business and technology targets that are reachable.

e fact that algorithms provide answers that are probabilistic and not

deterministic makes the way we manage AI projects very dierent from

any previous IT transformation projects companies have participated in.

irdly, an important focus would be the talent and skills diversity that

a team has. A sensible balance between business subject matter experts,

data scientists and coders would determine how successful a project will be.

AB How do you see the market developing over the next 5years or so?

VT While I can predict where we are heading in the next 5months, it is prac-

tically impossible to hit anything close to a 5year prediction. e reason

is simple—all the parameters are rapidly changing: the type of data we are

surrounded with, the size of data sets, the maturity of the algorithms,

hardware improvement, the list goes on.

But here is one prediction: as we have started using various NLP, NLU

and machine learning techniques to interpret text and documents, it

seems that certain types of text, certain sources, can be analysed better

than others. People will pick this up and will write in a way that can be

processed and summarised by machines. Why do that? It is for the same

reason where we think twice what tags we are using for our blog articles

as this will enable Search Engine Optimisation (SEO)bots to categorise

our text accordingly and disseminate it to the right channels.

Typically, we write in a way that our audience will understand us—my

take is that we will continue to do that—the dierence is that in the

crowd or readers will also be some bots. What might this look like?

Perhaps, simpler and shorter phrases where the object and subjects are

clear and NLP algorithms can consume them much better?

3 AI Capabilities Framework

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_4

Associated Technologies

Introduction

Articial Intelligence can do a lot of things, but it can’t do everything. Quite

a lot of the time implementing a stand-alone AI solution will satisfy whatever

objectives you are looking to achieve. Sometimes AI will rely on other tech-

nologies to make it work well, and other times it will complement them so

that they both work better. is chapter covers some of the associated tech-

nologies that anybody looking to implement AI will need to consider.

Some of these technologies are software-based, such as RPA. is is a rela-

tively new technology that automates rules-based processes but struggles to

handle unstructured data and any decision making. Cloud Computing, a

combination of software and hardware capabilities, is a key enabler of AI, and

many of the AI applications that we are seeing now would not be possible

without it.

ere are also some hardware technologies that enable, and can be exploited

by, AI.Physical robots (as opposed to the software-based RPA) can be made

more intelligent with the application of AI, and the Internet of ings (IoT)

can provide very useful data sources for AI systems. One of the technologies

that I have included in this chapter isn’t really a technology at all, but is pow-

ered by human beings: Crowd Sourcing enables data to be tagged and cleaned

in an ecient and exible way, and therefore is extremely useful to AI

developers.

I have not included more common enterprise systems, such as Enterprise

Resource Planning (ERP) and Customer Relationship Management (CRM)

systems, in this summary. All could be considered sources of data, and some

claim to have AI capabilities built into them (e.g. email systems can be a good

data source and also use AI to identify spam messages), but overall, they have

only general associations with AI.

AI andCloud

Cloud Computing is where a network of remote servers, hosted on the

Internet, are used to store, manage and process data, rather than the ‘tradi-

tional’ approach of using a local (on-premises) server or a personal computer

to do those tasks.

Because of these abilities to store, manage and process data away from the

user’s device (PC, mobile ‘phone, etc.) and instead on high-performance,

high-capacity specialised servers, Cloud Computing has become almost an

integral part of how AI systems operate today. As cloud technology itself

matures, the two will become inextricably linked—many people already talk

of ‘Cloud AI’ as the Next Big ing.

One of the more straightforward applications of cloud combined with AI

is the making available of large, public data sets. Most AI developers, unless

they are working for large corporates, don’t have their own data sets to train

their systems, but instead rely on these public databases. As I mentioned in

the Big Data section of Chap. 2, there exist quite a few of these datasets, cov-

ering subjects such as Computer Vision, Natural Language, Speech,

Recommendation Systems, Networks and Graphs, and Geospatial Data.

But the cloud provides more than just access to data—more often it will

actually process the data as well. We can experience this as consumers every

time we use a service like Amazon’s Echo. Although there is a small amount of

processing power on the device itself (mainly to recognise the ‘wake word’),

the processing of the words into meaning (using Speech Recognition and

NLU) is done by software on Amazon’s own servers. And, of course, executing

the actual command is also done on the cloud, perhaps sending the instruc-

tion back to your home to, say, turn the kitchen light on.

On an enterprise scale, even more of the processing work can be done on the

cloud, on huge server farms full of powerful machines. e biggest challenge

to this model though is that the data generally needs to sit on the cloudas well.

For enterprises with huge amounts of data (perhaps petabytes’ worth) then

transferring all of this to the cloud can be impractical. As a solution to very

long upload times, Amazon actually oers a huge truck (called the Amazon

Snowmobile) that contains 100 petabytes of computer storage—the truck is

driven to your data centre and plugged in so that the data can be uploaded to

4 Associated Technologies

the truck. It then drives back to the Amazon Data Centre and downloads it all.

But, as network speeds improve, there will be less and less need for these sort

of physical solutions.

Another challenge to Cloud AI is that it can be perceived as risky to store

data o-premises, especially if the data is condential, such as a bank’s cus-

tomers’ details. e security aspects can be covered by the type of cloud ser-

vice that is procured—the best suppliers can now oer security provisions that

are as good as, or better than, hosted or on-premises solutions. e UK

Government in fact released guidance at the start of 2017 that states it is pos-

sible for public sector organisations to safely put highly personal and sensitive

data into the public cloud.

e cost, though, of setting up an operation that can store, manage and

process AI data eectively, securely and economically means that the market

for Cloud AI is currently dominated by a small number of suppliers, namely

Amazon, Google and Microsoft. ese companies actual oer a complete set

of AI services, including access to ready-to-use algorithms.

Generally, the AI Cloud oerings are made up of four main areas (I’ve used

Amazon’s model as a basis for this description):

• Infrastructure—this consists of all the virtual servers and GPUs (the pro-

cessor chips) that are required to house the applications that train and run

the AI systems

• Frameworks—these are the AI development services that are used to build

bespoke AI systems, and tend to be used by researchers and data scientists.

ey could include pre-installed and congured frameworks such as

ApacheMXNet, TensorFlow and Cae.

• Platforms—these would be used by AI developers who have their own data

sets but do not have access to algorithms. ey would need to be able to

deploy and manage the AI training as well as host the models.

• Services—for those who don’t have access to data or algorithms, the AI

Services oer pre-trained AI algorithms. is is the simplest approach for

accessing specic AI capabilities with a minimum knowledge of how they

work technically.

e ability to tap into relatively complex, pre-trained algorithms is a boon

for anyone wanting to build AI capability into their applications. For exam-

ple, if you want to build an AI application that has some NLU capability

(a chatbot, for example) then you could use Amazon’s Lex algorithm,

Microsoft’s Linguistic Analysis or Google’s (wonderfully named) Parsey

McParseFace. Each of these is a simple Application Programming Interface

AI andCloud

(API), which means that they can be ‘called’ by sending them specic data.

ey will then return a result to you, which can be read by your application.

One of the interesting things about these services is that they are either free

or cheap. Microsoft’s Language Understanding Intelligent Service (LUIS)oer-

ing is currently based on a threshold of API calls per month—below this

threshold it is free to use, and every thousand calls above that is charged at just

a few cents. Other algorithms are charged as a monthly subscription.

Many businesses today are taking advantage of Cloud AI rather than build-

ing their own capabilities. For example: a brewer in Oregon, USA, is using

Cloud AI to control their brewing processes; a public TV company uses it to

identify and tag people that appear on its programs; schools are using it to

predict student churn and an FMCG company uses it to analyse job

applications.

As I’ve discussed in Chap. 2, one of the motivations for these companies to

oer their AI technologies for virtually nothing is that they get access to more

and more data, which in many ways is the currency of AI.But despite the

‘greedy corporate’ overtones, Cloud AI does have the feeling of democratisa-

tion about it, where more and more people have simple and cheap access to

these very clever technologies.

AI andRobotic Process Automation

Robotic Process Automation (or RPA) describes a relatively new type of soft-

ware that replicates the transactional, rules-based work that a human being

might do.

For clarity, it is important to dierentiate between RPA and the traditional

IT systems. RPA—at its most basic level—utilises technology to replace a

series of human actions (which is where the ‘robot’ terminology comes in).

Correspondingly, not all technologies provide automation, and replacing a

single human action with technology (e.g. a mathematical equation in a

spreadsheet) is not considered RPA.Similarly, much automation is already

embedded into software systems (e.g. linking customer information across

nance and procurement functions), but since it is part of the normal feature

functionality of a system, it is generally not considered as RPA, but simply a

more powerful system(s).

In an ideal world, all transactional work processes would be carried out

by large, all-encompassing IT systems, without a single human being

involved. In reality, whilst many systems can automate a large part of spe-

cic processes and functions, they tend to be siloed or only deal with one

4 Associated Technologies

part of the end-to- end process (think of an online loan application process

that will have to get data from, and input data to, a web browser, a CRM

system, a credit checking system, a nance system, a KYC system, an address

look-up system and probably one or two spreadsheets). In addition, many

businesses now have multiple systems that have been acquired as point solu-

tions or simply through numerous mergers and acquisitions. e default

‘integration’ across all of these systems, tying the whole end-to-end process

together, has traditionally been the human being. More often than not,

these human beings are part of an outsourced service.

Robotic Process Automation can replace nearly all the transactional work

that the human does, at a much lower cost (as much as 50% lower). e RPA

systems replicate (using simple process mapping tools) the rules-based work

that the human does (i.e. interfacing at the ‘presentation layer’) which means

that there is no need to change any of the underlying systems. A single process

could be automated and be delivering value within a matter of weeks. e

‘robot’ can then continue processing that activity 24hours a day, 7days a

week, 52weeks a year if required, with every action being completely audit-

able. If the process changes, the robot only needs to be retrained once, rather

than having to retrain a complete team of people.

So, to give a simple example, if a law rm is managing a property portfolio

on behalf of a client, they would be expected to carry out Land Registry

checks at some point. is is commonly a paralegal role that might involve

the person getting a request from a lawyer, or from the client directly, proba-

bly via a template, an email or a workow system. e person would read the

relevant information o the form, log into the Land Registry site, enter the

information into the site and read the results that came back from the search.

ey would then transpose that information back onto the form and respond

to the initial request. It is possible for this wholeprocess to be handled by a

software ‘robot’ without the need for any human intervention.

Although this is a very simple example, it does demonstrate some of the

benets of RPA:

• the cost of the robot is a fraction of the cost of the human (between a third

and a tenth);

• the robot works in exactly the same way as the human would, so no IT or

process changes are required;

• once trained, the robot will do the process exactly the same way 100% of

the time;

• every step that the robot takes is logged, providing full auditability;

AI andRobotic Process Automation

• the robot can carry out the process in the middle of the night or over a

weekend if necessary and

• the robot will never be sick, need a holiday or ask for a pay rise.

What this means is that wherever there are processes that are rules-based,

repeatable and use (or could use) IT systems, the person doing that process

can be replaced by a software robot. Some further examples of processes that

can be automated are:

• Employee on-boarding

• Invoice processing

• Payments

• Conveyancing processing

• Benet entitlement checks

• IT Service Desk requests

ese are just a small sample of the sorts of processes that can be automated

through RPA. Any comprehensive review of the processes carried out in a

back oce environment can identify a large number of candidates for auto-

mation. Some examples of where RPA has demonstrated particular benets

include:

• O2 replacing 45 oshore employees, costing a total of $1.35m a year, with

ten software robots, costing $100,000. Example processes included the

provisioning of new SIM cards. e telecoms rm then spent its savings of

$1.25m on hiring 12 new people to do more innovative work locally at its

headquarters.

• Barclays Bank have seen a £175 million perannum reduction in bad debt

provision and over 120 Full Time Equivalents (FTE)saved. Example pro-

cesses include:

– Automated Fraudulent Account Closure Process—Rapid closure of

compromised accounts

– Automated Branch Risk Monitoring Process—Collation and moni-

toring of branch network operational risk indicators

– Personal Loan Application Opening—Automation of processes for

new loan applications.

• Co-operative Banking Group has automated over 130 processes with

robotic automation including complex CHAPs processing, VISA

charge- back processing and many back oce processes to support sales and

general administration.

4 Associated Technologies

As well as delivering cost savings, RPA is having a huge impact on the way

companies are organising their resources: shared service centres are ripe for

large-scale automation, and outsourced processes are being brought back in-

house (on-shore) because the costs and risks are much more favourable after

automation. (is, by the way, is creating a huge threat to the viability of

Business Process Outsourcing (BPO) providers.

In implementing RPA, there are a number of aspects that need to be con-

sidered. Firstly, there is a need to understand whether the robots will be run-

ning ‘assisted’ or ‘unassisted’. An assisted robot generally works on parts of

processes and will be triggered to run by a human. In a contact centre, for

example, a customer service agent could take a call from a customer who

wishes to change their address. Once the call is complete, the agent can trigger

the robot to carry out the changes, which may be required across a number of

dierent systems. Meanwhile the human agent can get on with taking another

call. Unassisted robots work autonomously, triggered by a specic schedule

(e.g. every Monday morning at 8) or an alert (a backlog queue is over a certain

threshold). ey will generally cover whole processes and are therefore more

ecient than unassisted robots. Dierent RPA software packages are suited to

these two dierent scenarios in dierent ways.

Once the type of robot has been selected, the candidate processes for auto-

mation can be considered. ere are a number of characteristics that make for

a good candidate process:

• Rules-based, predictable, replicable—the process needs to be mapped and

congured in the RPA software; therefore, it has to be denable down to

key-stroke level.

• High volume, scalable—in most cases a process that is high volume (e.g.

happens many time a day) will be preferable because it will deliver a better

return on the investment.

• Relies on multiple systems—RPA comes into its own when the process has

to access a number of systems, as this is where people are generally employed

to integrate and move data around between them.

• Where poor quality results in high risk or cost—the exception to the high-

volume criteria is where a low-volume process might have a large risk asso-

ciated with it, such as in making payments, and where compliance and

accuracy are the main concerns.

ese criteria provide plenty of opportunities for RPA in most large busi-

nesses. But they do have some limitations. e reason that RPA software is of

particular interest to exponents of AI is that, although the RPA software is

AI andRobotic Process Automation

really clever in the way that it manages processes, the robots are eectively

‘dumb’; they will do exactly what they are told to do, with unwavering com-

pliance. In many situations that is a good thing, but there are situations where

there is ambiguity, either in the information coming in, or in the need to

make a judgement. is is where AI comes to the fore.

One of the biggest constraints of automating processes through RPA is that

the robots require structured data as an input. is could be, for example, a

spreadsheet, a web form or a database. e robot needs to know precisely

where the required data is, and if it’s not in the expected place then the process

will come abruptly to a halt. Articial Intelligence, and particularly the Search

capability, provides the ability to have unstructured, or semi-structured,

source data transformed into structured data that the robots can then

process.

Examples of semi-structured data would include invoices or remittance

advices—the information on the document is generally the same (supplier

name, date, address, value, VAT amount, etc.) but can vary considerably in

format and position on the page. As described in the previous chapter, AI

Search is able to extract the meta-data from the document and paste it into

the system-of-record even though every version of it may look slightly dier-

ent. Once in the main system, the robots can use the data for subsequent

automated processing.

e robots can even use the AI output as trigger for them to run. For

example, a legal contract can be considered a semi-structured document (it

has some common information such as name of parties, termination date,

limits of liability, etc.). An AI Search capability can extract this meta-data for

all of a business’s contracts so that they can manage their total risk portfolio.

RPA robots could be triggered if there was a change in regulation and all con-

tracts of a specic type (e.g. allthose under English and Welsh Law) needed

to be updated.

Another area where RPA falls down is where judgement is required as part

of the process. For example, in processing a loan application, much of the

initial stage (such as taking the information from the applicant’s web form

submission and populating the CRM system, the loan system and carrying

out a credit check) can be automated through RPA.But at some point there

needs to be a decision made whether to approve the loan or not. If the deci-

sion is relatively simple this can still be handled by RPA—it would be a case

of applying scores to specic criteria with certain weightings, and then

checking whether the total score was below or above a threshold. But for more

complex decisions, or where it seems like ‘judgement’ is required, then the AI

prediction capability can be used. (is could either be through a Cognitive

Reasoning engine or using a Machine Learning approach.)

4 Associated Technologies

us, using a combination of RPA and AI allows many processes to be

automated end to end, which inherently provides greater eciency benets

than would partly automated ones.

Conversely, RPA can also help AI automation eorts. As mentioned above,

robots are very good at extracting and collating data from many dierent

sources. erefore, RPA can be used as a ‘data supplier’ for AI systems. is

could also include manipulation of the data (e.g. remapping elds) as well as

identifying any unusable (dirty) data.

So far I have mainly focused on automating business processes, but RPA

can also be used to automate IT processes as well. e concept for automating

IT is exactly the same as for business processes: replace human sta doing

rules-based work with software agents. Many of the tasks carried out by an IT

Service Desk can be automated; common examples include password resets

and provisioning of additional software on a user’s desktop. One user of RPA

reported a reduction from 6minutes to 50seconds for the average incident

execution time for their Service Desk.

RPA can also work autonomously on infrastructure components, triggered

by system alerts: a robot could, for example, reboot a server as soon as it

received an alert that the server was no longer responding. Just like business

process automation, the robots can access almost any other system, and there

will be no disruption or changes required to those underlying systems. RPA in

this scenario can be considered a ‘meta-manager’ that sits across all the moni-

toring and management systems.

For IT automation, AI can enhance the capabilities of RPA. By using

Optimisation and Prediction capabilities they can be trained from run-books and

other sources, and will continue to learn from ‘watching’ human engineers. e

AI systems can also be used to proactively monitor the IT environment and its

current state in order to identify trends as well as any changes to the environment

(a new virtual server, for example) and then adjust their plans accordingly.

Companies that have implemented a combination of RPA and AI solutions

have seen, for example, 56% of incidents being resolved without any human

intervention, and a 60% reduction in the meantime to resolution.

So, RPA provides a useful complementary technology to AI.In addition to

AI enabling more processes, and more of the process, to be automated, RPA also

aids the data collation eorts that AI often needs. e two technologies also

work well together when there are larger transformational objectives in mind.

Enabling a self-service capability in a business can be a good way to improve

customer service at the same time as reducing costs. is could be achieved

through a combination of AI systems managing the front-end customer engage-

ment (e.g.through chatbots) and RPA managing the back-end processes.

AI andRobotic Process Automation

AI andRobotics

One of the rst practical applications of AI was in a physical robot called

SHAKEY, which was designed and built by Stanford Research Institute between

1966 and 1972. SHAKEY used computer vision and NLP to receive instruc-

tions which it could then break down into discrete tasks and move around a

room to complete the objective it had been set. Although we would now see it

as very rudimentary, it was, at the time, at the forefront of AI research.

Today we have robot vacuum cleaners costing a few hundred pounds that

can clean your house autonomously, and, although they can’t respond to voice

commands, this would be relatively easy to implement. Other examples of

physical robots that use AI are

• Autonomous vehicles—driverless cars and trucks use AI to interpret all the

information coming in from the vehicle’s sensors (such as using computer

vision to interpret the incoming LIDAR data, which is like RADAR but for

light) and then plan the appropriate actions to take. All of this needs to be

done in real time to ensure that the vehicle reacts quickly enough.

• Manufacturing robots—modern robots are much safer and easier to train

because they have AI embedded in them. Baxter, a robot designed by

Rodney Brooks’s Reink Robotics, can work on a production line without

a cage because it has the ability to immediately stop if it is going to hit some-

one or something. It can be trained by simply moving the arms and body in

the required series of actions, rather than having to program each one.

• Care-bots—the use of robots to supplement or replace the human care of

sick or vulnerable people is a controversial one, but they can have some posi-

tive benets. ere are robots that help care for the elderly, either by provid-

ing ‘company’ (through Speech Recognition and NLU) or support through,

for example, helping them remember things (Optimisation). Other

AI-powered robots that are used in the medical eld include the use of tele-

presence robots—these are mobile pods that can travel around hospitals but

are connected via video and sound to human doctors at a remote location.

• Service robots—Some retailers are starting to use mobile robots to greet

and serve customers. As with the hospital telepresence robots described

above, these are mobile units with computer vision, speech recognition,

NLU and optimisation capabilities built in. As well as being able to serve

customers in a shop, dierent versions of the robots can take the role of

waiters in restaurants or concierges in hotels. ere are also interesting

examples where robots are learning skills through reading material on the

4 Associated Technologies

internet—there is one case where a robot has learnt to make pancakes by

reading articles on WikiHow.

• Swarm robots—these are a specic eld of robotics where many small

machines work collaboratively together. ey rely heavily on the AI

Optimisation capability, constantly evaluating the next best move for each

of the swarm robots so that they can achieve the shared goal. Generally

ground-based, but can be aerial or water-based, they are usually used in

environments that are dicult for humans to work in, such as disaster res-

cue missions or, more controversially, in warfare (think autonomous

armies). Autonomous vehicles will also be able to exploit swarm

intelligence.

AI is also being used in human-like ways to help robots learn. Researchers

have developed an approach where a humanoid robot learns to stand itself up

by ‘imagining’ what it is like to stand up. Essentially it will run a series of

simulations using a DNN, and then use a second system to analyse the feed-

back from the various sensors as it actually tries to stand up.

Cognitive Robotics, therefore, can be considered the physical embodiment

of AI.Using input data from many dierent types of sensors the robot uses a

combination of the Speech Recognition, Image Recognition, NLU and

Optimisation capabilities to determine the most appropriate response or

action. And, because it is AI, the system can self-learn, becoming more eec-

tive the more it is used.

Of course, physical cognitive robots also stir in us the fear that they will even-

tually be able to ‘take over’. Software-based AI is not going to be a risk to the

human race if it is stuck on a server; we can always pull the plug out. But physical

robots could potentially have the ability to overpower us, especially if they are

able to build better versions of themselves. ere are already examples of cogni-

tive robots being able to do this, but I refer you back to my earlier comments in

the section on AI Understanding about the distinction between Articial Narrow

Intelligence and Articial General Intelligence. ere is, of course, an existential

risk there; it’s just that it won’t need to concern us for a very long time.

AI andtheIoT

eInternet of ings, or IoT,refers to simple physical devices that are con-

nected to the internet. IoT devices include webcams, smart lights, smart ther-

mostats, wearables and environmental sensors. Many people believe that IoT

is, along with AI, one of the major technology trends of the decade.

AI andtheIoT

ere are billions of IoT devices in the world today, each one generating

data or reacting to data, eectively creating and consuming big data on a

grand scale, which is why AI has such a symbiotic relationship with the IoT.

IoT devices are being used in business today to

• manage preventative maintenance programs by analysing data from sensors

embedded into the assets (e.g. escalators, elevators and lighting)

• manage supply chains by monitoring movement of products

• conserve energy and water usage of machines by analysing and predicting

demand (smart meters, which monitor energy usage every 15minutes, are

already common in many homes)

• improve customer experience by providing personalised content based on

IoT data

• increase crop yields by analysing eld sensors to provide precise feeding

and watering programs

• alleviate parking problems by matching empty spaces with cars and their

drivers

• keep us tter by monitoring and analysing our steps and exercise patterns

through wearable devices

One of the headline successes of ‘IoT plus AI’ was when Google announced

that they had been able to reduce the energy used for cooling in one of their

data centres by 40%. By using DeepMind’s AI technology, they were able to

analyse data from many dierent types of sensors (such as thermometers,

server fan speeds, and even whether windows were open or not), precisely

predicting demand so that they could instruct the various machines to work

at optimum levels.

Another area where IoT and AI are key enablers is in the development of

Smart Cities. IoT devices are used to track and extract data for transport,

waste management, law enforcement and energy usage. is data is analysed

and turned into useful information by AI Clustering, Optimisation and

Prediction systems, and then made available to the relevant authorities, the

city’s citizens and other machines. For example, sensors in street lights and

urban furniture are able to measure footfall, noise levels and air pollution—

this data is then used to prioritise the delivery of other services.

e biggest challenge to the proliferation of the IoT is the poor security

that has been associated with it. Many devices do not have the ability to

change passwords, and have only basic authentication. ere have been recent

cases of baby monitors allowing strangers to monitor their camera feed,

internet- connected cars’ entertainment systems and central locking systems

4 Associated Technologies

being taken over by hackers, and, probably most worrying of all, medical

devices being compromised so that a hacker could send fatal doses of medi-

cines to drug infusion pumps. e good news is that these high-prole cases

has meant that IoT security is very high on many technology company’s agen-

das and there is now plenty of activity to x all the issues. But if you are think-

ing of implementing an IoT strategy, do keep security at the top of your

considerations.

As more and more IoT devices are used (it is estimated that there will be 50

billion devices by 2020), there will be more need for AI to make sense of all

the data that is generated by them. e analysis that can be carried out can

then be actioned by other IoT devices such as actuators and lights. As in the

Smart Cities example, the real value will be the sharing and collaboration of

this data across organisations, people and other machines.

AI andCrowd Sourcing

Sometimes AI simply isn’t up to the job. Sometimes you will need to pull

humans into the loop to help complete the process.

A classic example of when this sort of situation can arise is when the source

documents for an automated process are handwritten. We already know that AI

is very capable of extracting structured data out of unstructured documents but

this really applies only when the unstructured documents are in an electronic

format in the rst place. Optical Character Recognition is able to take typed

documents such as PDFs and convert them into an electronic format, but if the

source is handwritten the challenge becomes a whole lot more dicult.

One solution to this is to use a Crowd Sourcing service. Crowd sourcing is

where large numbers of people are engaged to carry out small parts of a pro-

cess (‘micro-tasks’). e engagement model is usually via the Internet, with

each person paid a specic rate for every time they complete one of the

micro-tasks.

In the example above, one document can be split into many dierent tasks

so that one person is sent the First Name to read, another is sent the Last

Name and a third the Social Security Number. Each person will look at the

image of the handwriting and respond with the text that it represents. Because

each person only sees a small part of the information, condentiality is main-

tained. To increase accuracy, a single image can be sent to a number of people

so that only the most common answer is selected. Specialised software is used

to manage the interface between the customer and the crowd, including the

splitting of the documents into smaller parts.

AI andCrowd Sourcing

A second role for crowd sourcing in the AI space is to provide additional

capability when the AI is not condent enough of its answers. is might be

because the problem is too complex, or it hasn’t seen that particular problem

before (Fig.4.1).

In this case, the AI will send the problem to a person to answer. For exam-

ple, some AI systems are used to moderate oensive images on social media

sites. If the AI is unsure whether a particular image is oensive or not, it will

ask a human for their opinion. is approach is usually called ‘Human In e

Loop’, or HITL.

An enhanced version of HITL is where the input from the human is used

to actively train the AI system. So, in the oensive image example, the human’s

choice (oensive or inoensive) would be sent back to the AI so that it can

improve its learning and perform better in the future (Fig.4.2).

A third use of crowd sourcing with AI is in the development of the training

data. As I discussed in the Technology Overview section of Chap. 1, super-

vised learning approaches require data sets that are appropriately tagged (dog

pictures tagged as dogs, cat pictures tagged as cats, etc.). Because of the large

data sets that are required, the job of tagging all these data points is very labo-

rious. Crowd sourcing can be used to satisfy that requirement. Google itself

pays for tens of millions of man-hours collecting and labelling data that they

feed into their AI algorithms (Fig.4.3).

e most popular general crowd sourcing site is Mechanical Turk (owned

by Amazon) but others, such as Crowd Flower, that focus on supporting the

AI community, are also available. Some also have an Impact Sourcing angle as

Artificial

IntelligenceOutput

Confident?

Human

Intervention

Yes

Fig. 4.1 Human in the loop

4 Associated Technologies

Artificial

IntelligenceOutput

Confident?

Human

Intervention

Yes

Active Learning

Fig. 4.2 Training through a human in the loop

Tagging By

Humans

Artificial

Intelligence

Training

Fig. 4.3 Crowd-sourced data training

well, where the people doing the work are disadvantaged in some way and this

work gives them new opportunities.

So, despite the amazing things that AI can do, at some point it will proba-

bly still rely on people to do part of the job, whether that is training it or

helping it make decisions.

AI andCrowd Sourcing

The Established AI Vendor’s View

is is an extract from an interview with Andrew Anderson, CEO of Celaton,

a leading AI software vendor.

AB What got you into the world of AI in the rst place?

AA e story began in 2002. I sold my previous software company to a much

larger company that pioneered the delivery of software as a service, at that

time they were described as an ‘application service provider’. As the VP of

product development, we went on to acquire, license and develop lots of

software applications that solved many dierent challenges faced by organ-

isations, all delivered as a service. However, despite access to all this tech-

nology we still couldn’t solve the challenge of dealing with unstructured,

varied (human created) data that owed into organisations every day. It

remained a manual, labour- intensive task.

I saw an opportunity to create a solution to the challenge and so in

2004 I bought my company back, along with my development team and

set about trying to build an ‘as a service’ platform that could automate the

processing of unstructured, varied data. My optimistic view then hoped

that we would have a solution built within a year. e reality was that it

took over six years to create a platform that had the ability to learn, and

therefore enable us to describe what we had created as ‘articially intelli-

gent’. In fact, it wasn’t until we demonstrated it to analysts that they told

us what we had created.

We spent a few years (2011–2013) trying dierent approaches until we

realised that the greatest value we deliver was to large, ambitious organisa-

tions who deal with demanding consumers.

AB Why do you think AI is being so talked about right now?

AA AI is being talked about more than ever because it promises so much.

Whether those promises are realised is yet to be seen. It’s like a wonder

drug that has huge potential but the enthusiasm needs to be tempered

with a little reality.

What is emerging now are specialist AIs that are good at specic tasks

rather than general-purpose AIs that are good at everything. Whilst there

are many case studies where specic or narrow AI is delivering real bene-

ts this is being mixed up with noise about general AI.

ere’s sanity in numbers and it takes time and eort to educate and

convince enough organisations that they should try something but each

new client means a new case study and each case study leads to new cus-

tomers. ese are the realities behind the hype, but the media often prefer

to hear the more exciting stories of potential rather than reality.

4 Associated Technologies

Automation has been around since the wheel was invented and the

world has continued to invent and innovate and that’s why we see this

continuous improvement in technology. e increased power of technol-

ogy enables it to achieve outcomes that were previously out of reach.

Automation of any kind is also accelerating and with the emerging of

AI it must seem that everything can be automated. What was previously

considered the domain of human eort is now being eroded and this cre-

ates negative media.

In summary, I think it’s being talked about more for its potential than

its reality, although the reality (i.e. the case studies) are helping to fuel the

future potential because people can actually see the future emerging.

AB What value do your customers get out of your software?

AA ere’s a dierent answer depending on the customer challenge. By

understanding what customers are saying, then reacting and responding

to them enables our customers to deliver better service faster, with fewer

people. In summary, it enables them to achieve competitive advantage,

compliance and improve their nancial performance.

AB What do customers or potential customers need to focus on if they are

going to get the maximum value from AI?

AA I think the key is not to focus on the technology but to understand the

problem you want to solve. Many people (within organisations) have

trouble understanding their problem and therefore they struggle to iden-

tify the technology that might help to solve it.

is is where experienced consultancy is important. Understand the

problem, select and apply the most appropriate technology and then share

the story.

AI is often considered to be the solution to all problems like a magic

drug. e reality is there is no magic drug but there are lots of drugs that

solve dierent problems. e key is to talk with someone who can help to

understand the problem and prescribe the right ‘drug’.

AB Do you think the current hype is sustainable?

AA I don’t think that we are witnessing anything dierent with this hype. e

industry does tend to get ahead of itself because hype serves it well, creat-

ing awareness that helps to capture the interest of customers. ere’s san-

ity in numbers.

e dierence this time is that technology innovation is moving

quicker and it’s not so long after the thought has been considered that the

invention can be demonstrated.

The Established AI Vendor’s View

Regardless of how fast things move, it’s the human that is the limiting

factor. e bigger the problem that it solves, the more likely it is to be

adopted by humans and therefore be successful.

AB How do you see the market developing over the next 5years or so?

AA Hype will lead to reality, but by solving problems that really exist. ere

are a couple of areas that are particularly relevant:

• Natural interfaces. We’re seeing this already with the likes of Amazon

Echo and IoT appliances. ere is some way to go but they will become

more natural and to such an extent that humans will have to think less

about how they communicate with technology.

• Broader AI.We’re currently seeing specialist AIs that are good at doing

specic things– these technologies will consolidate into broader solu-

tions that are able to do lots of things well because they’re made up of

lots of vertical AIs.

And I particularly see some of the greatest and most profound use of

AI in medicine, energy and, subject to world events, in the military too.

4 Associated Technologies

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_5

AI inAction

Introduction

Up to this point in the book I have been pretty theoretical, explaining the capa-

bilities of AI and the types of technologies it requires to work. In this chapter I

change focus to look at how AI is actually being used in businesses—real use

cases of AI adding value and changing the way companies do business.

I have split the chapter into dierent themes which look at specic aspects

of business and the ways that AI adds value: enhancing customer service,

optimising processes and generating insights. ere are overlaps between each

of these areas (there are, for example, insights you can generate about your

customers) but it gives a general, useful framework to describe how AI is

being used in business.

In the chapter following this I describe the dierent types of benets that

should be considered in an AI Business Case. ese can be broadly mapped

against the themes:

• Enhancing customer service leads to revenue generation and customer

satisfaction

• Optimising processes leads to cost reduction, cost avoidance and

compliance

• Generating insights leads to risk mitigation, loss mitigation and revenue

leakage mitigation

Each of these themes will also draw upon dierent capabilities from the AI

Framework. All will use at least one of these capabilities, whilst others will

exploit a few.

I have tried to take examples from a wide range of industries, and have

deliberately not grouped the case studies into specic sectors. is is so you

are not tempted to skip over sectors that are not immediately relevant to

you—I am a rm believer that very dierent industry sectors can still learn

from each other. Even if you work in the retail sector, for example, you may

still get a spark of an idea from something in the utilities sector.

How AI Is Enhancing Customer Service

e ‘front oce’ is one of the more active areas when it comes to implement-

ing AI.is is because there is generally plenty of customer data available to

work o, but it is also down to the proliferation of chatbots.

Chatbots come in all shapes and sizes, which is a rather polite way of say-

ing that there are really good chatbots but also very bad ones. Chatbots, which

aim to have a natural conversation with a customer through a typed interface,

use NLU as their key AI capability. is means that, in theory, a customer

who wants to change their address on the system (this is their ‘intent’ in

AI-speak) can ask the chatbot in any way that they want (‘I’m moving to a

new house’, ‘I’ve got a new address’, ‘I’m not living at the same place any

more’, ‘My postcode has changed’, etc.) and it will still understand the under-

lying intent.

In reality, this is a big challenge for NLU, and most chatbots use a ‘diction-

ary’ of equivalent phrases to refer to (e.g. if the input is ‘I’ve got a new address’

or ‘I’m moving to a new house’, then the intent is ‘Customer wants to change

their address’). is, of course adds to the complexity of designing the chat-

bot, as every alternative phrase has to be identied and entered.

Some chatbots make extensive use of multiple-choice questions. So, instead

of relying on understanding what the customer has typed, the chatbot will ask

a question with a limited number of answers (e.g. Yes/No, Ask for a Balance/

Make a Payment/Change Your Address, etc.). is can be more ecient and

accurate but doesn’t make for a natural conversation.

Another challenge for chatbots is how they move through the conversation.

e simplest and most common approach is a decision tree, where each ques-

tion will branch o to a new question depending on the answer. If the process

being replicated is complex, this can lead to an over-complicated and burden-

some decision tree. A better approach here is to have a cognitive reasoning

engine do all of the ‘thinking’ whilst the chatbot gets on with the conversa-

tion. is provides much more exibility in how the chatbot deals with the

ow of the conversation. Cognitive reasoning systems were described in the

Optimisation section of Chap. 3.

5 AI inAction

e purest AI approach to chatbots is to train them on thousands of

human-to-human chat conversations, where each interaction has been tagged

with the intent and whether it was productive or not. ese chatbot systems

eectively learn the knowledge map (a.k.a. ontology) through those historical

interactions. e challenge with these systems is that there needs to be plenty

of training data available, and they tend to very expensive to implement.

With chatbots, it is very much ‘horses for courses’. A simple, ‘free’ chatbot

system will be ne for very simple and non-critical interactions with custom-

ers, but because of the reputational risk involved, implementing chatbots is

worth doing only if you do it right. e better the chatbot system, the better

it will cope with the challenge.

In 2017 Royal Bank of Scotland introduced a chatbot to help answer a

limited number of questions from customers. e chatbot, called ‘Luvo’, uses

IBM Watson’s Conversation capability to interact with customers who are

using the bank’s website or app. It was trialled for nearly a year with internal

sta who manage relationships with SMEs before being released to a small

number of external customers. At the time of writing, it is able to answer just

ten dened questions, such as ‘I’ve lost my bank card’, ‘I’ve locked my PIN’

and ‘I’d like to order a card-reader’.

Luvo demonstrates the cautious approach that enterprises are taking to

implementing chatbots. Over time though, as the system learns from each

interaction it has, the bank will allow it to handle more complex questions,

build in greater personalisation and use predictive analytics to identify issues

for customers and then to recommend the most appropriate action. e key

objective is to free up more time for the human customer service agents to

handle the trickier questions that customers may have.

In a similar two-step implementation, SEB, one of Sweden’s largest banks,

deployed IPsoft’s Amelia software rst in their internal IT Service Desk, and

then to their one million customers. During the rst three weeks of the trial,

the chatbot-cum-avatar helped to answer the sta’s IT issues, resolving around

50% of the 4000 queries without human interaction. During 2017, it was

rolled out to help manage interactions with the bank’s customers (and named

‘Aida’). ree processes were initially chosen as candidates: providing infor-

mation on how to become a customer, ordering an electronic ID and explain-

ing how to do cross-border payments.

Although Amelia learns from historical interactions and uses sentiment analy-

sis, it is also able to follow dened workow paths to ensure compliance with

banking regulations. Whereas most chatbots can be described as probabilistic,

providing likely results, Amelia can be considered a deterministic system because,

once it has worked out the intent, it can, where possible, carry out the required

How AI Is Enhancing Customer Service

actions on the enterprise systems (in a similar way that RPA tools would). IPsoft

is a US-based company, and this was their rst non- English deployment of their

solution.

From an infrastructure point of view, SEB decided to install the Amelia tech-

nology on their own servers rather than as a cloud solution. is was due to

concerns over the compliance and legal concerns that a cloud deployment raises.

For IPsoft’s nancial services customers, this is the most common approach.

e deployment of Amelia is very much aligned with the bank’s overall

strategy, which includes the line “SEB will focus on providing a leading cus-

tomer experience, investing in digital interfaces and automated processes”.

e US ower delivery service 1-800-Flowers deployed a simple chatbot

that allowed customers to place orders through Facebook Messenger. It is a

linear, decision tree system with an NLU chat interface that took around three

months to develop and test. is makes it rather constrained in what it can

do, but additional functionality and complexity is being rolled out. Following

the initial two months of operation, 70% of the orders being placed through

the chatbot were from new customers, and were dominated by ‘millennials’

who tend to already be heavy users of Facebook Messenger.

As well as taking orders, the chatbot can direct a customer to a human

agent, of which there may be up to 3,500 at any one time. ey have also

implemented an integration with Amazon Alexa, and a concierge service that

is powered by IBM Watson. Together, these digital initiatives have attracted

‘tens of thousands’ of new customers to the brand. ey also provide up-to-

the-minute behaviour data for the company which can then inuence real-

time marketing activity such as promotions.

Another company that has used IBM Watson to enhance customer engage-

ment is Staples, the stationery retailer. ey have implemented a range of

dierent ways for people to buy their products as easily as possible, including

email, Slack, mobile app and (honestly) a big red button. e button is simi-

lar to Amazon’s Alexa in that it can understand voice commands (although it

does need a physical press to activate and deactivate it). e mobile app can

also understand voice commands as well as being able to identify products

from photographs taken. All these channels make the buying process as

frictionless as possible for the customer, and therefore has a direct and positive

impact on revenue for the retailer.

As well as chatbots, recommendation engines are another common AI tech-

nology that is used to enhance customer service (and, of course, drive revenue).

Amazon and Netix have the most well-known recommendation engines, and

these are deeply embedded in the normal workow of how the customer engages

with the companies. All the required data—the individual customer’s buying

5 AI inAction

and browsing behaviours and the historical data of all the other customers’

behaviours—are available through those normal interactions; that is, there is no

additional work that the customer has to do to enrich the data set.

In some cases, the recommendation engines will require additional informa-

tion from the customer and/or the business in order to work eectively. North

Face, a clothing retailer, has implemented a recommendation engine for their

customers who want to buy jackets. Based on IBM Watson this solution, called

XPS, uses a chatbot interface to ask aseries of rening questions so that it can

match the customers’ requirements with the product line. According to North

Face, 60% of the users clicked through to the recommended product.

Another clothing retailer, Stitch Fix, uses a slightly dierent approach by

deliberately including humans in the loop. Its business model involves recom-

mending new clothes to their customers based on a selection of information

and data that the customer provides (measurements, style survey results,

Pinterest boards, etc.). All this structured and unstructured data is digested,

interpreted and collated by the AI solution, which sends the summary, plus

anything that is more nuanced (such as free-form notes written by the cus-

tomer) to one of the company’s 2,800 work-from-home specialist human

agents, who then select ve pieces of clothing for the customer to try.

is is a good example of where the AI is augmenting the skills and experi-

ence of the human sta, making them better at their jobs as well as being

more ecient. Having humans in the loop (HITL is the acronym for this)

also makes experimenting easier, as any errors can quickly be corrected by the

sta. To test for bias, the system varies the amount and type of data that it

shows to a stylist—it can then determine how much inuence a particular

feature, say a picture of the customer or their address, can make on the stylist’s

decisions. On top of all this, the data that they gather about all of their cus-

tomers can also be used to predict (and inuence?) general fashion trends.

Other customer service AI solutions don’t rely on chatbots or recommen-

dation engines to provide benets. Clydesdale and Yorkshire Banking

Group (CYBG) is a medium-size bank in the United Kingdom, having to

compete with the ‘Big Four’ of Barclays, HSBC, Lloyds and RBS.eir digi-

tal strategy includes a new current account, savings account and app package,

called ‘B’. It uses AI to help manage the customer’s money: it allows you to

open account, and, once opened, will learn the patterns of usage so that it can

predict if you might run out of funds in your accounts, and suggest ways to

avoid unnecessary bank charges. e bank claims that an account can be

opened in 11minutes. Clearly, customers avoiding bank charges will result in

lower revenue, but the hope is that it will attract enough new customers that

this revenue loss will easily be outweighed by the benets of having the addi-

tional funds.

How AI Is Enhancing Customer Service

Virgin Trains ‘delay/repay’ process has been automated through the appli-

cation of AI.After implementing Celaton’s inSTREAM AI software to cate-

gorise inbound emails (see next section) the train operating company used the

same software to provide a human-free interface for customers to automati-

cally claim refunds for delayed trains.

e Asian life insurance provider AIA has implemented a wide range of AI

initiatives that all impact how they engage with their customers, including

insight generation from prospective customers, enhanced assessment of cus-

tomer needs, 24×7 chatbots for online enquiries, inbound call handling by

NLU-based systems, enhanced compliance of sales, personalised pricing,

dynamic underwriting and an augmented advice and recommendation engine.

Some companies are building AI into the core of their customer applica-

tions. Under Armour, a sports clothing company that has a portfolio of t-

ness apps, uses AI in one of those apps to provide training programs and

recommendations to the users. e AI takes data from a variety of sources,

including the users’ other apps, nutritional databases, physiological data,

behavioural data and results from other users with similar proles and objec-

tives. It then provides personalised nutrition and training advice that also

takes into account the time of day and the weather.

Other examples of where AI can enhance customer service are: optimising

the pricing of time-critical products and services such as event ticketing; opti-

mising the scheduling of real-time services such as delivery and departure

times; creating personalised loyalty programs, promotional oers and nan-

cial products; identifying candidate patients for drug trials and predicting

health prognoses and recommending treatments for patients.

ere is clearly a balance between using AI to serve customers better, and using

AI to gather information about customers. In some of the cases I have described,

the same AI capability will do both. As I have reiterated a number of times, for

anyone looking to implement a ‘front oce’ AI solution, it is important to rst

understand your objectives. e danger of trying to ask your AI to do too much

is that you compromise on one objective or the other. I discuss approaches and

examples of how to generate customer insights later in this chapter.

How AI Is Optimising Processes

If the previous section was all about enhancing the front oce, then this sec-

tion focuses on what happens behind the scenes in the back oce operations.

e benets here are centred around, although not exclusively, reducing cost

(directly or through avoidance) and improving compliance.

5 AI inAction

One of the key capabilities exploited in the back oce is being able to

transform unstructured data into structured data, that is, from the Image

Recognition, Voice Recognition and Search capabilities.

Tesco have implemented a number of AI-powered solutions to improve the

productivity of their stores. ey are using image recognition systems to iden-

tify empty shelves on stores, called ‘gap scanning’. ey are experimenting

with physical robots that travel down the aisles during quiet periods to lm

the shelves so that they can measure stock availability and inform sta to

replenish them where necessary. Not only does this save the time of the sta

(who would normally have to use a diagram on a piece of card as the template)

but it also reduces revenue loss due to stock unavailability.

For their home delivery service Tesco have implemented optimisation sys-

tems that minimise the distance the picker walks around the store collecting

the goods, and also a system that maximises the productivity of the delivery

vans through eective route planning and scheduling. Interestingly, in a meta-

example, they have used AI to help place the many products into the neces-

sary taxonomy that AI systems use for classication.

AIA, the Asian insurance provider I mentioned in the previous section,

have also implemented initiatives in their servicing and claim functions,

including service augmentation, investment management, medical second

opinions, risk management, fraud management, claims adjudication and

health and wellness advice to their customers. AIA have been very clear that

transforming their business requires end-to-end transformation with AI at

the core.

Insurance claims processing is a prime candidate for enhancement through

AI and RPA. Davies Group, a UK-based insurance rm that provides a claims

processing service to their own insurance customers, has automated the input

of unstructured and semi-structured data (incoming claims, correspondence,

complaints, underwriters’ reports, cheques and all other documents relating

to insurance claims) so that it is directed into the right systems and queues.

Using Celaton’s inSTREAM solution, a team of four people process around

3,000 claims documents per day, 25% of which are paper. e tool processes

the scanned and electronic documents automatically, identies claim infor-

mation and other meta-data, and pastes the output into databases and docu-

ment stores ready for processing by the claims handlers and systems (which

could, of course, be either humans or software robots). It also adds service

meta-data so the performance of the process can be measured end to end.

Some documents can be processed without any human intervention, and oth-

ers need a glance from the human team to validate the AI’s decisions or ll in

missing details.

How AI Is Optimising Processes

AI can also be used in the claims process to identify fraud (by looking for

anomalous behaviours), to help agents decide whether to accept a claim or

not (using cognitive reasoning engines) and by using image recognition to

assess damage. Ageas, a Belgian non-life insurance rm, has deployed

Tractable, an AI image recognition software, to help it assess motor claims. By

scanning images of damage to cars, the software is able to assess the level of

damage, from repairable to being a write-o. rough the process, it also

helps identify fraudulent claims.

Image recognition has also been used in other industry sectors. Axon,

which used to be known as Taser, the manufacturer of police equipment, are

now using AI to tag the thousands of hours of video that is recorded from the

body cameras that they manufacture. e video recordings were previously

available to police forces on subscription but, due to the sheer volume of

recordings, they were rarely used as evidence. Now that the lm is being

tagged, the crime agencies will be able to automatically redact faces to protect

privacy, extract relevant information, identify objects and detect emotions on

people’s faces. e time saved, from having to manually write reports and tag

videos, will be huge, but the ability to make better use of the available evi-

dence will likely have a bigger impact. For Axon, it will encourage more police

forces to buy their body cams.

Data from video feeds has been used as the core of the business model of

Nexar, an Israeli-based technology company. ey are giving away for free

their dash cam app which helps drivers anticipate accidents and problems in

the road ahead. By using the data provided by the apps from their user base

(which is mandatory if you want to use the app) the system predicts the best

course of action, for example to brake hard if there is an accident up ahead.

is model relies heavily on having large amounts of data available, hence the

up-front giveaway. Rather controversially, the company can use the data for

‘any purpose’, including selling it to insurance companies and giving it to the

government. Whether this is a sustainable business model remains to be seen,

but it does demonstrate how business models are transforming to focus more

on the data than the service being provided to the customers.

A particularly worthwhile use of image recognition is in the medical

eld. Probably the most widely reported has been the use of IBM’s Watson

platform to analyse medical images, such as X-rays and MRI scans, so that

it can help identify and diagnose cancer cells. e real benet of this sys-

tem is that it can cross-reference what it sees with the patient’s (unstruc-

tured) medical records and thousands of other images that it has learnt

from. is approach is already spotting characteristics of tumours that

medical sta hadn’t seen—they are nding that peripheral aspects of the

5 AI inAction

tumour (e.g. those distributed around, rather than in, the tumour) have a

much greater inuence on predicting malignancy than at rst thought.

ese systems are currently working in two hospitals: UC San Diego

Health and Baptist Health South Florida. (See the section on Generating

Insights for more examples of how AI is helping ght cancer.)

Deutsche Bank are using the speech recognition technology to listen to

recordings of their sta dealing with clients, in order to improve eciency,

but, more importantly, ensure compliance to regulations. ese conversations

are transcribed by the AI and can be searched for specic contextual terms.

is used to be the job of the bank’s auditors, and would require listening to

hours of taped recordings, but the AI system can do the same thing in min-

utes. e bank is also using other AI capabilities to help it identify potential

customers based on the large amount of public information that is available

on people.

e legal sector is currently going through a big transition period as it starts

to adopt new technologies, including AI.Inherently risk averse, and never an

industry to change quickly, the legal sector is starting to feel the benet of AI

particularly through implementing Search and NLU technologies to help

make sense of contracts.

Legal contracts can be described as semi-structured documents—although

they follow some general rules (they usually includeparties to the contract,

start and end dates, a value, termination clauses, etc.), they are verbose and

variable. ere are now a number of software solutions on the market which,

together, work across the full contract lifecycle—some are able to search docu-

ment repositories to identify where contracts are; others can ‘read through’ the

contracts, identify specic clauses and extract the relevant meta- data (such as

the termination date and the limits of liability). Some have the capability to

compare the real contracts with a model precedent and show all instances

where there is a material dierence between the two. All of this allows large

enterprises and law rms to manage their contracts much more eectively,

especially from a risk point of view.

McKesson, a $179 billion healthcare services and information technology

organisation, uses Seal’s Discovery and Analytics platform to identify all of

their procurement and sourcing contracts across the business (they employ

70,000 people) and store the documents in a specic contract repository.

When in the repository, the contracts could be searched easily and quickly,

saving some sta hours per day. e meta-data from the contract analysis

allowed them to identify potential obligation risks as well as revenue opportu-

nities and savings from otherwise hidden unfavourable payment terms and

renewal terms.

How AI Is Optimising Processes

A Magic Circle law rm, Slaughter & May, adopted Luminance AI soft-

ware to help manage the hundreds of Merger and Acquisition (M&A) transac-

tions they do a year. is area is a particular challenge because of the complexity

involved (thousands of documents, many jurisdictions) and the intensity of

the work—the law rm was concerned that some of the junior lawyers who

were tasked with managing the M&A ‘data room’ (the contract repository cre-

ated specically for the deal) would eventually burn out. Luminance is used to

cluster, sort and rank all of the documents in the data room, with each docu-

ment assigned an anomaly score to show how it diers from the ideal model.

Whereas in normal due diligence exercises only around 10% of the documents

are analysed, due to the sheer volume, Luminance is able to analyse everything.

It takes about an hour for it to process 34,000 documents. Overall, it halves the

time taken for the whole document review process.

One of the benets of having the AI systems carry out ‘knowledge based’

works such as this is that it allows lawyers to focus on complex, higher-value

work. Pinsent Masons, a London-based law rm, designed and built in-

house a system, called TermFrame, which emulates the legal decision-making

workow. e system provides guidance to lawyers as they work through dif-

ferent types of matters, and connects them to relevant templates, documents

and precedents at the right time. Taking away much of the lower-level think-

ing that this requires means that the lawyers can spend more time on the

value-add tasks.

As well as calling on Search and NLU capabilities, companies are also using

NLG solutions to optimise some of their processes. Using Arria’s NLG solu-

tion, the UK Meteorological Oce can provide narratives that explain a

weather system and its development, with each one congured with dierent

audiences in mind. e Associated Press (AP), a news organisation, uses

Wordsmith to create publishable stories, written in the AP in-house style,

from companies’ earnings data. AP can produce 3,700 quarterly earnings sto-

ries which represents a 12-fold increase over its manual eorts.

Articial intelligence can also be used to enhance the IT department as well

(‘Physician, heal thyself’). ere are a number of AI capabilities that can be

deployed, both in the support function and for infrastructure management.

Generally, AI brings a number of important aspects to the IT function: the AI

systems can be trained from run-books and other sources; they will continue

to learn from ‘watching’ human engineers; they can proactively monitor the

environment and the current state; they can identify trends in system failures

and they are able to cope with the inherent variability of IT issues.

We have already seen how chatbots can support customer engagement, but

they can also be used with sta as well, particularly where there are a large

5 AI inAction

number of employees, or a large number of queries. Although chatbots can be

deployed on any type of service desk, including HR, they are most commonly

found in IT.

As in the SEB example discussed in the previous section, AI Service Desk

agents are able to receive customer enquiries and provide answers by under-

standing what a customer is looking for and asking the necessary questions to

clarify the issue. e more advanced systems will, if they cannot help the

customer themselves, raise the issue to a human agent, learning how to solve

the problem itself for future situations.

AI can also be used to manage a company’s IT infrastructure environment.

e AI systems tend to work as meta-managers, sitting above and connecting

all the various monitoring systems for the networks, servers, switches and so

on. ey include both proactive and predictive monitoring (using AI) and

execution of the necessary xes (using software robots). Key performance

indicators such as downtime can be signicantly improved using these sys-

tems. And, by tackling many of the mundane tasks, the systems free up the IT

engineers to focus on higher-value, innovative work.

TeliaSonera, a European telecoms operator, implemented IPCentre to

help manage its infrastructure of 20 million managed objects, including

12,000 servers, and have subsequently seen cost savings of 30%. A New York-

based investment rm used the same solution to help it x failed xed-

income securities trades due to system issues. Eighty per cent of the failed

trades are now xed without human intervention, and the average resolution

and x time has been reduced by 93% (from 47minutes to 4minutes). is

has resulted in a sta reduction of 35%.

Google, a company with many very large data centres, turned to its AI

subsidiary, DeepMind, to try and reduce the cost of powering these facilities.

e AI worked out the most ecient methods of cooling by analysing data

from sensors amongst the server racks, including information on aspects such

as temperatures and pump speeds. From the application of the AI algorithms,

Google was able to reduce cooling energy demand by 40%, and to reduce the

overall energy cost of the initial data centre by 15%. e system now controls

around 120 variables in the data centres including fans, cooling systems as

well as windows.

Another example of where AI can optimise processes includes the ability to

model real-world scenarios on computers, with the most obvious example being

the prediction of long-term weather conditions. But this modelling approach

has been used to test physical robotic systems so that the designers can make

adjustments to their robots virtually without the fear of them falling over and

breaking. Facebook have created a specic version of Minecraft, a virtual-world

How AI Is Optimising Processes

game, for this sort ofpurpose. AI designers can also use the environment to help

teach their AI algorithms how to navigate and interact with other agents.

In this section, we have seen examples of how AI can optimise the back oce

processes of retailers, banks, insurance rms, law rms and telecoms companies,

across a wide range of functions, including claims management, compliance and

IT.Other use cases for AI in optimising processes include: optimising logistics

and distribution of inventory in warehouses and stores; real-time allocation of

resources to manufacturing processes; real-time routing of aircraft, trucks and so

on; optimising the blend and timing of raw materials in rening and optimising

labour stang and resource allocation in order to reduce bottlenecks.

How AI Is Generating Insights

In the previous two sections I looked at how AI can enhance customer service

and optimise processes. But, in my mind, the biggest benets from AI are

when it can deliver insight. is is where new sources of value are created from

the data that already exists, enabling better, more consistent and faster deci-

sions to be made. AI can therefore enable a company to mitigate risks, reduce

unnecessary losses and minimise leakage of revenue.

One of the most eective uses of AI to date has been in the identication

of fraudulent activity in nancial services. e benet here is that there is

plenty of data to work from, especially in retail banking. PayPal process $235

billion in payments a year from four billion transactions by its more than 170

million customers. ey monitor the customer transactions in real time (i.e.

less than a second) to identify any potentially fraudulent patterns—these ‘fea-

tures’ (in AI-speak) have been identied from known patterns of previous

fraudulent transactions. According to PayPal, their fraud rate is just 0.32%,

compared to an industry average of 1.32%.

e American insurance provider USAA is similarly using AI to spot iden-

tity theft from its customers. It is able to identify patterns in behaviour that

don’t match the norm, even if it is happening for the rst time. Axa, another

insurance rm, has used Google’s TensorFlow to predict which of its custom-

ers are likely to cause a ‘large-loss’ car accident (one that requires a pay-out

greater than $1,000). Normally up to 10% of Axa’s customers cause a car

accident every year, but about 1% are ‘large-loss’. Knowing which customers

are more likely to be in that 1% means that the company can optimise the

pricing of those policies.

e Axa R&D team initially tried a Decision Tree approach (called Random

Forest) to model the data but were able to achieve only 40% prediction

accuracy. Applying deep learning methods on 70 dierent input variables,

5 AI inAction

they were able to increase this to 78%. At the time of writing, this project is

still in its proof-of-concept (PoC) phase but Axa are hoping to expand the

scope to include real-time pricing at point of sale and to make the system

more transparent to scrutiny.

Another company that is using AI to optimise pricing in real time is Rue

La La, an online fashion sample sales company who oer extremely limited-

time discounts on designer clothes and accessories. ey have used machine

learning to model historical lost sales in order to determine pricing and pre-

dict demand for products that it has never sold before. Interestingly, from the

analysis they discovered that sales do not decrease when prices are increased

for medium and high price point products. ey estimated that there was a

revenue increase of the test group of almost 10%.

Otto, a German online retailer, is using AI to minimise the number of

returns they get, which can cost the rm millions of euros a year. eir par-

ticular challenge was the fact that they knew that if they got the orders to their

customers within two days then they would be less likely to return them

(because it would be less probable for them to see the same product in another

store at a lower price). But, they also knew that their customers liked to get all

their purchases in one shipment, and, because Otto sources the clothes from

other brands this was not always easy to achieve.

Using a deep learning algorithm, Otto analysed around three billion his-

torical transactions with 20 variables so that it could predict what customers

would likely buy at least a week in advance. Otto claims that it can predict

what will be sold within 30days to an accuracy of 90%. is means it can

automate much of the purchasing by empowering the system to auto-order

around 200,000 items a month. eir surplus stock has reduced by a fth and

returns have gone down by more than two million items a year.

Goldman Sachs, the global investment bank, has implemented a range of

automation technologies to both improve decision-making processes and also

to reduce headcount. ey started by automating some of the simpler trades

that were done at the bank—in the year 2000 there were 600 equity traders

working in the NewYork headquarters; at the start of 2017 there were just

two. e average salary of an equity trader in the top 12 banks is $500,000,

so the savings have been signicant. e majority of the work is carried out by

automated trading systems that are supported by 200 engineers (one third, or

9,000, of Goldman Sachs employees are computer engineers).

To automate more complex trades such as currency and credit, cleverer

algorithms are required. Where Goldman Sachs has automated currency trad-

ing, they have found that one computer engineer can replace four traders.

ey are also looking at building a fully automated consumer lending plat-

form that will consolidate credit card balances. is is an innovation that has

How AI Is Generating Insights

been incubated internally at the bank’s New York oce by small ‘bubble’

teams. As Marty Chavez, the company’s deputy chief nancial ocer, says:

there is plenty of empty oce space to use up.

e UK-headquartered bank HSBC has been running ve proof-of-

concepts (PoCs) that will utilise Google’s AI capabilities. Because their 30

million or so customers are engaging online much more, their data store has

increased from 56 petabytes in 2014 to more than 100 petabytes in 2017.

is means they are now able to mine much more value and insight from it.

One of the PoCs is to detect possible money laundering activity. Just as

with PayPal and USAA, they are looking for anomalous patterns that they can

then investigate with the relevant agencies to track down the culprits. As well

as improving detection rates, using the AI software (from Ayasdi) means that

there are less false-positive cases that have to be investigated, which saves the

time of expensive resources. In the case of HSBC, they managed to reduce the

number of investigations by 20% without reducing the number of cases

referred for further scrutiny. HSBC are also carrying out risk assessments

using Monte Carlo simulations (these were described in the Optimisation

section in Chap. 3). From these they will be better able to understand their

trading positions and the associated risks.

Some of the more contentious uses of AI in business are in recruitment and

policing. e main contention is how bias can be (or even perceived to be)

built into the training data set, which is then propagated into the

decision- making process. In Chap. 8, I will discuss this challenge and the

potential mitigating approaches.

e Police Force of Durham, a county in the North East of England, have

started using an AI system to predict whether suspects should be kept in cus-

tody or not (in case they might reoend). In this case, there are lots of data

available, from previous cases of reoenders and non-reoenders over ve years,

that can be used to predict future reoenders. At the time of writing it would

seem that there still a number of issues with this approach, the biggest of which

is probably with the validity of the data—this comes only from the region’s own

records, so excludes any oending that the suspect may have done outside

Durham. Despite the data challenges, the system accurately forecast that a sus-

pect was a low risk for recidivism 98% of the time, and 88% for whether they

were a high risk. is shows how the system deliberately errs on the side of

caution so that it doesn’t release suspects if its condence is not high enough.

In recruitment, a number of rms are taking the plunge into using AI to

help short-list candidates (although there are only a few who will currently

admit to it). Alexander Mann, a Human Resource Outsourcing Provider,

initially automated some manual tasks such as interview scheduling and

5 AI inAction

authorising job oers. ey have recently introduced some AI software

(Joberate) to help them nd candidates that are good two-way matches with

the job requirement. e software analyses both the candidate’s CV and pub-

licly available social media feeds to create proles of the candidates.

But AI can mean more than just mitigating business risks. Insights from big

data analysis can also be used to help ght diseases, especially cancer. AI has

been applied to the molecular analysis of gene mutations across the whole

body—usually cancer treatment research relates to specic organs—which

means that treatments that have been developed for, say, breast cancer could be

used to treat colorectal cancer. rough this approach personalised treatments

become possible—statistically signicant meta-studies have looked at how

much benet there was in matching the molecular characteristics of the tumour

of a patient with their treatment. is matching resulted in tumours shrinking

by an average of 31% compared to 5% from a non-personalised approach.

AI is also being used to develop cancer drugs. A biotech rm, Berg, fed as

much data as it could gather on the biochemistry of cells into a supercom-

puter so that the AI could suggest a way of switching a cancerous cell back to

a healthy one. e results have so far been promising, leading to the develop-

ment of a new drug, and, of course, those results have been fed back into the

AI to further rene the model.

e quality of healthcare can also be monitored using AI.e Care Quality

Commission (CQC), the organisation that oversees the quality of healthcare

delivered across the United Kingdom, implemented a system to process large

numbers of textual documents and understand the opinions and emotions

expressed within them. e CQC is now able to manage the inux of reports

with fewer people and, most importantly, can apply a consistent scoring

methodology. e use of sentiment analysis (which was described in the NLU

section of Chap. 3) is also used in business-to-consumer companies, such as

Farfetch, a UK online retailer, to understand in near real time what their

customers think of their products and services. e companies are then able

to respond quickly and get a better understanding of their customers’ needs.

Other use cases for AI in optimising processes include: predicting failure

for jet engines; predicting the risk of churn for individual customers; predict-

ing localised sales and demand trends; assessing the credit risk of loan applica-

tions; predicting farming yields (using data from IoT sensors) and predicting

localised demand for energy.

From the above examples, it should be clear that AI has huge potential to

unearth hidden value from a company’s data, helping manage risks and make

better, more informed, decisions. ere are certainly challenges around the

lack of transparency and unintended bias, but, managed correctly the data can

provide insights that would have been impossible for a human to discover.

How AI Is Generating Insights

The Established AI User’s View

is is an extract from an interview with John Sullivan, CIO of Virgin Trains

West Coast, one of the major train operating companies in the United

Kingdom.

AB Tell me how you rst came across AI.

JS Well, I actually studied articial intelligence at college. At that time, it was

obvious to me how useful AI would be but it wasn’t yet very practical,

particularly in a business setting. But I did get a good understanding of

what it could do and why it is dierent from traditional systems.

AB And when did you start looking for AI opportunities at Virgin Trains?

JS In my current role as CIO, I was interested in looking for applications for

AI that could solve some real issues. We had a customer relationship chal-

lenge in that we were getting lots of queries by email that took a long time

for people to deal with. Some of the same questions were coming up again

and again, day in, day out.

So we looked at implementing Celaton’s inSTREAM solution to

improve the service we were giving to the customers. By using AI we were

able to respond to the majority of these queries quicker and more consis-

tently. It also made it more ecient for us as a business and the work

became more interesting for the sta.

AB What does the AI software do?

JS Basically, it reads all of the incoming emails, which of course are all written

in free-form, and works out what the customer wants– it understands

whether their email is a question, a complaint or a compliment and sends

it to the right agent within the organisation to deal with. It also does a lot

of validation work at the same time– so if a customer is writing about a

specic train, the system will check that that train actually ran, and will

also see if there are any similar queries relating to that journey– that all

helps us prioritise the email.

We’ve managed to reduce the amount of eort required at that initial

stage by 85%, which far exceeded our expectations. All of the people that

were doing that very mundane work are now doing much more interesting

stu dealing with the more challenging questions we get.

AB So, do humans still deal with the remaining 15%?

JS at’s right. When we started o we tried to identify which types of que-

ries the AI could respond to and which ones a human would need to do.

Because the system learns as it goes along, more and more of the work can

now be done by the AI.So the agents only work on the really tricky ones,

5 AI inAction

as well as over-seeing the work done by the AI. is oversight task is

important for Virgin as we want to get the tone of the responses just right

so that they align with the brand. We call it ‘bringing out the Richard

Branson’.

AB So apart from improving customer service, what other benets have you

seen?

JS e really important thing for us is that it has allowed the business to be

scalable. We now have a core team of human agents that is stable, so that

any increase in volume can be handled by the AI.Email volumes are now

not an issue for us.

AB How did you approach the implementation?

JS We found Celaton quite quickly and immediately got on well with them.

ey came in and we did a prototype with the Customer Relationship

team. Doing ‘small speedboat’ projects always works best for me for trials.

It’s always better than trying to build an ocean liner, which we know takes

too long, and are dicult to stop!

AB What challenges were there in implementing the system?

JS Change management is always going to be a challenge in projects like this.

e CS team, though, got on board really quickly because they actually

wanted it. Remember– this was a system that was going to take away all

of those mundane, repetitive queries for them. ey couldn’t wait!

e other thing that can be tricky with an AI project is getting the data

right. We denitely needed to involve the IT team, but, because we had a

good vendor in, Celaton knew what the challenges would be so we could

prepare as much as possible. We relied on their resources quite a bit, as we

didn’t really have any AI capability in-house at the time, but they were

good people who could explain everything in a simple manner to our

people—they weren’t just a ‘technical’ supplier.

AB anks John. And nally, what advice would you give anyone just starting

out on their AI journey?

JS Well, the communications, which I just mentioned, are a really important

aspect. You need to be able to articulate what AI is and how it works–

don’t assume that even the CIO knows these things. If I were to do this

whole thing again I’d probably bring somebody in that could focus on

this. Almost like doing internal marketing for the project.

I also think it is vital to open your mind up to what AI can do—again

external input can be useful here. We have Innovation Days to look at

developments we are doing with the trains, and we really should try and

do the same sort of thing for AI as well. It’s all about trying to understand

the art of the possible.

The Established AI User’s View

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_6

Starting anAI Journey

Introduction

I am often asked by executives, “I need AI in my business—how can I imple-

ment it?”. is is, of course, the wrong question to ask. e much more appo-

site question is, “I have some big business objectives/challenges—how can AI

help me deliver or address them?” Ideally, executives should be pulling AI in

to where it is needed the most and will provide the greatest value. In reality,

though, it is usually a bit of push and pull—AI is such a new subject and has

such huge potential to disrupt whole business models that it would be foolish

to consider it only in the context of your existing business strategy.

So, if businesses are already implementing AI and deriving real value from

it, as we have seen in the case studies presented in this book, how did they

actually start their journey? How did they discover, plan and implement the

solutions? is chapter will cover the approach to creating a meaningful and

workable AI strategy.

I have deliberately blurred the lines between an ‘AI Strategy’ and an

‘Automation Strategy’. As is hopefully clear from Chap. 4 when I discussed

the associated technologies, AI is rarely the only answer. Usually other auto-

mation technologies are required, such as RPA, cloud and IoT.But with this

book’s focus on AI, I will be looking at the automation strategy very much

through the lens of AI, mentioning the other technologies only where I think

it is necessary for clarity.

If I could summarise the best approach to maximising value from AI it

would be: think rst, try something out, then go large. It’s very tempting to

jump straight in and create a PoC or bring in some AI software, but creating

an automation or AI strategy, one that matches your ambitions, is based on

your business strategy and has explored the business at a high level for the

prime opportunities, will give you, by far, the best foundation to maximise

value.

Once you have the AI strategy sorted, then you can start trying things out.

is isn’t mandatory, but it usually helps to build knowledge, trust and

momentum within the business. Your initial steps could be through a pilot, a

PoC, a hard prototype or a purchased piece of software. I will explain each of

these and discuss their pros and cons in this chapter.

But once the initial eorts are in and proven, then you will need to look at

pushing on quickly. ere can be, at this stage, what I call the AI Bermuda

Triangle—lots of really good ideas that were heading in the right direction

mysteriously disappear from the radar. is happens with other technology

programs as well—once the eort of getting that rst piece of work o the

ground has been done, everyone sits back and becomes distracted by other

things, motivations dissipate and momentum is lost. Once this happens it is

very dicult to get it back on track again. erefore, the time to really push

forward is as soon as that pilot or PoC or prototype is showing promise and

demonstrating value.

And that plan really needs to be big and bold if it is to escape from the AI

Bermuda Triangle. Creating an AI roadmap is a key part of the AI strategy—

after all, how are you going to go on an AI journey without a map to guide

you? But it’s more than just a list of projects—it needs to describe how AI will

be industrialised within the business, and I’ll cover those aspects in more

detail in the penultimate chapter.

So, armed with the knowledge of what AI is capable of and how other busi-

nesses are using it, it is now time to start your own AI journey.

Aligning withBusiness Strategy

In my work as a management consultant and AI specialist, I know that the

one single activity that will ensure that maximum value is extracted from any

AI program is to rst create an automation strategy that aligns with the busi-

ness strategy, but also one that challenges it.

To align the automation strategy with the business strategy it is necessary

to understand the benets and value that will be derived from that overall

strategy. Business strategies, if they are written succinctly, will have a hand-

ful (at most) of strategic objectives—these might be things like ‘Reduce the

6 Starting anAI Journey

cost base’, ‘Reduce the exposure to internal risk’ or ‘Improve customer ser-

vice CSAT score’.

Each of these strategic objectives would deliver benets to the business:

‘Reducing the cost base’ would deliver lower costs through, for example, not

hiring any new sta, not taking on any additional oce space or minimising

travel. ‘Reducing the exposure to risk’ might be delivered through reducing

the number of unnecessary errors made by Customer Service sta, or improv-

ing reporting and compliance. And ‘Improving Customer Service’ could be

fullled by improving the average handling time (AHT) of inbound queries,

reducing the unnecessary errors (again) and enabling 24×7 servicing.

e important thing from our perspective in creating an automation strat-

egy is to understand its role in enabling some or all of these benets.

In a hypothetical example: AI Search could be used to reduce AHT by

reading incoming documents; Optimisation tools could be deployed to pro-

vide knowledge support to the customer service agents; self-service could be

enabled through a number of AI and RPA tools so as to reduce the demand

on bringing new sta and renting more oce space; Clustering and Search

capabilities could be used to oer deep reporting insights into the business

information; Search could be used to identify areas of non-compliance against

a constantly updating regulatory database and chatbots (using NLU) could be

used to provide a 24×7 service desk. RPA could also be brought in to elimi-

nate errors. Each of these tools then becomes an enabler to the business strat-

egy benets (Fig.6.1).

Tech1

Tech2

Tech3

Parties

“Reduce risk

exposure”

“Improve customer

experience”

“Reduce cost base”

These strategic

objectives

…

…need to deliver these

benefits…

Improve AHT

Improved reporting

Improve compliance

Improve AHT

24x7 servicing

Reduction in errors

No new hires

No new facilities

Minimise travel

…enabled by

automation:

Plan

Analytics & Reporting

Self Service

Customer Centricity

Back Office Automation

Fig. 6.1 Aligning with the business strategy

Aligning withBusiness Strategy

So, before we have even started creating the automation strategy, we must

know what it should achieve. en it is a case of working backwards to deter-

mine the capabilities, technologies, people, plan and so on that will deliver

those objectives.

Of course, there is nothing to stop you from implementing AI simply

because you want to implement AI.In fact, demonstrating to shareholders or

customers that you are an innovative, forward-thinking company because you

are using AI can be of great benet, and much PR can be made of the fact.

But, generally, aligning your Automation Strategy rmly with the Business

Strategy will ensure that the benets you are hoping to achieve will deliver

long-term, sustainable value to the business.

Understanding Your AI Ambitions

e second major aspect to consider before starting your AI journey is to

understand, as much as possible, where you want to end up. at may sound

obvious, or you may think why should you bother thinking about that now

when you haven’t even started anything yet. But, as with any journey, it is

crucially important to know your destination. In AI terms, you won’t be able

to know exactly where you will end up (it’s a voyage of discovery, after all) but

you should at least understand your initialAI ambitions.

ese ambitions can range from anywhere between ‘we just want to say

we’ve got some AI’ to creating a completely new business model from it. ere

are no right and wrong answers here, and your ambition may change along

the way, but knowing your overall aspirations now will mean that you can

certainly start on the right foot and be going in the right direction.

I would consider four degrees, or levels, of ambition, which I have called:

ticking the AI box, improving processes, transforming the business and creat-

ing a new business.

e rst of these, ‘ticking the AI box’, is for those who just want to be able

to claim in their marketing material and to their customers that their busi-

ness, service or product has AI ‘built in’. is approach is surprisingly com-

mon, but I’m not going to focus too much on this approach because it can be

covered by selecting the most appropriate elements from the other types of

approaches. To be honest, you could justiably say that your business uses AI

now because you lter your emails for spam, or that you use Google Translate

occasionally. Many businesses that have deployed simple chatbots claim to be

‘powered by AI’, which is factually true but a little disingenuous.

6 Starting anAI Journey

e rst serious step to extracting value from AI will be in improving the

processes that you already have, without necessarily changing the way the

function or the business works. AI is being used to make existing processes

more ecient and/or faster and/or more accurate. ese are the most com-

mon uses of AI in business right now, and inherently the least risky; process

improvement is the approach that most executives will consider rst when

deciding on their initial steps into the world of AI.

I’ve discussed case studies where processes can be streamlined through the

application of AI Search (extracting meta-data from unstructured documents,

for example) or by using big data to carry out predictive analytics (more

accurate preventative maintenance schedules, for example). Other examples

of process improvements are where AI has helped to lter CVs for job candi-

dates, or helping make more ecient and more accurate credit decisions.

Whilst making existing processes faster, better and cheaper can provide a

great deal of value to a business, there is arguably even more value available

through transforming the processes or the function. By transformation I

mean using AI to do things in a materially dierent way, or in a way that

wasn’t even possible before. Some of the examples of transformation that I

have discussed earlier include: analysing the customer sentiment from hun-

dreds of thousands of interactions (NLU); predicting when a customer is

going to cancel their contract (Prediction); recommending relevant products

and services to customers (Clustering and Prediction); predicting demand for

your service (Clustering, Optimisation and Prediction); or modelling dier-

ent risk scenarios (Optimisation).

One transformation that AI is particularly suited for is the enablement of

customer or employee self-service. e benets of a self-service option are that

it can be made available twenty-four hours a day, seven days a week, and that

it is generally cheaper to run. It also gives the customers or employees a sense

of empowerment and control. AI can be used for the direct engagement

aspects of the process, employing chatbots and/or speech recognition capa-

bilities to communicate with the person, and also for any decisions that need

to be made based on that communication (e.g. should this request for credit

be approved?) by using prediction or reasoning tools. (Also useful when creat-

ing a self-service capability is RPA, which is able to handle all of the rules-

based processing and connect all of the necessary systems and data sources

together in a non-disruptive manner, as discussed in Chap. 4.)

e biggest impact that AI can make on a company is when a whole new

product, service or business can be created using the technology at its core.

Probably the most famous example of this is Uber which uses a number of

Understanding Your AI Ambitions

dierent AI technologies to deliver its ride-hailing service. For example, it

uses AI to provide some suggestions as to where you might want to go to,

based on your trip history and current location (e.g. I usually want to go

straight home after I am picked up from the pub). It will also help predict

how long it is going to take until the driver arrives to pick you up. Uber have

also analysed the time it takes to actually pick people up (i.e. from the car

arriving at the site to when it leaves again) to enable the app to suggest the

most ecient pickup spots. (After using third-party navigation apps, Uber

eventually developed its own navigation capability, although it still relies on

data from third parties.)

Other types of businesses have been created from a foundation of AI. (I’m

not considering AI software vendors or AI consultancies here as they will

inherently have AI at their core.) Powerful recommendation engines have

been used by companies such as Netix and Pandora to transform their busi-

nesses, and Nest, a ‘smart thermostat’ that uses predictive AI to manage the

temperature of your house; without AI Nest would just be another thermo-

stat. Other businesses that have initially used AI as a foundation for their core

business are exploiting the technology to create new revenue. Pinterest, the

website where people post interesting images from other sites, is a good exam-

ple of this. ey developed a very strong image recognition system so that

users could nd, or be recommended, similar images. ey are now develop-

ing apps based on the same technology which can automatically detect mul-

tiple objects in images and then nd similar images of the objects on the

internet, including links to buy those objects (from which they are bound to

take some commission).

So, understanding your AI ambitions is an important early step in develop-

ing an AI strategy and subsequent AI capability. ose ambitions will guide

the rst steps that you take and help steer you on the overall journey. But do

bear in mind that not every part of your business will want or need to go on

the same journey or at the same speed. Your front oce might want to adopt

AI wholesale whilst the back oce is happy to take things slowly. And each

department or function might be starting with dierent levels of automation

maturity. If there are functions that have already adopted some form of auto-

mation or have made eorts to organise their data, these will provide stronger

platforms to introduce AI than somewhere that still operates very manually or

doesn’t have a strong data governance.

e next section introduces the AI Maturity Matrix that can help you assess

both the current automation maturity as well as your ambitions for each sig-

nicant area of your business.

6 Starting anAI Journey

Assessing Your AI Maturity

A Maturity Matrix is a relatively simple concept that is a very useful on a

number of fronts. Not only does it encourage discussion and reection during

the creation process but, once completed, it can also be used as a communica-

tion tool.

Maturity Matrices were originally developed by Carnegie Mellon University

to assess the maturity of IT development functions. ey generally describe

ve levels of maturity, from very immature (Level 1, or ‘Initial’) to world class

(Level 5, or ‘Optimising’). Each maturity level consists of related practices for

a predened set of process areas that improve an organisation’s overall perfor-

mance. us, an organisation achieves a new level of maturity when a system

of practices has been established or transformed to provide capabilities the

organisation did not have at the previous level. e method of transformation

will dier at each level, and requires capabilities established at earlier levels.

Consequently, each maturity level provides a foundation on which practices

at subsequent maturity levels can be built.

Capability Maturity Model (CMM) levels in the IT development world

can be formally assessed by approved consultancies, and many big IT compa-

nies wear their CMM Level 5 badges with pride. But not everyone should

want or need to reach Level 5—in many cases Level 3 (‘Dened’), where

processes are documented, standardised and integrated into an organisation,

is perfectly adequate for most businesses.

is idea of evaluating what is ‘good enough’ for your business, and each of

the major functions in it, is at the heart of developing the AI Maturity Matrix.

e methodology I describe in this section takes the concept of the Maturity

Matrix and applies it specically to automation. As I mentioned when dis-

cussing the Automation Strategy generally, it is best to try and think holisti-

cally about automation but with a strong focus and lens on AI, just so that

related opportunities, or dependencies, are not missed along the way. So, in

the matrix we are considering ‘automation’ that includes the full range of AI

tools (chatbots, search, data analytics, optimisation engines, image classica-

tion, voice recognition, etc.) as well as RPA, robotics, IoT and Crowd Sourcing

(which are described in detail in Chap. 4).

e AI Maturity Matrix has six levels rather than the usual ve—this is to

introduce a Level 0 where there is no automatson at all. Each of the six levels

is described thus:

Level 0: Manual Processing ere is very little evidence of any IT automation

in the organisation. Only basic IT systems such as email and ‘oce’ applications

are deployed. ere are large numbers of people processing transactional work,

Assessing Your AI Maturity

either in-house or through an outsource provider. Data is not considered an

asset and there is no formal governance in place to manage it. ere are no sig-

nicant projects looking at, or delivering, automation.

Level 1: Traditional IT-Enabled Automation e organisation has imple-

mented task-specic IT applications for particular processes (e.g. an invoice

processing application to process invoices). ere is no evidence of automa-

tion tools, and specically AI nor RPA, having been deployed. Data is

managed only to the extent that it needs to be to ensure the smooth running

of the organisation. ere are still large numbers of people carrying out trans-

actional and cross-system processing work.

Level 2: Isolated, Basic Automation Attempts Some individual teams have

used scripting or macros to automate some tasks within a process or applica-

tion in isolated areas of the business. Minimal benets are evidenced from

these eorts. ere are still large numbers of people carrying out transactional

and cross-system processing work. Data is still managed on a nominal basis.

Level 3: Tactical Deployment of Individual Automation Tools Some func-

tions have deployed individual automation tools such as AI and RPA to auto-

mate various processes. Some useful benets have been identied. No

dedicated automation resources are used or have been set up. Some data is

managed so as to make it valuable for the automation eorts, but an

organisation- wide data governance framework does not exist. In areas that

have experienced automation, the sta are working in dierent ways. A case

study or two from the organisation is known within the industry.

Level 4: Tactical Deployment of a Range of Automation Tools Functions or

divisions within the organisation have deployed a number of dierent auto-

mation tools, including AI, across a range of processes. A strong business case

exists that identies sizeable benets. Data is being managed proactively, with

some areas having introduced data management and retention policies. Sta

may have been redeployed as a result of the automation eorts, or they are

working in materially dierent ways. Some dedicated resources have been

organised into an automation operations team. e organisation has a reputa-

tion for implementing new technologies and being innovative.

Level 5: End-to-End Strategic Automation e organisation has imple-

mented a strategic end-to-end process automation program with a range of

automation tools including AI.Signicant benets in cost, risk mitigation

6 Starting anAI Journey

and customer service have been delivered. Data is treated as a valuable asset

and is managed through an organisation-wide data governance framework.

Many of the organisation’s sta are working in materially dierent ways than

before. An Automation Centre of Excellence has been established. e

organisation is renowned for its innovative and forward-thinking culture.

In the above descriptions, I have used the word ‘organisation’ to describe

the scope of the work being evaluated—this could indeed be the whole organ-

isation but could equally be applied to parts of it, such as the Finance

Department, the Customer Services Department or a Shared Service Centre.

When choosing the level of granularity to assess, it should be at the highest

point (i.e. the biggest scope) that has a signicant dierence in automation

approach and needs. Generally, this will be by function or division, but could

also be by geography.

e evaluation of maturity levels is a subjective one, assessed through inter-

views and the review of evidence. e approach should be one that is satisfac-

tory to all people who are involved and must be as consistent as possible across

the dierent areas. Many people use a third-party consultancy to carry out the

evaluation to ensure consistency but also provide independence from any

internal politics there may be.

ere can be some overlap between the dierent levels. For example, a

department could have tactically deployed a range of automation tools (Level

4) but also created an Automation Centre of Excellence (a Level 5 criteria). It

will be up to the person or team evaluating the levels to decide the most

appropriate level to assign it to. e important thing is to be consistent.

As well as assessing the current automation maturity, the ‘automation

ambition’ should also be agreed upon. As mentioned earlier, the target level

doesn’t necessarily have to be Level 5, and could be dierent for dierent areas

that are being assessed. ere are many reasons why not all areas should strive

for Level 5, including the cost of implementation, there not being enough

relevant data, it not being aligned with the strategic objectives and it simply

not being appropriate (a human touch might be the best one in some cases).

A completed AI Maturity Matrix would therefore look something like the

following (Fig.6.2):

It shows both the ‘as is’ level of maturity (in grey) and the agreed ‘automa-

tion ambition’ for each area (in black). Although it is a relatively simple chart,

it lays the foundations for the Automation Strategy and Roadmap. It also

provides a useful communication tool to describe, at a high level, what the

business is hoping to do with automation and AI.

Assessing Your AI Maturity

100

Levels 0, 1 and 2 do not involve the sort of automation that I am consider-

ing in this book. Level 3 is where AI (and RPA) are starting to be introduced,

generally focusing on one or two individual processes. is equates to the

‘ticking the AI box’ ambition discussed in the previous section. Level 4 applies

AI to a wide range of processes, applying it across a number of areas, although

still tactically. is level equates to the ‘process improvement’ ambition. Level

5, where AI is applied strategically across the business, is equivalent to the

‘transformation’ ambition.

e ambition I described of creating a new service line or business by

exploiting AI goes beyond the maturity matrix structure as it is not based on

an existing function or department. at level of ambition means starting

with a blank sheet of paper.

e AI Maturity Matrix then is a useful tool to start your AI journey. It

provides an opportunity to openly discuss the role and opportunities for AI

and automation across the business and provides a platform to develop a ‘‘heat

map’ of what those opportunities might be. e approach to creating an AI

heat map is described in the next section.

Creating Your AI Heat Map

Following the development of the AI Maturity Matrix, the next step in creat-

ing an AI Strategy is to build a ‘Heat Map’ of where the opportunities for

automation might be. Driven by the Business Strategy and the benets that it

is hoping to achieve, the Heat Map provides a top-down perspective on areas

where AI is desirable, economically viable and/or technically feasible. It starts

to identify the types of AI capabilities that could be applied in each area in

order to realise the automation ambitions (and therefore contribute to deliv-

ering the business’s Strategic Objectives).

MaturityLevel

012345

ProcessAreaManual processing Traditional IT-

enabled automation

Isolated, basic

automation attempts

Tactical deployment

of individual

automation tools

Tactical deployment

of a range of

automation tools

End-to-end strategic

automation

Customer Service

Risk Assessment

Operations

Finance

ITSM

Fig. 6.2 AI maturity matrix

6 Starting anAI Journey

101

e approach to creating an AI Heat Map is not too complicated as long as

you understand your business well enough, and, of course, have a sound grasp

of the AI Framework. It is intended as a starting point, a way to bring some

focus and logic to prioritise your initial AI eorts.

Firstly, you must decide on the overall scope of the Heat Map. It usually

makes sense to keep this consistent with the Maturity Matrix. So, if you origi-

nally evaluated the whole business split down into, say, ve dierent business

areas, then use the same structure for the heat map.

Each area, then, is assessed in turn, identifying the opportunities and the

relevant AI capabilities required in each one. is is best done in two passes,

the rst one identifying all opportunities without any judgement being

applied to them, rather like the brainstorming of ideas: all potential opportu-

nities should be logged, dismissing none of them at this stage.

e opportunities are identied by considering a number of dierent crite-

ria, each of which is described below. e opportunities are surfaced through

interviews with relevant managers in the areas being assessed, and should be

done by people who have a sound understanding of the AI capability frame-

work as well as the AI technology market. Organisations without the appro-

priate resources in-house will tend to use third parties for this exercise.

• Alignment with strategic objectives—unless you are looking to imple-

ment AI for the sake of it, it is important to identify AI opportunities that

contribute in some way to achieving the Strategic Objectives. For example,

if your only strategic objective is to improve customer satisfaction, then the

focus for this exercise should be on those processes that can impact cus-

tomer service, rather than, say, cost reduction.

• Addresses existing challenges—there may be opportunities for AI to solve

existing problems, such as inadequate management information, poor

compliance or high customer churn. ese opportunities may or may not

be aligned with the strategic objectives but they should still be considered

for inclusion as they could certainly add value.

• Available data sources—because AI, in most of its forms, requires large

amounts of data to be most eective, a key consideration is to identify rel-

evant data sources. Where there are very large data sets, then it is possible

that there is an opportunity for AI to extract some value from them.

Conversely where there is very little, or no data, it may preclude using AI.

(Some AI technologies, such as chatbots and cognitive reasoning engines,

both discussed in Chap. 3, do not require large data inputs, just captured

knowledge, to work eectively, so don’t simply equate low data volume to

no AI opportunities.)

Creating Your AI Heat Map

102

• Available technology—understanding the AI capabilities and the relevant

tools that can deliver them is also important in order to identify AI oppor-

tunities. Whilst it is always best to ‘pull’ ideas in based on what the business

requires, with AI at the forefront of technology it is also valid to ‘push’ ideas

based on the technology that is available.

By this point you should have a range of dierent opportunities identied

in each of the respective areas you are investigating. For example, in Customer

Services, you could have identied the opportunity to provide an online self-

service capability for the purchase of tickets for events. is could help deliver

your strategic objectives to improve the customer experience and to increase

ticket sales. In terms of the AI capabilities it could require Speech Recognition

(if you want to oer telephone access), NLU (for the chatbots) and

Optimisation (to guide the user through the purchasing process). It could also

require some RPA capability to actually execute the purchase. (I would also

include Crowd Sourcing, discussed in Chap. 4, as another capability that

should be considered in order to support any of the AI opportunities.) With

regard to data sources, there are pre-trained voice services available, and there

is a good source of knowledge available from the human customer service

agents that currently process these transactions (Fig.6.3).

For each of these ideas, you should therefore understand what the opportu-

nity is, how it links to strategic objectives (and/or addresses current issues) and

which AI capabilities you will require to realise it. Building each of these on top

of each other then starts to create a heat map of AI benets and requirements.

Automation Type

Function

RPA

Chatbots

Analytics

Process

Risk

Fraud

Voice

Image

IT automation

Crowd

Customer Service

Risk Assessment

Operations

Finance

Fig. 6.3 AI heat map ﬁrst pass

6 Starting anAI Journey

103

(I tend to use colours to identify the ‘hottest’ areas, but you can use numbers

or whatever approach you are used to.) Areas that are delivering the most ben-

et are clearly visible, as well as those AI capabilities which are going to be most

important to you (Fig.6.4).

e second pass across the various opportunities is to lter them down to

those that are desirable, technically feasible and economically viable. If the

idea can’t satisfy all of these criteria then it is unlikely to be successful.

Desirability is a measure of how much the business wants and needs this

new idea, so it relates to how aligned it is with the strategic objectives and how

it addresses existing challenges. But it should also consider the opportunity

from the customer’s perspective (if it impacts them) and, culturally, how

acceptable the idea will be to the department or function that will be imple-

menting it. In some cases, you will have to consider the personality of the

managers and sta of those areas as well and understand how supportive (or

defensive) they may be. Clearly, if an idea does not pass the desirability test (to

whatever threshold you think is appropriate for your organisation), then it

probably shouldn’t be progressed anyfurtherfor now.

Technical feasibility will already have been assessed to a certain extent

during the rst pass when the data sources and the availability of technology

were taken in to account. For the second pass this is done in more detail,

considering aspects such as the quality of the data, the processing power or

bandwidth that may be required, the maturity of the required technology and

the technical skills that will be required internally and externally to imple-

ment it. Other aspects such as regulatory restrictions, especially about the use

of data, may also have to be considered.

Testing the economic viability is the rst look into what the business case

might belike. A comprehensive business case will be developed in time, but

at this stage the economic viability can be assessed at a relatively high level. It

should start to consider the nancial benets to the business which could be

achieved through cost reduction, risk mitigation, improved debt recovery,

new revenue generation or reduced revenue leakage. Estimates can be made as

to how the opportunity might improve Customer Satisfaction (CSAT) scores,

if relevant. Costs should also be assessed where they can be at this stage. ese

could include license fees, IT infrastructure and professional services fees. It

may be dicult to assess the costs at this early stage; therefore, it could be

appropriate to score, out of 10, say, the magnitude of the costs, with anything

scoring above a certain threshold rejected (this obviously needs to be balanced

against the benets that will be gained).

Each idea should be able to pass all three tests if it is to survive into the nal

Heat Map. You can, of course, make exceptions, but be clear on why you are

Creating Your AI Heat Map

104

AI Heat

Map

Strategic

Objectives

Existing

Challenges

Benefits AI Capabilities

Maturity

Strategic Objective 1

Strategic Objective 2

Strategic Objective 3

Challenge1

Challenge2

Challenge3

Cost Reduction

Customer Service

Compliance

Risk Mitigation

Loss Mitigation

Revenue Generation

Leakage Mitigation

Image

Voice

NLP

Planning

Prediction

RPA

Crowd

Organisation 1

Customer Services 1

Opportunity 1

Opportunity 2

Opportunity3

Operations 2

Opportunity 1

Opportunity 2

Finance1

Opportunity 1

Fig. 6.4 AI heat map

6 Starting anAI Journey

105

making those exceptions, and keep them in mind as you progress forward. Of

the rejected ideas, do keep them safe. ings change, especially around the

technological viability, so something that may not be suitable now may be just

right in the future.

So now you have an AI Heat Map which shows the main areas you will be

focusing on, the benets they can bring and the capabilities required to

achieve them. Each opportunity, as well as the high-level view presented in

the Heat Map, should also have a corresponding passage of text (or a separate

slide) which provides more of the necessary details about what the opportu-

nity actually entails and some explanation of the numbers.

As a summary, and for presentation purposes, the AI Heat Map can be

rolled up into an overview across the main areas and the organisation as a

whole.

Now that we have a good idea of the opportunities that form the basis of

the Automation Strategy, the next stage is to develop those Heat Map oppor-

tunities into a Business Case.

Developing theAI Business Case

Creating a Business Case for an AI project is, in many ways, the same as for

any technology project—there are benets to be achieved and costs to incur.

But with AI the task is made more challenging because there tends to be more

unknowns to deal with. ese unknowns can make calculating an ROI

(Return on Investment) an almost-futile exercise, and the organisation must

rely on an element of intuition as well as the numbers. is is especially true

when new approaches and new services are being developed through AI.

Luckily, the work done creating the AI Maturity Matrix and Heat Map

provides a good methodology for evaluating which are the AI opportunities

that will deliver the most value. Creating a meaningful business case for AI for

each of these is certainly feasible with a little thought.

We already have, from the AI Heat Map, a list of opportunities and a high-

level assessment of which are the most promising. In my example in the previ-

ous section the opportunities which are showing as the ‘‘hottest’ (dark grey)

are the ones to prioritise, especially if they are strong across a number of dif-

ferent aspects (strategic alignment, solving current challenges, benets types).

At this stage, you can easily apply some scoring mechanisms to make the

prioritisation process easier. For example, you could replace the shades or

colours with scores out of 3, and give weightings to each of the criteria, and

then add all these together to give a total score for each opportunity.

Developing theAI Business Case

106

If you are feeling condent, or have a third-party supporting you in this

exercise, you could also introduce additional criteria under the broad heading

of ‘Ease of Implementation’. is would be inuenced by the maturity of the

function (from the Maturity Matrix) but also considers the technical feasibil-

ity and desirability that we looked at when ltering the opportunities during

the second pass of creating the Heat Map. Scoring each opportunity in this

way, again with appropriate weightings, will give additional depth to your

decision-making process.

From this work, we now have a prioritised list of AI opportunities to con-

sider: it’s time to start getting serious with a few of these. Whether you take

just the top scoring idea, or the top 3 or top 10 is entirely up to you, and will

be based on your overall AI ambitions and the time and resources you have

available. However many you take, it is likely that you will need to draft in

some specialist resources at this stage, either from your own organisation or

from third parties.

Dierent organisations have dierent ways of calculating business cases.

Some will focus on ROI, some Net Present Value, some internal rate of return

and others payback. I’m not going to explain each of these here as they are

pretty standard approaches, and I am assuming that you are familiar with the

one that is favoured by your own organisation. But all of these do require

calculating the benets and the costs over time, and so I will provide an aide

memoire of what sorts of things you might need to include in each of these

areas.

e benets that AI can deliver are signicant and diverse, and can be split

into those that are ‘hard’, that is, can easily be equated to monetary value, and

those that are ‘soft’, that is, those that are more intangible anddicult to

quantify. It should also be noted that a single AI implementation may deliver

a range of dierent benets, not just one specic type. is is an important

point—whilst you may implement an AI system to, say, reduce costs, there

could well be associated benets in compliance or risk mitigation. For each

opportunity identied in the AI Heat Map, consider whether each of the

benet types described below could be applicable.

e Hard Benets can be categorised as follows:

• Cost reduction: this is the simplest benet to quantify as there is usually a

base cost that can be compared to a future, lower cost. For AI, these are

generally the situations where the new system will replace the humans

doing a role or activities (remember the discussion in Chap. 1 around

replacement versus augmentation?). e AI search capability will replace

the activities of reading and extracting meta-data from documents—it will

6 Starting anAI Journey

107

do it faster and more accurately than humans which means the benets will

be magnied. Chatbots (using NLU) can also replace some of the work

done by human call centre agents, as can speech recognition when com-

bined with a cognitive reasoning engine. NLG (a sub-set of NLP) can

replace business analysts in creating nancial reports. More ecient route

planning for workers and/or vehicles reduces time and cost.

• Cost avoidance: for businesses that are growing, cost avoidance is the more

acceptable face of cost reduction. Rather than recruiting additional people

to meet demand, AI (as well as other automation technologies such as

RPA) can be implemented as an alternative. e AI solutions will be simi-

lar to those for cost reduction.

• Customer satisfaction: this is usually measured through a survey-based

satisfaction index such as CSAT or Net Promoter Score (the dierence

between positive and negative customer feedback). Some businesses have

connected this to a monetary value, and others relate it directly to a man-

ager’s own evaluation and bonus. For many it is a key performance indica-

tor. AI has the opportunity of improving customer satisfaction through

being more responsive to queries, being more accurate in the responses,

providing richer information in the responses, reducing the friction in cus-

tomer engagements and in making relevant recommendations. (Of course,

AI can also be used to automatically measure customer satisfaction itself

through sentiment analysis.)

• Compliance: some people might challenge the fact that a self-learning sys-

tem can improve compliance which is based on hard and fast rules, but

actually AI is very good at identifying non-compliance. Using NLU and

Search it can match policies and procedures against regulations and rules

and highlight the variances. Compliance benets can be measured through

the potential cost of not being compliant, such as through nes, or loss of

business in specic markets. (RPA, by the way, is a strong driver of compli-

ance as each automated process will be completed in exactly the same way

every time, with each step taken logged).

• Risk mitigation: AI can monitor and identify areas of risk in areas where

it would be impossible, or very expensive, for humans to do it. e classic

example for AI here is fraud detection in high-volume transactions, such as

credit card payments. AI can, in some circumstances, make better risk-

based decisions than humans, such as for credit approvals. AI can also con-

tribute to the Know Your Customer (KYC) process in being able to validate

documents and data sources and help with the credit checking process

(RPA also helps here to access the necessary systems and run the process).

Developing theAI Business Case

108

As with compliance, risk mitigation can be measured by the costs that have

been avoided, such as the loss through fraud or poor credit decisions.

• Loss mitigation: loss mitigation can be achieved through improved debt

recovery. Most of the heavy lifting here can be done through RPA by man-

aging the timing and activities in the process, but AI can play a role, for

example, in generating appropriate letters or engaging with the debtors.

Loss mitigation can be measured through the increase in cash being

recovered through the use of automation.

• Revenue leakage mitigation: this is usually achieved through reducing the

situations where revenue generating opportunities are lost, such as when

customers no longer use your services. Reducing customer churn through

the early identication (i.e. clustering) of the tell-tale behaviours can be

measured by calculating the revenue value of a customer. Other AI capa-

bilities, such as NLU and prediction, can also contribute by keeping the

customer more engaged with your business.

• Revenue generation: this is the AI benet that can probably deliver the

most in terms of monetary value. AI can help enable self-service capabili-

ties (through Speech Recognition, NLU, Optimisation and RPA, for

example), identify cross-selling and up-selling opportunities (through

Clustering) and create new revenue generating opportunities either from

your existing business or through completely new services or products.

Revenue generation is easy to measure after the fact, although isolating the

specic contribution of the AI may be challenging and assumptions will

need to be made. Predicting revenue generation will need to use modelling

techniques, most of which should already be available in your business.

One thing to bear in mind with measuring automation benets is that, in

some cases, sta productivity could actually be seen to decrease after imple-

mentation. is is not a bad thing, but just a reection that the sta are now

handling the more complex cases whilst the technology is handling the sim-

pler ones. Overall, the productivity will have improved, but if you just look at

the human sta they may well be taking longer to process the (more compli-

cated) cases.

e softer benets are inherently more dicult to put a monetary value to,

but can still be evaluated, even if the accuracy is less certain:

• Culture change: this is probably the most dicult thing to achieve and the

most dicult to measure, but can deliver signicant value to an organisa-

tion. Of course, it depends on the type of culture you have already and

6 Starting anAI Journey

109

what you would like to change it to, but AI can help embed a culture of

innovation as well as deliver customer centricity into the business. Actual

benets will usually be associated with a wider change program and it will

be challenging to isolate these that are specically due to AI, but, because

of the potential value, these should not be ignored.

• Competitive advantage: this can deliver signicant benets if AI provides

a rst-mover advantage, a new service line or access to new markets or cus-

tomers. e benets will be closely associated with (and usually accounted

for in) some of the ‘hard’ ones such as cost reduction, customer satisfaction

and revenue generation, but competitive advantage should always be

sought as a way to deliver a step-change in value.

• Halo eect: AI, at least in the current decade, is a highly marketable aspect

to have within a business. e objective of being able to ‘tick the AI box’

can provide the opportunity to claim you are an ‘AI-enabled’ business

which could attract new customers to your product or service. Any amount

of AI implementation, if marketed correctly, can also have a positive impact

on shareholder value if it results in an increase in the share price of the

company.

• Enabling other benets: as well as providing direct benets, AI can also

enable other, indirect benets to be delivered. For example, implementing

AI can free up sta that can be deployed on higher-value activities, or data

that is generated from an automated process can be used to provide addi-

tional insights that can be exploited in another part of the business.

Implementing AI may, through eliminating much of the mundane work in

a department, alsoreduce employee churn.

• Enabling digital transformation: transforming businesses to be more

‘digital’ is a common strategic objective for many businesses right now. AI

is clearly part of that transformation, and many of the benets of a digital

business can be directly delivered, or indirectly enabled, through the imple-

mentation of AI.

An AI business case will include at least one of the above hard and soft

benets I have listed and often quite a few of them. e individual classes of

benets should be used as a starting point for your AI business case. For each

AI opportunity identied in the heat map, consider each type of benet care-

fully so that all can be identied and none are forgotten or left out.

Once all the relevant calculations have been done, the benets for each

opportunity can be summed across the departments and the organisation to

give a full picture of the value that AI can bring. Also, consider whether there

Developing theAI Business Case

110

are any synergies between the dierent opportunities—for example, does hav-

ing a number of AI initiatives going on enable a favourable cultural change in

that department?

e heat map, bolstered with the business case data, now provides a com-

prehensive prioritised list of AI opportunities within your business. But,

before you take the rst step into prototyping and implementing the most

favourable candidates, you will need to understand what your ultimate road-

map looks like and how you will handle all the change management aspects

that go along with it. ese two pieces of the AI strategy jigsaw are covered in

the next two sections.

Understanding Change Management

Even the simplest IT projects require change management. With automation

and AI, the challenges are magnied—not only are you going to fundamen-

tally change the way people work, but you may also have to let people go. And

even if this is not your intention, the reputation of automation projects means

that sta will likely think it is the case.

You may, as part of your AI strategy, be creating a new product or service

that will be competing with your legacy products or services. at ‘healthy

competition’ may be good for business but is not necessarily good for sta

morale. And building a new capability will mean transforming your organisa-

tion—new jobs will be created and old jobs will disappear. Some are going to

be able to cope with the change, and some are not.

Right at the beginning of the book I talked about the dierences between

AI replacing humans and AI augmenting them. Clearly an augmentation

approach will have less change management issues than a replacement one.

But anything that can demonstrate how AI will enrich the work that people

are doing (even if they have to do it dierently) will help. Having greater

insights into their work, or customers, or suppliers, through AI analysing and

interpreting data, will help people become more successful in their jobs.

If automation is replacing tasks, it is often for the more transactional, tedious

ones. ere is a useful phrase coined by Professors Willcocks and Lacity, which is

that “automation takes the robot out of the person”(Willcocks and Lacity2016).

So, if your AI projects in any way relieve people of the mundane, repetitive tasks

that they have been doing for years, then this should be celebrated.

Despite the amount of change that AI inherently throws up, many of the

common change management practices are relevant to AI projects: communi-

cate early and often; involve and empower employees as much as possible;

6 Starting anAI Journey

111

look for short-term wins; consolidate gains to generate further momentum;

incentivise employees where appropriate; pace the change appropriately and

have honest reviews before the next one.

A challenge that automation projects in particular face is a post-prototype

slump. e initial build can become a victim of its own success—the relief of

actually creating something that works, even if it is not a complete solution,

can be so overwhelming that everyone then takes a step back, resulting in a

loss of focus and momentum.

To overcome this, it is rst necessary to be aware of it, and then to prepare

for it. Schedule in review sessions for as soon as the prototype is due to be

completed. Have a Steering Group Meeting soon after, or get the CEO to

make a celebratory announcement about how this is ‘the end of the begin-

ning’. If possible, have the next phase of the project, or another prototype,

start before the rst project nishes, so that focus can quickly shift to that.

e worst thing you can do at this point is to pause for a few weeks or months.

Generally, a ‘big and bold’ approach to an AI program will deliver the most

success, as it avoids any potential slump periods, and helps maintain that all-

important momentum. However, all organisations are dierent and an ‘under

the radar ‘approach may be the most suitable for you—just make sure that

your business case and roadmap (covered in the next section) reect that.

As well as managing all the change within your organisation, you may have

to manage change for your customers as well. If your AI project is one that

impacts the way customers engage and interact with you, then you will need

to fully prepare them for that change. is will involve plenty of communica-

tion, supported by marketing eorts and relevant collateral such as emails,

website articles and frequently asked questions (FAQs).

If your AI project changes the way that you use your customers’ data, then

you will need to make it clear to them what the impact of this is. Any sensitivi-

ties around data privacy, in particular, will need be made explicit. You would be

well advised to seek legal advice for any changes to your customers’ data usage.

is isn’t a book about how to manage change (there are plenty of those

about already), and you will likely already have your own preferred method-

ologies to tackle this. is is only to say that you should not neglect change

management in any way—implementing AI brings about its own unique

challenges, especially when some of the changes in an organisation can be

seismic in nature. ese challenges need to be understood and carefully man-

aged, and built into your AI roadmap.

Understanding Change Management

112

Developing Your AI Roadmap

e AI Roadmap provides a medium- to long-term plan to realise your AI

Strategy. It can be a relatively simple aair that draws heavily from the AI

Maturity Assessment and the AI Heat Map.

e Maturity Assessment will provide you with your starting point. ose

areas that are immature when it comes to automation will require more eort

and time to complete. Your Change Management Assessment will also direct

you to those areas that might need additional support along the way.

At the core of the Roadmap will be the opportunities identied through

the AI Heat Map and subsequent Business Case. But rather than having a

series of individual opportunities being worked through, it is good practice to

group them into common themes. ese themes can represent a number of

dierent projects, or work streams, ideally centred around common AI tools.

For example, a process excellence stream might focus on improving the accu-

racy of processes and reducing the AHT, whilst an analytics and reporting

stream would focus on delivering compliance and improved reporting. Each

stream, using a range of dierent technologies, relates specically back to a

number of benets that are outcomes of the business strategy.

e AI Roadmap should be kept at a relatively high level, identifying the

specic project work streams and associated activities (change management,

program management, governance, etc.). For each work stream there should

then be a much more detailed project plan which identies dependencies and

responsibilities (Fig.6.1).

It is likely that you will want to build one or two prototypes of your most

favourable candidate opportunities, and these should be built into the road-

map, ideally with their own detailed project plans. More details on the

approach to prototyping are provided in the next chapter. e prototype

builds will test and validate assumptions but also provide important momen-

tum for your AI program.

e key considerations when building a roadmap include: your ultimate

ambition with regard to automation (is this a game changer for your busi-

ness or just something you want to try out in certain areas); whether you

want to evaluate the whole business rst and then implement AI or try and

get some quick momentum by implementing in some specic areas rst;

and how much change your organisation can bear at any one time (see previ-

ous section).

6 Starting anAI Journey

113

It is important to remember that your AI Roadmap will be an important

communication tool. It should set out clearly what you are going to do and

when, so that you can use it to explain how it will deliver your AI strategy, and

ultimately contribute to your overall Business Strategy.

Creating Your AI Strategy

Your AI Strategy is now eectively complete. You will have looked at your

Business Strategy and worked out what aspects of that could be satised by

the application of AI technologies. You will have determined how ambitious

you want to be with your AI program—whether it is just to be able to say

you have some AI, or to improve some processes, or to transform parts of

your business. You may have even decided to create a new business by

exploiting AI.

You will then have looked at your organisation and assessed its level of AI

maturity. Some functions or departments could still be working manually,

whilst others could have started some automation projects and others might

even have some AI projects in place already. For each of these areas you will

have worked out their AI ambition, and thus determined the size of the gap

that you have to work with.

e AI Heat Map is the tool that helps identify how you are going to bridge

that gap. Based on your knowledge of the AI Capabilities Framework you will

have identied a number of dierent AI opportunities in each of the areas,

assessing and prioritising their potential benets, challenges and their align-

ment with the Business Strategy.

From the information in the AI Heat Map you will have developed a high-

level business case that sets out both the hard and soft benets that you expect

to achieve.

And nally, you will have considered the change management activities

that are necessary to ensure the success of the AI program, and built them into

an AI roadmap that sets out your medium- and long-term plans of how you

will realise the AI strategy.

It is now time to start building something.

Creating Your AI Strategy

114

The Cognitive Reasoning Vendor’s View

is is an extract from an interview with Matthew Buskell, Head of Customer

and Partner Engagement at Rainbird, a leading vendor of Cognitive Reasoning

software.

AB: Why do you think AI is being so talked about right now?

MB: I think it’s a combination of forces coming together and driving the inter-

est. Specically Outsourcing, Cloud Computing, Big Data and, of course,

Hollywood.

e major driving force for outsourcing, realistically, is economic.

However, regulation has increased and margins have continued to reduce.

So, when outsourcing does not give a deep enough return we need to look

elsewhere. In this case AI is attractive because it allows us to amplify the

productivity of the sta we already have in a way that is not possible by

outsourcing alone.

Cloud computing is a big part of the reason AI has become economi-

cally viable. at and the algorithms that have been optimised and rened

over the past 30years. To illustrate this let me tell you about Ian. In 1994

Ian was doing undergraduate studies in software engineering and AI with

me at Birmingham. He decided to code a PhD thesis on linguistics.

When he ran his program the processing power required was so great that

it ground the entire Sun Hyperspark network of servers and workstations

to a halt for the entire campus. So he was banned from using the comput-

ers outside of the hours of 10pm–6am. As a result, we did not see Ian for

nearly a year. Today we call his eld of research Natural Language

Understanding and you can buy it for a fraction of a cent per hit from

several cloud providers.

Big Data has made AI interesting. For some types of AI like Machine

Learning to work well they love large amounts of data. Whilst it’s true

most businesses were collecting data 20years ago, until recently they had

not put the big data platforms in place that would allow AI systems to

access them. Now that they do, AI is well placed to take advantage of it.

Hollywood might seem a weird one to add, but that fact is as humans

we have been fascinated by the idea computers could think since Ada

Lovelace described the rst computer in the eighteenth century. TV pro-

grams like Channel 4’s Humans in the UK and movies like Ex-Machina

fuel that excitement. It also sells news articles and ad-words on blogs.

ere is a famous, but anonymous quote that sums up Hollywood’s inu-

ence on AI: “Articial Intelligence is the study of how to make real com-

puters act like the ones in the movies.”

6 Starting anAI Journey

115

is also poses a big challenge for the AI industry today. e fact is we

are way o simulating a human mind, so the expectation of AI is so high

it is bound to lead to some disappointments along the way.

AB: What value do your customers get out of your software?

MB: e Rainbird software is about mimicking human decisioning, judge-

ment and recommendations. It’s dierent to other AI in that its ‘people

down’ rather than ‘data up’. Meaning we take a subject matter expert and

sit them down with Rainbird so they can teach it.

When you stop for a moment and think about what you can do if you

mimic human expertise and decisioning the list is endless. We have been

involved with projects from Abbey Road studios trying to mimic the

expertise of their (rather ageing) sound engineers, through to large banks

and tax consultancies trying to reduce headcount by encoding the best

employees’ expertise into Rainbird.

On the whole, however, we have noticed two areas where we see sig-

nicant value:

1. If you can take expertise and create a solution that allows custom-

ers to self- serve you can drive some staggering eciencies. At the

moment, this is being manifested as a ‘chatbot’. However, long

term, we believe companies will have a ‘cognitive platform’ and the

UI may or may not be a chatbot.

2. If you can take knowledge that was in a process and move it fur-

ther upstream the impact to the downstream processes is dramatic.

In one instance, we moved the liability resolution in a motor claim

so that it could be handled by the call centre agent. is removed

the need for over 15 downstream processes.

AB: What do customers or potential customers need to focus on if they are

going to get the maximum value from AI?

MB: I have noticed a lot of companies rushing into AI proof-of-concepts with-

out spending enough time working though the customer journey or the

business case.

ere are some customers that are approaching this correctly. For

example, I was with a client the other day in Scotland that was taking a

much more Design inking approach to the development of a PoC.

When we started the process we all had the idea we could get up and run-

ning quickly with a simple bot that could answer FAQs. When we actu-

ally looked at the questions and followed the conversation with a real

agent it quickly became clear that the general advice would only last a few

seconds before it became specic. At that point, you needed a human in

The Cognitive Reasoning Vendor’s View

116

the loop, so the result of putting AI in place is that you would have paid

twice—once for the virtual agent and secondly for the human. is is a

Bad Idea. We were actually able to nd the value by going deeper with the

virtual agent and make it capable of completing a full interaction with a

smaller number of questions.

AB: Do you think the current hype around AI is sustainable?

MB: Personally, I don’t think the hype is helpful, so I welcome it slowing down

or becoming more grounded. However, I think unfortunately it will con-

tinue, mainly because we need AI to work. Without it we have to face

some pretty stark economic realities.

AB: How do you see the market developing over the next 5years or so?

MB: e market is very fragmented at the moment and the use cases for the

technology are very broad. So, I think it will do what software innovation

has always done: all these companies will consolidate so they can oer an

end-to-end solution and the use cases that work well will survive and the

rest will die out.

e nal thought I would leave you with is from John McCarthy, who

is considered the father of AI: “As soon as it works it’s no longer called AI

anymore.”

Reference

Willcocks LP, Lacity MC (2016) Service automation: robots and the future of work.

Steve Brookes Publishing Warwickshire, UK.

6 Starting anAI Journey

117

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_7

AI Prototyping

Introduction

Creating your rst AI build, however small, is a key milestone for any AI pro-

gram. After all of the hard work developing the AI Strategy, this is the point

at which things really start happening, and people in your organisation can

actually see tangible outputs. Getting this stage right, therefore, is vitally

important.

ere are a number of dierent approaches to this, which I cover in some

detail in the ‘Creating Your First Build’ section of this chapter, and they each

have dierent names and acronyms. For simplicity, I have used the word ‘pro-

totype’ as the general term for all of these.

Prototyping can be done once the AI Strategy is complete, or can be done

to validate some of the assumptions during the development of the strategy.

is may be inuenced by how important those assumptions are (if they are

fundamental to the success of the program, then test them early) and how

much stakeholder buy-in and momentum you need (a successful prototype is

a great way to engage with stakeholders).

Some of the decisions you make at the prototyping stage may have an

impact on the rest of the program. e most important of these is your tech-

nology strategy—do you build your AI solutions yourself, buy them from a

vendor or build them on top of an established AI platform, or use a mixture

of all of these? It is this question which I will address rst.

118

Build Versus Buy Versus Platform

Some of the business case costs that were discussed in the previous chapter

will have required some assumptions on how you intend to create your AI

solutions. Generally, there are three main approaches to take: o-the-shelf AI

software, an AI platform or a bespoke AI build. Your nal solution may

involve any of these approaches, but the core functionality is likely to initially

focus on one of these:

• O-the-shelf AI Software is generally the simplest approach, since most of

the hard work in designing the system has already been done by the vendor.

As I’ve described in the earlier chapters, there is a plethora of AI vendors,

each of which can provide a specic capability that can be used as a stand-

alone application or as part of a wider solution.

As well as all the design, testing, debugging and so on having already

been done, implementing o-the-shelf software means that the product

will be supported by the vendor, and they are likely to have either their own

implementation resources, or partners trained up to support implementa-

tion. You will still need to exert eort to identify, clean and make available

the required data, as well as providing subject matter expertise regarding

your own processes (so ‘o-the-shelf ’ is a bit of a misnomer), but generally

this is the ‘path of least resistance’ for implementing an AI capability.

e biggest disadvantage of using a software ‘package’ is that its capabili-

ties may not align well enough with your objectives and required function-

ality. You may want to implement, for example, a system to analyse the

sentiment of your customers’ feedback. One vendor may provide a system

that analyses the data exactly as you would have expected but doesn’t pro-

vide any useful reporting tools. Another may be less eective at the analysis

but has a best-in-class reporting suite.

• ere are also a number of risks in relying on a software vendor to provide

your AI capability. Because of the excessive hype around AI right now

(remember Chap. 1?) there are quite a few vendors that are little more than

an idea and a website. e challenge is that many of the AI software ven-

dors are start-ups or young companies, so it can be a dicult task to iden-

tify those with viable and stable products. Your due diligence should be

extremely thorough so that it lters out those who do not yet have enough

experience or capability. Using AI experts at this selection stage can save

considerable pain later on in the project. (You may, though, wish to delib-

erately go with a software vendor that does not yet have a fully commercialised

system if their technology could potentially provide you with a distinct

7 AI Prototyping

119

competitive advantage, or where you want to help them shape their oer-

ing, so that it delivers some specic advantage to you, whether that be

functionality, exclusivity or price.)

e commercial models for AI software can be quite varied. e most

common, and simplest, is an annual subscription that includes the neces-

sary licenses as well as the support and maintenance of the software. It will

usually include hosting of the software by the vendor (or an agent of the

vendor) if it is a software-as-a-service model. ese AI software packages

are fully formed, with a user interface for training and using the product.

For other types of AI software where the software is accessed through an

API (e.g. to determine the sentiment of a particular passage of text) a ‘pay-

as- you-go’ model is used—the user pays per API call made. ere are usu-

ally bands of pricing depending on the volume of calls made.

• AI Platforms are the middle ground between an o-the-shelf-package and

a bespoke build. ey are provided by the large tech companies such as

IBM, Google, Microsoft and Amazon, some large outsourcing providers,

such as Infosys and Wipro, as well as specic platform vendors such as

H20, Dataiku and RapidMiner.

In the section on ‘Cloud Computing’ in Chap. 4 I described Amazon’s

‘stack’ of AI services, which is typical of a platform oering. It provides

ready-made, ready-trained algorithms for those with simple and well-

dened requirements (such as generic text-to-speech translation); untrained

but ready-made algorithms for those that need to add their own specic

practices, nuances and data into the model; and a set of AI tools for

researchers and data scientists to build their algorithms from the ground

up. (e platform provider can also usually provide the infrastructure facil-

ities as well.) Of course, an organisation may want to use a range of these

services for the dierent capabilities that their solution requires.

e platform approach may make sense if your organisation already has

a relationship with one of the tech giants, especially if there is already a

strong connection with cloud services. You may nd, though, that you

have to make some compromises on certain aspects where a platform pro-

vider’s specic AI capability may not be as strong as a stand-alone one. Of

course, you can always mix and match between the two types, depending

on how rigid your partnership model is.

Outsourcing providers also provide AI platforms for their clients—for

example Infosys have Nia and Wipro have Holmes. ey usually combine

a data platform (e.g. for data analytics), a knowledge platform (to represent

and process knowledge) and an automation platform which brings these

together with RPA. If your outsourcing provider does have a robust AI

Build Versus Buy Versus Platform

120

platform then it is certainly worth looking at it—you will probably have to

use their resources for the implementation, but it does provide a relatively

simple route assuming that their capabilities match your requirements.

As well as the platforms provided by the tech giants and outsourcing

providers, there are also development platforms available from AI-only

vendors, such as H20, Dataiku and RapidMiner (these are sometimes

referred to as Data Science Platforms). ese provide a solid foundation for

developing AI applications with your own resources or using third parties.

ey consist of a number of dierent tools and application building blocks

that can be integrated with each other whilst providing a consistent look

and feel. is approach will give you the greatest exibility and control out

of all the platform models—the tools are generally more advanced than

those provided by the bigger platforms. You will need to take a more hands-

on approach to the design and implementation though—the main users of

these systems will be the data scientists.

e pricing models vary between the dierent providers. For commer-

cial developments, some oer a revenue share model where they will charge

an ongoing percentage (usually around 30%) of any subsequent revenue

generated from the application built on their platform. e most common

approach is to charge per API call—this means that every time the applica-

tion that you have developed uses a specic piece of functionality of the

platform (e.g. converting a piece of text to speech) there is a small charge.

Commonly there is a threshold below which the API calls are free or at a

xed price.

In the platforms space, there is some overlap between each of the catego-

ries I have dened—IBM, for example, could easily t into all three—but

this does provide a useful framework for selecting which one (or ones) may

be most appropriate for your AI strategy.

• Building bespoke AI applications will provide you with the greatest level

of exibility and control; however, it is unlikely that you will want to take

this approach across all the opportunities that you have identied. Bespoke

AI development, just as for any bespoke IT development, can be great for

providing you with exactly what you need but can create related issues

around change management and support. erefore, for the majority of

enterprises, bespoke AI development should be used only when absolutely

necessary, that is, for complex, very large data problems, or when creating

a completely new product or service that requires technological competi-

tive advantage. (It may make sense to use bespoke development for creating

the initial builds—see next section for details—but subsequently moving

onto a vendor or platform approach.)

7 AI Prototyping

121

A bespoke build will also require eort to be put into designing and

building the user interface for the AI system. is can be good in that you

can develop something that suits your users perfectly, but can be a chal-

lenge to get it just right. Many excellent and clever AI systems have failed

because the user interface simply wasn’t good enough.

For a bespoke development, you will need your own highly capable data

scientists and developers or (more likely) to bring in specialist AI consul-

tancies that already have these resources. e commercial model is usually

based around Time & Materials, although Fixed Price and Risk/Reward

can also be used when the requirements are well dened.

You may want to use a combination of all the above approaches. If your

AI strategy is ambitious and will impact many areas of the business, then

you will probably want to consider the AI platforms as your prime approach,

making use of o-the-shelf packages and bespoke build where appropriate.

If you only want to target one or two AI opportunities, then it may make

more sense to consider just the o-the-shelf solutions. A strategic program

to create a brand-new product or service may require bespoke development

to ensure that it delivers enough competitive advantage.

So, now that you have an idea of the development approach, or approaches,

that you are going to use, it is time to actually build something.

Creating Your First Build

At this point in your AI journey you will have a list of prioritised opportuni-

ties, each with an estimate of the value that it could deliver, as well as an idea

of how they could be implemented (build, buy or platform-based). For the

next stage, we need to test some of our assumptions and build some momen-

tum for the program, which means we have to start building something.

e scale of this initial build stage will depend very much on your intent.

ere are ve generally accepted approaches to this, each of which focuses on

satisfying slightly dierent objectives. You won’t need to understand the

detailed intricacies of each of these approaches (your internal development

team or external partner will have the necessary knowledge) but it is impor-

tant to know which is the most appropriate approach for your project so that

you don’t waste valuable resources and time.

ere are some overlaps between the dierent approaches, and you could

legitimately choose specic aspects from each of them. You may also want to

use a number of these approaches in turn, as they each focus on dierent

objectives to be achieved.

Creating Your First Build

122

• Proof of Concept (PoC): a PoC is a software build that will be discarded

once it has been able to demonstrate that the concept that is being tested

has been proven. It can be of any scale, but generally focuses on the barest

functionality whilst ignoring any complexities such as security or process

exceptions (this is called ‘keeping to the happy path’). PoCs will not go into

live production, and will use non-live ‘dummy data’. PoCs are usually car-

ried out to test the core assumption (e.g. we can automate this process

using this data set) but are also useful to manage stakeholder expecta-

tions—seeing a system in the esh early on often provides an extra boost of

momentum for the project as a whole.

• Prototyping: this is a broad term that covers the building of specic capa-

bility in order to test the viability of that capability. Prototypes may focus

on the user interface (usually called horizontal prototypes) or specic func-

tionality or system requirements (vertical prototypes). One project may

have a number of dierent prototypes, each testing for a dierent thing. It

is rare for a prototype to resemble the nal product. As with PoCs, proto-

types are generally discarded once they have done their job, but some can

be incorporated into the nal product (this is called ‘evolutionary proto-

typing’)—it is important to know at the start whether the prototype will be

retained as it will need to be built more robustly than a throwaway one.

• Minimum Viable Product (MVP): the proper denition of an MVP is

one where the build, which contains the minimum level of functionality

for it to be viable, is released to its customers or users. e feedback from

these early adopters is then used to shape and enhance the subsequent

builds. e term MVP has, though, become a catch-all description for any

sort of early build and therefore tends to lose focus as to what it might be

looking to achieve. For a build that is not intended to be released, then a

RAT (see below) is generally the better approach. For a build that will go

live, then a Pilot (again, below) is usually preferred. MVPs can have their

place in the development cycle, but only once their objectives have been

well dened.

• Riskiest Assumption Test (RAT): RATs are a relatively new approach that

focus on testing only the riskiest assumption that requires validation. is

is in contrast to the MVP which is less specic about what it is trying to

achieve. Also unlike MVPs, RATs are not intended to go into production.

A RAT will seek to build the smallest experiment possible to test the riski-

est assumption that you have. As with prototypes (RATs are really a specic

type of prototype) a number of dierent tests are carried out to validate

dierent assumptions—once the riskiest assumption has been tested and

validated, then the next RAT will focus on the next riskiest assumption and

7 AI Prototyping

123

so on. Understanding what the riskiest assumption actually is requires

some detailed thought—you will need to identify all of the assumptions

that you have made so far that need to be true for the AI opportunity to

exist. Ask yourself if these assumptions are based on other assumptions,

then try to identify the root assumptions and then nd the riskiest one of

those. en it is a case of working out how you can test that assumption

with the minimum amount of code. RATs are not the easiest approach to

take but can deliver the best long-term value for your project.

• Pilot: a Pilot, like an MVP, will be used in a production environment.

Unlike an MVP, it should be fully formed and complete. It is considered as

a test because it focuses on automating a process or activity that is relatively

small and low risk to the organisation. Pilots take longer to build and are

more expensive than any of the options above, but have the advantage of

not ‘wasting’ time and eort (although focused prototyping is rarely a

waste of time). e Pilot needs to achieve all of things that are expected of

a PoC and MVP as well as being fully functional; therefore, the candidate

process needs to be chosen well—if it is too complex (e.g. it involves lots of

systems or has many exceptions) then it will take too long (and cost too

much money) to build, and crucial momentum will be lost. A successful

Pilot will provide a huge condence boost to the project as well as deliver

tangible value.

As with the wider automation strategy, when selecting the most appropri-

ate approach for that initial build always bear in mind what your end goal

is—ask yourself whether you need to build any sort of prototype at all (the

answer is probably yes) but also which approach is going to provide you with

the highest chances of success. Speed of implementation and cost will also be

big factors in the choice of your approach. As a default answer, the RAT is

likely to be the best approach for the majority of cases, but do consider all

possibilities.

Understanding Data Training

One aspect where AI projects are generally trickier than ‘normal’ IT projects

is with the dependency on data, and this challenge is particularly acute during

the prototyping stage.

Although it is possible to cut down on the functionality and usability of the

system being built in order to test assumptions, it is rarely possible to do the

same with the training data. Of course, not all AI systems rely on large data

Understanding Data Training

124

sets—cognitive reasoning engines and expert systems require knowledge

rather than data, but for machine learning applications in particular, data

availability is paramount.

ere will be a minimum amount of data that will produce a viable out-

come, and this can sometimes mean that the prototype will have to rely on

nearly all the data available, rather than a small sub-set. If your complete data

set contained, say, 10 million records, then a sample size of 10% may still be

workable, but if your complete data set was, say, 100,000 records then

(depending on what you intend to do with it, of course) a sub-set may not

give you any meaningful results.

e more complex the model you want to create (i.e. one with many dif-

ferent parameters), the more training data you will need. If the data is insuf-

cient, your AI model could suer from what data scientists call

over-tting—this is where the algorithm models the t of the data too well

and doesn’t generalise enough (it ‘memorises’ the training data rather than

‘learning’ to generalise from the trend). To understand whether your model is

exhibiting over-tting and correct for it there are various statistical methods,

such as regularisation and cross-validation, that can be exploited, but usually

at the expense of ecacy.

A good portion of your training data must also be used for testing. is

validation set should, like the training set, be ‘known’; that is, you should

know what output you are expecting from it (‘this is a picture of a cat’) but

you don’t reveal this to the algorithm. Because AI outputs are probabilistic the

answers won’t always be correct, so you need to understand and agree what

level of condence you are comfortable with. ere is no hard and fast rule

about how much of your training data should be retained to do this testing

but 30% is a good starting point. Factors such as the size of the complete set,

the richness of the data and risks of getting the wrong answer will impact this

gure.

You may not be able to get around the fact that, in order to train and test

your prototype system eectively, you need to use nearly all of the data you

have available. If data availability is going to be a challenge then one or more

RATs, as described in the previous section, may be the best way forward at this

point.

Once you have a good understanding of how much data you will need for

the prototypes, you will need to acquire the data, (probably) clean it and

(maybe) tag it. I can’t stress enough how important the quality of the data is

to the value that the AI system will deliver.

e most likely scenario is that the data will be sourced from within your

own organisation—it may be customer records, transaction logs, image libraries

7 AI Prototyping

125

and so on. You may also want to acquire data from external sources such as the

open-source libraries I discussed in the section on big data in Chap. 2. is

data will come ready-tagged, but there may also be publicly available data sets

(such as trac movements) that will require tagging before they become useful

for training your algorithm.

e cleanliness, or accuracy, of the data will clearly have an impact on the

ecacy of your AI system. e old adage of ‘garbage in, garbage out’ is par-

ticularly relevant here. Common sense and system testing will determine

whether your data is of sucient quality to provide useful results.

For training data that needs tagging, such as images or video, the most

ecient way is through crowd sourcing (see Chap. 4 for how crowd sourcing

works). Crowd sourcing rms, such as CrowdFlower, have many human

agents at hand to complete micro-tasks—each are sent items of the data and

simply tag them with the required information (this is a picture of a cat, this

is a picture of a dog, etc.). e fully tagged data set is then returned to you so

that you can train the model.

One risk with crowd sourcing is that any biases in the humans doing the

tagging will eventually be reected in the AI model. As a rather silly example,

if the human agents doing the tagging were all very short, and they were asked

to tag pictures of people as to whether they were tall or not, then it may be

that a higher proportion than expected would be tagged as tall. at ‘small-

ness bias’ would be carried through into the model and skew the results of the

new, unseen data. Most good crowd sourcing rms though will ensure that

bias doesn’t enter the data in the rst place, but can also correct for any bias

that may exist.

Another, rather innovative, approach to data acquisition and training is to

use virtual worlds or computer games instead of ‘real life’. I mentioned previ-

ously that Microsoft have created a specic version of Minecraft which can be

used to train AI agents, and some software companies are using games like

Grand eft Auto to help train their image recognition software that is used

in driverless cars. So, rather than having to drive around many dierent envi-

ronments for hours and hours lming and then tagging objects, all of it was

done in a fraction of the time in the virtual world of the computer game.

So, with your AI Strategy complete, and your rst build underway (which-

ever way you have decided to approach it), it is time to think about the longer

term, and how you can start to industrialise your new-found AI capability.

But not before getting a good understanding of all the risks that AI can raise,

and how you might mitigate these, and that is the subject of the next

chapter.

Understanding Data Training

126

The AI Consultant’s View

is is an extract from an interview with Gerard Frith, co-founder ofMatter

and Sensai.ai, AI Strategy and Prototyping consultancies.

AB: What got you into the world of AI in the rst place?

GF: I have been fascinated with the mind since I was small child. I read Jung

and Freud in my early teens, Godel Escher Bach when I was 18. I initially

studied Psychology at university, but in my rst year it was my Cognitive

Science module that grabbed my attention most. I became obsessed! At

the end of my rst year, I decided to start over, and signed up for an AI

degree instead. at was 1991.

Once my degree was done, I found out that the rest of the world was

rather less interested in AI than I was. During the AI winter of the 1990s, I

mostly had to pretend my degree was just an exotic computer science degree.

In 2013, the rest of the world was nally catching on, and I established

Matter AI, one of the rst AI consultancies anywhere. I sold Matter in

late 2016.

My major focus at the moment is on the use of AI to create better and

deeper customer relationships.

AB: Why do you think AI is being so talked about right now?

GF: ere are two key reasons: rstly, AI tech has recently become practically

useful. at’s mostly due to the vast amounts of data that the digital age

has exponentially spewed, and to the massive advances in available pro-

cessing power. e theoretical advances in AI that underpin much of

what is being used in business today were developed years, and some-

times decades, ago.

e second reason is that AI has always been exciting, scary, troubling

and challenging in a way that no other technology is or could be. If you take

away the breathless discussions of how AI will take all our jobs, then you are

left with a pretty appropriate level of coverage of a disruptive technology.

e creation of genuine AI could easily claim to be the most signi-

cant scientic achievement in history. It has the potential too to be one of

the most signicant social and spiritual achievements.

AB: What value do your customers get out of your services?

GF: My customers say they get three main things from working with us:

• Expert guidance. I’ve been a developer, a CEO, and a management

consultant, so I know the technology, I know the market and I

understand what a company needs to be successful.

• I’m a disruptor. I like to challenge the current equilibrium and bring

new thinking to situations that allows organisations to reinvent

their value propositions and the way they deliver them to market.

7 AI Prototyping

127

• Because of my breadth of experience, I can provide the glue that

brings together tech and business strategy, and then how to execute

those strategies in eective ways.

AB: What do customers or potential customers need to focus on if they are

going to get the maximum value from AI?

GF: e same things they should always focus on when assessing new tech-

nology—how can I create greater value for my customers using this tech-

nology? What competitive advantage could this give me? e critical

thing to do is to move fast. It’s getting easier for disruptors to change

industry dynamics quickly, and with less capital. But do treat AI as an

early stage technology too. Invest seed capital in experiments, but get

those experiments live quickly and then iterate.

AB: Do you think the current hype is sustainable?

GF: It’s inherent in human systems that hype is never sustainable. Our biol-

ogy seeks novelty and in doing so makes cycles of hype and deation

inevitable. at said, I do believe that AI is worthy of tags like the ‘4th

Industrial Revolution’ in ways that, say, the 2nd and 3rd ones weren’t. In

this sense it’s just a continuation of the impact of technology develop-

ment in general, but its impact will be quicker, but only in the same way

that the internet’s impact was quicker than that of TV, and the impact of

roads occurred faster than the impact of the printing press.

Beyond its vast (but normal) economic impact though, I think it will

actually change everything for humans. It is not like any other technology

in terms of how it aects how humans will think about themselves.

Currently even professors of neuroscience who argue that the mind is

nothing greater than a complicated computer hold on privately to the

idea that they have free will and a ‘soul’. AI will challenge those ideas in

quite new and scary ways.

AB: How do you see the market developing over the next 5years or so?

GF: e next ve years will be very exploratory. AI is a general-purpose tech-

nology and will thrust out in multiple directions at once. We are seeing

new products appear weekly covering a plethora of use cases, with even

the biggest tech players only covering relatively small areas. Companies

will need to look at gluing together a range of best of breed providers,

often with a strong vertical industry focus.

We’ll undoubtedly see a lot of acquisitions, but only slow consolida-

tion as tech companies focus on land grab. We’ll also begin to see some

industries such as Law begin to be taken over by software. In general,

industrial boundaries will become even more porous as data drives the

discovery of new horizontal processes that can cross the traditional

verticals.

The AI Consultant’s View

129

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_8

What Could Possibly GoWrong?

Introduction

It is important to provide a very balanced view on the benets and risks of

AI.roughout the book, I’ve talked about some of the unique challenges

that need to be faced when implementing AI, but it is worth drawing these

out into a separate chapter, as I have done here, to make sure they get the

attention they need.

In the chapter I’ve tried to cover the ‘localised’ challenges—those that any

company will have to consider when implementing AI—but also the more

general ones that will aect the way that we work and how we will live our lives.

The Challenge ofPoor Data

Data quality is traditionally measured by the ‘4 Cs’: Clean, Correct, Consistent

and Complete. But in the world of AI and Big Data we need dierent rules to

guide us. Data quality can be thought of as a combination of data veracity (its

accuracy) and data delity (its quality in the context of how it is being used).

Data veracity is less important in the world of Big Data because any small

errors will be drowned out by the sheer volume of correct data. e model

that is created from the algorithm will look at overall trends and tness, so a

few outliers will not signicantly impact the outcome. (If there are fundamen-

tal errors in the data, such as the decimal point being one place out on every

data point, then, of course, it will have a material impact, but in this section

I am only considering small issues in data quality.)

130

With data veracity, whether the data is being used for AI or not, there is

always a balance to be made between accuracy and cost. e trick is to under-

stand what level of accuracy is ‘good enough’. With AI applications, that

threshold is generally lower than for more traditional computing applications

because of the generalised modelling eect.

Data delity, on the other hand, can have a serious impact on the results.

is is where the data is inappropriate for the task for which the AI has been

set. In Chap. 5 I wrote about one example of AI being used to predict recidi-

vism of people who have been charged with criminal oences by the Durham

Police Force. I noted that the data used for the predictions was sourced only

from the region itself, and therefore excluded any relevant activity the subject

might have taken part in outside of Durham County. So, a career criminal

from Leeds (just down the A1 main roadfrom Durham) who had just com-

mitted his rst oence in Durham may have been allowed to walk out of the

police station because he was considered a low risk. In this case, the data that

the AI system is using is correct but not wholly appropriate.

Another, slightly dierent, reason that the source data would not be appro-

priate is if it contained unintended bias. is is a specic problem with AI

systems and is covered in detail in a later section in this chapter.

Related to data quality is the question of whether more data always means

better results. Generally, with machine learning applications this is true; how-

ever, there are some subtleties to consider. ere are two, almost conicting,

perspectives.

e rst, called ‘high variance’, is where the model is too complex for the

amount of data, for example where there are many features compared to the

size of the data. is can lead to what is called over-tting and would cause

spurious results to be returned. In this case, the data scientist could reduce the

number of features, but the best solution, and the one that seems most like

common sense, is to give the model more data.

But for the opposite case, where the model is too simple to provide mean-

ingful results, called ‘high bias’, adding more data to the training set will not

deliver any improvement to the outcome. In this case adding more features,

or making the available data a higher quality, both in veracity and in delity

(such as data cleansing and outlier removal) will be the most eective

approaches to improving the outcome.

Generally, it is the role of data scientists to ensure that the data is big

enough, relevant and appropriate for the problem at hand. Although my

police example above is relatively simple, some aspects of data science can

seem more like an art form than a science. In most cases there is no clear

‘right’ and ‘wrong’ answer—the data scientist must use creativity and inference

8 What Could Possibly GoWrong?

131

to identify the best sources and mix of data. is particular skill set is one of

the reasons that the best data scientists can demand such high fees at the

moment.

Once poor data quality has been identied as an issue then there are a num-

ber of approaches to try and x (or enhance) it. Some of these could be the

traditional methods of data cleansing; crunching through the data to identify

anomalies to constraints and then correcting them using a workow system.

Crowd Sourcing, which I described in Chap. 4, can also be used as a cost-

ecient way to cleanse or enhance data sets, particularly with regard to ensur-

ing labels are correct in training data sets. Tasks, usually split down into small

units, are assigned to a distributed team of humans, who will process each one

in turn.

RPA can also be a useful tool to help cleanse data where large enterprise

workow systems are not available. e robots will need to be congured to

identify all the constraints they need to look for. Once they have found the

data errors they can then validate those with other systems to determine what

the correct answer should be, and then make that correction. RPA can be a

good solution for ‘one o’ cleansing exercises because it is relatively easy to

congure. It can, of course, be used in combination with crowd sourcing.

And, with only a slight element of irony, AI systems themselves can be used

to cleanse data for AI systems. Some vendors now have solutions which can

carry out Field Standardisation (e.g. making two dierent data sets consis-

tent), Ontology Mapping (e.g. extracting product characteristics),

De-duplication (e.g. removing entries with the same content) and Content

Consistency (e.g. identify and matching abbreviation forms). Generally, they

work by using machine learning to analyse the structure of the data model to

then determine the kind of errors such a model is likely to generate.

Another area where AI could be used to improve the quality of data, and

even generate new data, is in the use of Generative Adversarial Networks

(GANs). is is where one AI system judges the outputs from another system.

For example, the originating AI could generate a picture of what it thinks of

as a cat. e second system then tries to work out what the picture is of. e

assessment, i.e. how close is this to looking like a picture of a cat, is then used

to guide the rst system to try again and create a better picture. GANs are

relatively new, and this approach is still at the experimental stage, so I will save

a detailed description of how this works to the nal chapter.

As should be clear from much of this book, AI (in most cases) feeds o

data—without the data then there is little value that AI can add. But, as we’ve

seen in this section, more data does not always mean better results (especially

in high bias model scenarios). And it’s not just about volume of data but the

The Challenge ofPoor Data

132

quality. e veracity and delity of the data can have a huge impact on the

performance and eectiveness of the outcomes. Any data-dependent AI proj-

ect must therefore involve careful planning, ensuring the data is the appropri-

ate size and complexity for the problem that is being addressed.

Understanding theLack ofTransparency

e reason Machine Learning is called Machine Learning is, rather obviously,

that it is the machine, or computer, that does the learning. e computer does

all the hard work in ‘programming’ the model that is going to make the pre-

dictions. All it needs is the data to be trained on: feed it the data and it will

come up with the model. e human (the data scientist) has to choose the

right algorithms in the rst place and make sure the data is appropriate and

clean (see previous section), but the model that is created is essentially the

work of the machine.

I labour this point somewhat because it is an important, inherent aspect of

machine learning that is responsible for one of its biggest drawbacks—the

lack of transparency. I can ask a trained AI system to, for example, approve a

credit lending decision, or to recommend whether a candidate gets short-

listed for a job, but I can’t easilyask it how it came to that decision. e model

has looked at the many dierent features of that customer or that candidate

and, based on the many dierent features of the training set, has determined

the answer to be, in these examples, yes or no. But which features were inu-

ential in that decision, and which ones were irrelevant? We will never really

know. It’s a bit like trying to recreate an egg from an omelette.

Which, of course, can be a problem if you need to explain to that candidate

why they weren’t short-listed, or why you didn’t give that customer the loan.

And if you are in a regulated industry, then you will be expected to be able to

provide those answers as a matter of course.

ere are three general approaches to tackling AI opaqueness. e rst one

is to not use machine learning in the rst place. In Chap. 3, in the section

concerning the optimisation capability, I described Expert Systems and

Cognitive Reasoning Systems, which are particular avours of AI that work

on the principle of a human subject matter expert creating the original model

rather than it being created using data. (For this reason, some AI experts claim

that expert systems should no longer be described as AI.) e ‘knowledge

map’ that is created from these approaches can be interrogated, by a person or

via a chatbot, to access the information. In some of the more advanced

systems, the queries can be automatically routed through the map using the

specied weightings that have been applied to each node and connector.

8 What Could Possibly GoWrong?

133

is all means that the decision that comes out at the end can be traced

back through the map, making it fully auditable. So, using one of my exam-

ples above, the failed credit application could be traced to, say, 75% of the

decision due to salary, 40% due to postcode, 23% due to age and so on. And,

once it has been designed, assuming the processes don’t change, it will remain

consistent in its decisions.

e challenge in using expert systems is that they can get very complex very

quickly. e more features that the system has, the more complicated the

model becomes to dene. If the processes do change, the model then has to

be changed as well to reect them, and that can be a laborious task.

Another approach for simple problems is to use self-learning decision trees,

usually called Classication and Regression Trees. From a set of input data,

these CART algorithms create a decision tree that can then be interrogated to

understand which features have been the most inuential. CART alogorithms

generally provide the best balance between eectiveness and transparency.

For complex systems with lots of data and many features, an ‘ensemble’

approach that calls on many dierent algorithms to nd the most popular

answer (Random Forest is the most common ensemble approach), will prove

the better choice, but it will have that challenge around transparency. e

most common approach to tackling this is to try and reverse-engineer the

decision by changing just one variable at a time. Returning to my customer

who has had his loan rejected, we could repeat his case but changing each of

the features that he has fed into the system (his salary, his postcode, his age,

etc.). is trial-and-error approach will then be able to indicate (but not nec-

essarily isolate) which of the features had the most inuence on the decision.

e problem with this approach is that, with many features, this can be a

long process. Carrying out the analysis on a number of test cases and publish-

ing these rst is a good way to avoid analysing every rejected case. e ‘model

cases’ could demonstrate, for example, how, with everything else being equal,

age impacts the decision-making process. But you might have to do that for lots

of dierent model cases since the inuence of age might be dierent in dierent

postcodes or for dierent salary levels. And, if the system self-learns as it goes

along, then the test model will need to be refreshed at regular intervals.

e level of testing required will be very dependent on the type of problem

that is being solved. e more sensitive the problem (medical imaging, for

example), the more robust the testing will need to be.

A disgruntled customer who has just been rejected for that loan may, then,

be satised with this demonstration of the ‘logic’ used to make that decision.

But would that satisfy the industry regulators? Again, each use case will have

dierent requirements for testing, but it could be the case that being able to

demonstrate a fully tested system, and the inuences that dierent features

Understanding theLack ofTransparency

134

have, will be enough for some regulators. In the legal sector where e- Discovery

software is used to automatically process thousands or even millions of docu-

ments to identify those that are relevant or privileged in a litigation case, the law

rm using the software must be able to demonstrate a robust use of the solu-

tion, with due process applied at every stage. Once the court is satised that this

is the case, then the results from the analysis can be used as valid evidence.

Articial Intelligence is a new and fast developing technology—regulators

are generally slow and considered in their approach, and will need time to catch

up. Putting the risk of fraud to one side, once AI is more generally accepted in

society it may be the case that customers, and therefore regulators, take a more

relaxed approach to the transparency question. Until then, AI-adopting compa-

nies need to ensure that they have the answer when the question is raised.

The Challenge ofUnintended Bias

A common fallacy about AI systems is that they are inherently unbiased: being

machines they will surely lack the emotional inuences that inict humans in

our decision making that lead to bias, whether intended or not. But that is

just a fallacy. e core of the problem is that the data used to train the AI may

have those biases already baked in. If you use biased data to train an AI, then

that AI will reect back those same biases. And, because the model can be

opaque to interrogation (as we saw in the previous section), it can be dicult

to spot them.

Here’s a simplied example to demonstrate the problem. In the world of

recruitment, AI can be used to short-list candidates based on their CV (cur-

riculum vitae, or résumé). e AI system would be trained by feeding in many

CVs, each one labelled as either ‘successful’ or ‘unsuccessful’ based on whether

they got the job they were applying for at the time. e AI can then build up

a generalised picture of what a successful CV looks like, which it can then use

to short-list any new candidate CV that is submitted—a close enough match

between the candidate CV and the ‘successful’ model would mean the CV

gets through to the next round.

But how did those candidates in the training set get their jobs? By being

assessed by humans, with all our conscious and subconscious biases. If the

human recruiters had a propensity to reject older candidates (even if they

didn’t realise they were doing it), then that bias would ow through into the

AI model.

So, how to ensure there is no bias in the training set? e rst answer is to

try and use as wide a sample as possible. In my example above, you would try

to take training CVs from many dierent recruiters, ideally those with as

8 What Could Possibly GoWrong?

135

neutral a mix of gender, race, age and so on as possible (just as polling rms

try to use a representative sample of the population).

is may not always be possible, especially if the data is from your own

company and is all you have. But even publicly available data sets can be

prone to bias. Some of the data sets used for facial recognition training do not

have enough representative samples from people of colour. is means that

any AI trained using that data could struggle to identify non-white faces. As

these biases in public data sets are unearthed, they are being slowly xed (there

is even an Algorithmic Justice League which highlights bad practices like

these), but AI developers should be particularly cognisant of the issues with

any public data that they use.

In any of the above cases where there could be bias in the data, it will be

necessary to try and test for it. Just as in the previous section, where we looked

at testing for dierent inuences in decision making, we can similarly test for

the inuence on any particular feature for bias, as long as we know what the

‘right’, that is, unbiased, answer should be.

In the recruitment example, we would expect an unbiased response to show

that age does not inuence the short-listing decision. If we are able to isolate

the age feature in our data set then we can test, by varying only the age in our

model case, whether it is inuencing the outcome at all. As with the transpar-

ency test, this would need to be done across a range of samples (e.g. just men,

just women) to ensure that age was not inuential for any particular group.

Assuming some level of bias has been detected, the next question is what to

do about it. Finding the type of bias (e.g. age, gender) will immediately help

identify any groups of data in the training set that may be causing it (there

may have been a sub-set of the data that was from a particularly young set of

recruiters, for example). is input data can then be excluded or altered to

eliminate the source of that bias.

e model can also be tweaked by changing the relative weightings between

dierent terms. If the training CVs in my example showed a strong correla-

tion between Manager (role) and Male (gender), the mathematical relation-

ship between gender-neutral words like ‘Manager’ and gendered words such

as ‘Male’ can be adjusted. is process, called de-biasing, is not insubstantial,

and usually involves humans to identify the appropriate and inappropriate

words, but it can be done.

One important thing to bear in mind is that not all bias is necessarily bad.

ere may be instances where the inherent bias is important and where the

consequences justify the means, such as in identifying fraudsters. ere is

clearly a ne balance between representing the truth, biases and all, and alter-

ing data to represent the accepted social position. Also, on a much simpler

The Challenge ofUnintended Bias

136

level, the AI will need to understand specic denitions in areas that could be

biased, such as in the dierence between a king and a queen, if it is to work

properly.

Eliminating unintended bias from AI is, largely, still a work in progress and

must be considered carefully where there are specic consequences. Using

publicly available data sets does not, currently, ensure neutrality. Using data

from your own organisation will make de-biasing even more important, and

therefore must be built into the AI development roadmap.

Understanding AI’s Naivety

It was the French philosopher Voltaire who famously said that you should

judge a person by the questions they ask rather than the answers they give.

ese may be very wise words when it refers to humans, but for AI the situa-

tion is even simpler: the machine doesn’t need to know what the question is

in the rst place in order to give a respectable answer.

is is the whole idea behind Clustering, which I described in Chap. 3. You

can present a large volume of data to a suitable algorithm and it will nd clus-

ters of similar data points. ese clusters may depend on many dierent fea-

tures, not just a couple of things like salary and propensity to buy quinoa, but,

in some cases, many hundreds. e AI is providing mathematical muscle

beyond the capability of a human brain to nd these clusters.

But these clusters are not (or, more accurately, don’t have to be) based on

any pre-determined ideas or questions. e algorithm will just treat the infor-

mation as lots of numbers to crunch, without a care whether it represents data

about cars, houses, animal or people. But, whilst this naivety of the data is one

of AI’s strengths, it can also be considered a aw.

For big data clustering solutions, the algorithm may nd patterns in data

that correlate but are not causal. In the section on Clustering in Chap. 3 I

gave the rather whimsical example of an AI system nding a correlation

between eye colour and propensity to buy yoghurt. It takes a human to work

out that this is very unlikely to be a meaningful correlation, but the machine

would be naive to that level of insight.

e AI may also nd patterns that do not align with social norms or expec-

tations—these usually centre around issues such as race and gender. I’ve

already written in the previous section on the challenges of unintended bias,

but in this case an awkward correlation of purely factual data may naively be

exposed by the algorithm. e challenge for those responsible for that algo-

rithm is whether this is a coincidence or there is actually a causality that has

8 What Could Possibly GoWrong?

137

to be faced up to. How that is handled will have to be judged on a case-by-case

basis, and with plenty of sensitivity.

ere is also the infamous example of the Microsoft tweetbot (automated

Twitter account) that turned into a porn-loving racist. It was originally

intended that Tay, as they called the bot, would act through tweets as a ‘care-

free teenager’ learning how to behave through interactions with other Twitter

users. But it quickly turned nasty as the human users fed it racist and porno-

graphic lines which it then learned from, and duly repeated back to other

users. Tay, as a naive AI, simply assumed that this was ‘normal’ behaviour. It

only took a few hours of interaction before Microsoft were forced to take the

embarrassing tweetbot oine.

One useful way of thinking about the naivety of AI is to consider how dogs

learn. Like all other dogs, my own, Benji, loves going for a walk. I know this

because he gets excited at the rst signs that a walk might be imminent. ese

include things like me locking the back door and putting my shoes on. Now,

Benji has no idea what the concepts of ‘locking the back door’ or ‘putting my

shoes on’ are, but he does know that when these two events happen in close

succession then there is a high probability of me taking him for a walk. In

other words, he is completely naive to what the preceding events mean—they

are just data points to him—but he can correlate them into a probable

outcome.

(is dog/AI analogy is quite useful and can be extended further: my dog

is quite lazy, so if he sees me lock the back door but then put my running shoes

on, he goes and hides to make sure I don’t take him with me. In this scenario,

he is using increased granularity to calculate the outcome this time—it’s not

just ‘shoes’ but ‘type of shoes’. Of course, he doesn’t know that my running

shoes are specially designed for running, just that they are dierent enough

from my walking shoes. It may be the dierent colour/shade, a dierent smell,

the dierent place where they are kept and so on. is demonstrates the

opaqueness issue I discuss in a previous section: I have no real idea (unless I

do some pretty thorough controlled testing) what aspect of the shoes switches

the outcome from ‘Excellent, I’m going for a walk’ to ‘Hide, he’s going for a

run’, but it clearly does have a binary impact. I should also point out that the

dog/AI analogy also has its limitations: Benji has lots of other basic cognitive

skills, such as knowing when it is time for his dinner without being able to tell

the time, but because AIs are currently very specialised in their capabilities, an

AI that predicted walks would not be able to predict dinner time.)

So, the naivety of AI systems can be a real headache for its users. Suce it

to say that the outcomes from the clustering must be used carefully and wisely

if they are to yield their full value. Data scientists and AI developers must be

Understanding AI’s Naivety

138

aware of the consequences of their creations, and must apply heaps of com-

mon sense to the outputs to make sure that they make sense in the context for

which they were intended.

Becoming Over-Dependent onAI

As you will have seen from the many examples provided throughout this

book, AI can achieve some remarkable things, particularly when it is carrying

out tasks that would be impossible for humans to do, such as detecting clus-

ters of like-minded customers (or fraudsters) in databases containing many

millions of data points.

e problem may come when companies become overly dependent on

these systems to run their business. If the only way to identify your best cus-

tomers or people trying to defraud you is through very complex AI algorithms,

then there will always be a risk that these systems actually stop working eec-

tively or, probably worse, stop working eectively without you realising it.

is all comes down to the complexity of the problems that are being

solved and therefore the minimal understanding of how they are really work-

ing. I have already covered the lack of transparency of how an AI may come

to a decision, but for very complex algorithms (and one AI solution usually

contains a number of dierent types of algorithms all strung together) there

will only be a few people—the AI developers and data scientists—that under-

stand how these have been designed and built in the rst place. Being depen-

dent on a few (very in-demand) people is clearly a risk for any business. It’s

rather like the world of traditional IT where a core legacy system has been

hand-coded in an obscure language by one developer who could leave the

company, or get run over by the proverbial bus, at any time. e risk can be

compounded further if the AI system is being built by a third party who will

not necessarily have that long-term commitment to their client that an

employee might.

ere are mitigating actions though. Most of them are similar to the

approach one would take with the bespoke legacy system coder—make sure

that they have documented everything that they do, and are incentivised to

stick around to help support the system. If they won’t be around for long,

make sure there is a robust succession plan in place.

For a third party, there can be more pressure applied, through contractual

obligations, to get the solution fully documented, but the most important

thing will be to select the right provider in the rst place, which I discuss in

more detail in Chap. 9. In that chapter I also write about setting up a Centre

8 What Could Possibly GoWrong?

139

of Excellence (CoE) that will be the guardian of the necessary skills and docu-

mentation to maintain and improve the AI solutions in the future.

Another factor to bear in mind when trying to solve complex problems is

that it is dicult to know whether the system is providing a correct, or reason-

able, answer. For some AI solutions, such as measuring sentiment analysis, we

can test the output of the machine with what a human would reasonably say

was the correct answer; for example, the machine said the sentence ‘I was very

dissatised with the service I received’ would be predominantly ‘negative’ and

a human would agree with this. If there was a high volume of sentences to

assess (as would usually be the case) then a sample can be tested. But for com-

plex problems, such as trading nancial instruments or designing drugs, it is

nearly impossible to tell if the machine is making a correct decision. We can

look at the nal result (the end-of-day trading position, or the ecacy of the

drug) but we will not be able to determine if this was the best outcome of

all—perhaps more prot could have been made or a better drug could have

been designed.

ere is also a more philosophical risk from the over-dependency on AI: as

AI becomes more and more commonplace in our lives, we will eventually lose

the ability to do even the simplest cognitive tasks because we are no longer

practising those skills. Our ability to remember people’s names and ‘phone

numbers, and our ability to read maps are already being eroded by our depen-

dency on smartphones and satnavs.

As AI capabilities become greater, they are bound to impact more of our

cognitive skills. Some people may argue that this is not necessarily a bad thing,

but we have developed all those skills over many millennia for particular rea-

sons and, absent of the technology to help us, we quickly become exposed and

vulnerable.

But, coming back to a more practical basis, AI skills will, over time, become

more commoditised (just as HTML development skills have in the last

20years) and the problem of dependency on highly skilled AI developers and

data scientists will gradually recede. But for now, the risk of over-dependency

should be built into the AI strategy as soon as it is clear that the solutions will

go beyond an o-the-shelf or simple platform approach.

Choosing theWrong Technology

e eld of AI is fast moving—what was not possible in one year can be

solved in the next. All the drivers for AI that I described in Chap. 2 are all

continuing to get better and more inuential—big data is getting bigger,

Choosing theWrong Technology

140

storage is getting cheaper, processors are getting faster and connectivity

between devices is now almost innite. So, how do you choose which

approach and technology to take today, if tomorrow something better could

come along? (Users of Apple products, by the way, face this dilemma every

time they think they want to upgrade anything.)

One of AI’s constraints, that each application can only do one thing well,

is actually an advantage here. If an AI solution has been built up from a num-

ber of dierent capabilities (as per the AI Framework) then each of those

individual capabilities could be swapped out with a newer, better approach if

one comes along. Replacing one chatbot solution with another will not neces-

sarily impact any Optimisation capability you have already built. You will still

have to train the new piece of AI but you won’t necessarily have to throw away

the whole solution.

Say, for instance, that you are using one of the AI platforms exclusively to

provide all your required AI capabilities. If one of the other platform provid-

ers brings out a better AI capability for, say, text-to-speech, then it is not too

big a challenge to switch out the current API connection for the new one.

(Some providers will, of course, try to lock you into their platform through

contractual means, or even just canny sales techniques, so you will need to

watch this as you select your approach and provider.)

Bigger investments will, of course, require greater levels of due diligence.

Putting in a multi-million-pound vendor-based AI solution will certainly

commit you to a roadmap that may be dicult to step o. But that is true for

any large IT investment.

It does become more challenging if there is a fundamentally new approach

available. Established users of Expert Systems will have looked at Machine

Learning when it rst came along with some envy. But if we assume, as is

likely, that machine learning (and all its associated approaches like DNNs)

will be the fundamental core technology of AI for a long while, then it should

be a relatively safe bet to base a strategy around. (e only technology that

may have a material impact on machine learning is Quantum Computing,

but this is still very much still in the labs and will take decades to deliver prac-

tical day-to-day uses.)

Probably the bigger issue with committing to one particular AI technology

is the skills that you will need to retain to develop and support it. Generally,

individual developers will be specialists in a particular tool or platform; there-

fore, changing the tools may mean changing the developers. If these resources

are being bought in through a consultancy or implementation partner, then

you will just need to make sure that that rm has capabilities across all the

relevant tools and platforms that you may require (I cover this in more detail

8 What Could Possibly GoWrong?

141

in the Vendor Selection section of Chap. 9). If you have started to build up a

Centre of Excellence based around specic technologies though, you may

have to think hard about whether the change is worth it.

Preparing forMalicious Acts

With great power (as Spiderman’s Uncle Ben famously said) comes great

responsibility. All the capabilities that I have discussed throughout this book

have shown the huge benets that AI can bring. But that power could also be

used for nefarious means as well.

e ability for AI Clustering to, for example, identify customers that may

buy a certain product, could also be used to identify people who are ideal fraud

targets, especially if it is triangulated with other data sources. Criminal AI sys-

tems would usually have access only to publicly available data (which is always

more detailed than you think) but, as has been seen with the numerous large-

scale hacking attacks, other, more private data may also be available to them.

In early 2017, some banks introduced voice passwords for their online ser-

vices—all the customer had to do was train the system on their voice and they

could then login by simply saying ‘My voice is my password’. Within months

it could be proved that this approach can be fooled using facsimiles of the

user’s voice—the BBC showed in one example how a presenter’s non-identical

twin could log in to his account—and the worry now is that AI-powered

voice cloning technology will be able to do the same job. e AI will need

voice samplesto be trained on, but, again, these are surprisingly common

across social media, and if you are a famous person, then widely available.

ere is already a commercial mobile app, CandyVoice, which claims to be

able to clone voices.

It won’t just be banking systems that could be fooled by voice cloning tech-

nologies; you and I might think we are listening to our mother on the

telephone saying she has forgotten the password on her bank account, or our

boss asking us to send specic emails to suppliers or to make certain

payments.

Similarly, Image Recognition can be used to subvert Captchas—these are

the small images that appear when you try and make a payment on certain

websites. You can proceed only if you can type the numbers that are in the

photos, or identify only those pictures with, say, cars in. ese are meant to

avoid software robots making unauthorised purchases, but are now being

bypassed by armies of people, or clever AI systems, entering the correct

answers.

Preparing forMalicious Acts

142

e dierence between the type of online criminal behaviour we have been

used to in the past and what we see now is the scalability of the AI.Voice and

image recognition can be done at high volume and at low cost—if just a very

small fraction get through then the criminals will be successful. It’s the same

numbers game that spammers exploit.

AI can also be used to socially engineer people’s behaviours. During recent

political campaigns (Brexit in the United Kingdom is one that comes to mind)

software ‘bots’ were used to inuence people’s opinions through targeting their

social media accounts with pertinent messages. Social media companies already

use AI to change the order of the articles in a person’s social media feed so that

the most ‘relevant’ are closest to the top. External companies can use a person’s

online behaviour to predict which way they might vote and then try to rein-

force or change that using targeted posts and tweets. Extending this to include

more nefarious acts than voting is within the realms of possibility, especially if

the accounts that are being posted from are pretending to be someone else.

Articial Intelligence systems can also aid malicious behaviour by being

trained to do the wrong thing. ‘Dirty data’ could secretly be fed into a training

set to change the learnt behaviours of the AI.e large search engines are

constantly having to defend themselves against malicious companies who cre-

ate fake sites to increase their ranking in the search results.

In the earlier section on the issue of naivety in AI systems, I wrote about

Microsoft’s tweetbot, Tay. Tay didn’t understand the nastiness of the ‘data’ that

it was being fed, but Microsoft clearly didn’t expect the deliberate sabotage of

its creation by mischievous users. Although Tay is a slightly humorous exam-

ple (unless you work for Microsoft, of course), it does show how AI can very

quickly be inuenced by dirty data. Extrapolate this to a situation where the

AI is controlling millions of interactions with customers, or even nancial

transactions, and you can start to see the potential risks from this.

e xes for the types of behaviours I have described above are diverse, and

usually very technical (apart from insuring against loss, which should be done

as a matter of course). Ensuring that your voice recognition application can-

not be a victim of voice cloning will be an inherent part of its development.

With Captchas, they need to be updated on a regular basis to ensure that the

latest technology is being deployed. To stop dirty data getting in requires the

usual defensive approaches to stop anyone from inltrating your systems. e

level of defences you put up will depend on the sensitivity of the data and the

risk of anything going wrong.

8 What Could Possibly GoWrong?

143

Social engineering is more dicult to ameliorate. It is necessary to swim

against an incoming tide of more and more people who want to use, and gen-

erally trust, social media channels. e mitigating actions are awareness and

education. Just as young people (mostly) know that someone at the other end

of a messaging conversation may not be who they claim to be, people also

need to be aware that the chatbot at the other end of their chat conversation

may not be who, or what, it claims to be.

As with all online activities, being one step ahead of the criminals is the best

that can be hoped for. AI has the ability to do very good and useful things but,

as we have seen in this section, also has the potential to do bad things at scale.

Simply being aware of what can go wrong is the rst step in combating it.

Conclusion

For readers hoping for a book all about the benets and value of AI, this chap-

ter may have come as a bit of a shock. But, as I said at the start, it would have

been remiss of me not to include details of how AI can also present risks to

businesses, by being either misappropriated or sabotaged.

I deliberately included some of the worst cases of those risks, as awareness

of what can possibly go wrong will be the most important part of being able

to mitigate them. Some of these defensive steps are part of the normal IT

development cycle, some are unique to AI development and some are more

sociological. For big, complex AI projects, you will be dependent on strate-

gists, developers, security experts, data scientists and sociologists to ensure

that your application is not open to abuse or unnecessary risk.

In Chap. 10, the nal one, I will cover some of the more philosophical

debate around the impact of AI, including how it aects jobs, and the big

question of what happens when (if ever) AI gets smarter than us.

But, before that, we need to look at how your AI eorts and projects can be

industrialised and embedded into your business so that they provide sustain-

able benets for the long term.

Conclusion

144

The AI Ethicist’s View

is is an extract from an interview with Daniel Hulme, CEO and Founder

of Satalia, a London-based AI development rm with a strong ethical

mission.

AB: Daniel, rst tell me about Satalia, and how you approach AI.

DH: In this capitalistic world right now, businesses are naturally looking at AI

to either make more money or to reduce costs. Satalia helps companies do

this by providing full-stack AI consultancy and solutions, but our higher

purpose is to ‘enable everyone to do the work they love’. We think we can

achieve that by building a completely new ‘operating system for society’

that harmonises technology with philosophy and psychology. We are tak-

ing the core learnings from the solutions we have developed for our cli-

ents and then making them available as blueprints and tools for people to

use to make their businesses and lives better. Satalia is also a global voice

for purposeful AI start-ups, of which there are not enough of in this world

right now.

Within Satalia itself, our sta are free to do what they want—they set

their own salary, working hours and vacation days, and they have no

KPIs. We combine AI and organisational psychology to enable our

employees to work on the projects they want to, unburdening them

from bureaucracy, management and administration. is gives them the

freedom to rapidly innovate in ways that they would rarely nd any-

where else.

AB: Can you talk to me more about how you see the ethics of articial intel-

ligence playing out?

DH: ere are three perspectives of AI that need to be considered dierently if

we are talking about ethics.

e rst is where decisions are made through a trained but static

model. e ethical question is who is responsible if that model doesn’t

behave according to our norms—if it is racist or sexist for example. is

is the Unintended Bias problem. is is linked to the big challenge of

building explainable algorithms—how do we have transparency from

what is essentially a black box model? Now, if governments legislate for

these things, will that stie innovation? Or to stay competitive should

companies be allowed to ‘move fast and break things’ as the current trend

goes? AI developers have a real responsibility in the middle of all of this,

and breaking things without careful consideration of the impact is not

the best way forward.

8 What Could Possibly GoWrong?

145

Secondly, there are the more general AI solutions where multiple types

of AI are combined into an ever-adapting system that changes its model

of the world when in a production environment. e most famous exam-

ple of this is the Trolley Problem that designers of driverless cars are fac-

ing. Should the crashing car hit the child or the three adults, if there is a

choice? What if the car adapted its model in an unpredictable way ‘decid-

ing’ to hit as many people as possible? You might actually end up with a

Preferences setting in your car that allows you to set these choices ahead

of time.

Here’s another example: in a burning building, there is a chance to

save either a baby or a suitcase full of £1 million. Most people’s instinctive

reaction is to save the baby, but if you think about it, perhaps the best

action for society would be the suitcase of money, as you could probably

save many more babies’ lives using that money. What is the right decision

for society? We’re at an intriguing time in humanity whereby we are hav-

ing to start to codify our basic ethics.

Liability is also a big challenge with these adaptive systems. If a CEO

decides that his company should make an AI-powered machine that

administers medication to people, who is liable if that machines makes a

bad medication decision because it has learnt the wrong thing since it left

the factory? It’s almost impossible to predict how these algorithms will

behave. Just look at the Flash Crash that hit the nancial market in 2010.

e third type of AI to consider is where the machines become more

intelligent than humans—this is the Singularity. I don’t believe that we

can build ethics into a super-intelligent machine (this is the Control

Problem)—they will have to be learnt. You could, perhaps, exploit game

theory to help it learn some ethics. Most people know of the Prisoner’s

Dilemma—the winning strategy is always one of ‘tit for tat’, that is, follow

what your opponent did previously. is is the sort of ethical approach

that a super-intelligent machine could learn, that has been embedded

into humanity over thousands of years: “do unto others as you would

have them do unto you.”

Obviously, the big problem with super-intelligent machines is that

they may not be concerned with the impact on humans for whatever

plans they have, and this is perhaps one of our greatest existential risks.

One thought I’ve had is that we should perhaps help them decide to

depart our planet and leave us in peace, a bit like how we might have

made an oering to the gods in ancient times in the hope they’d have

mercy on us.

The AI Ethicist’s View

146

AB: at’s a lot of dierent ethical issues to consider, especially if the future of

the planet is at stake.

DH: We may not see the Singularity come along anytime soon but we abso-

lutely have to think about the impact of an AI-driven capitalistic society,

and to work out—both individually and collectively—how we can create

a radically better society today.

8 What Could Possibly GoWrong?

147

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_9

Industrialising AI

Introduction

is book is predominately about how to introduce AI into your business, how

to take those rst steps in understanding and then implementing prototype AI

capabilities. But at some point, you are going to want to move beyond that,

especially if you think that AI has the potential to transform your business.

So far you should have a good grasp of what the dierent AI capabilities

are, where they are being used eectively in other businesses, the best way to

create an AI strategy and start building AI prototypes, and being aware of

some of the risks involved.

is chapter then is about how to industrialise those AI learnings and capa-

bilities within your business. It focuses on how you move from a few projects

to a mature capability that can implement most, if not all, of the AI require-

ments that your business will need in the future.

e AI strategy that was discussed in some detail in Chap. 6 will have given

you an excellent starting point. It should include your AI Ambitions which

will determine how far down the industrialisation road you want and need to

go. I have assumed in this chapter that you will want to at least create a per-

manent AI capability within your organisation, one that is able to be a focal

point for AI activity, and act as a catalyst for ideas and opportunities. If your

ambitions are more or less than this, then just dial up or down my recom-

mendations appropriately.

You will also recall from the Change Management section in Chap. 6 that

there is often a ‘slump’ after the initial success of a prototype. Now is the time

to ‘go large’ and ensure that the momentum from those early wins is translated

148

into continued success. Having a robust plan for how that will happen will go

a long way to achieving that. is chapter will give you the foundations for

that plan.

Building anAI Eco-System

You may already have been working with a number of third parties to help

you build a strategy and the initial AI project builds. Some of them you may

want to continue working with on a longer-term basis; others may have just

been useful for a specic task and are no longer required. If you are going to

build a long-term AI capability though, you will need to start thinking about

how much of it will be supported by third parties, and which pieces you

intend to build up internally.

A useful way to think of this is as an ‘Automation Eco-System’. is may

include software vendors, strategists, implementation partners, change man-

agement experts and support teams, some of whom will be internal and some

of whom will be external. Some may have a bigger part to play in the early

days, whilst others will become more important once some of the projects

have been rmly established.

If you are planning to use a third party to provide any of the Eco-System

capabilities, be aware that some may be able to provide more than one; an

implementation partner, for example, may also have change management

capabilities. Whether you use both those capabilities from that provider will,

of course, be up to you. I cover some of the subtleties of this in the section on

Vendor Selection later in this chapter.

An Automation Eco-System may end up looking something like this

(Fig.9.1):

PrototypingImplementation

RPA Vendor

Assoc. Tech. Vendor

AI Vendor

Support

Strategy

Change Management

Assoc. Tech. Vendor

Fig. 9.1 AI Eco-System

9 Industrialising AI

149

Each of the capabilities can be provided internally or externally. Apart from

the software vendors, the roles can cover AI specically, or Automation

(including, say, RPA) generally. I will cover each of these roles in turn.

Strategy is area concerns the sorts of things you are reading about in this

book, particularly those in Chap. 6. ese are the activities that I, as an auto-

mation advisor, carry out for my clients. It involves building the Automation

Strategy, which will include the Maturity Matrix, the Heat Map, the Business

Case and the Roadmap. It will likely involve the technical approach (vendor

versus platform versus bespoke) and, connected to that, the approach to this

Eco-System. It can also include supporting the selection of the software ven-

dors and the other service providers. Most importantly, it should provide

thought leadership to the organisation.

As the AI market is very complex but relatively young there are very few

advisors that are able to cover the business strategy aspects as well as the tech-

nical approaches. Some of these (including myself) are able to provide proto-

typing services as well. e connection between these two capabilities is pretty

tight; therefore, it may make sense to cover both of these with one provider.

Although the bulk of the strategy work is carried out up-front, it can be

benecial to retain the strategy advisor longer term to ensure that the benets

are realised and the internal capability can be realised. Most clients who wish

to pursue a predominantly internal capability (with little dependency on

external parties) keep a light-touch external strategic involvement to provide

appropriate checks and balances.

Prototyping e building of the prototypes, pilots and PoCs requires a ex-

ible, agile approach, and follows on closely from the output of the automation

strategy (and, in some cases, can form part of the automation strategy). ere

are not (at the time of writing) many consultancies specically focused on

prototyping—this capability is generally provided by either a few of the strat-

egy advisors, or by the implementation providers.

e activities are those that are largely covered in Chap. 7 of this book,

including the build of PoCs, RATs, MVPs and Pilots. If your company is just

starting its AI journey then it makes sense to buy in this capability until you

have enough experienced and skilled resources in-house.

AI Vendor e AI Vendor could include a packaged software provider, a plat-

form provider or a vendor of some of the various development tools that are used

to create bespoke AI builds, depending on the technical approach being taken.

Building anAI Eco-System

150

For some of the platform providers, and maybe some of the development tools,

you may have existing relationships in place. No matter what arrangement

though, the software obviously sits at the core of your Automation Eco-System,

and many of the other members of that eco-system will be dependent on those

choices.

It should also be noted that some of the packaged software vendors can

provide their own implementation resources. is is usually the case for those

that have yet developed their software to be ‘implementation partner ready’.

One or two of the platform providers, such as IBM, also have large implemen-

tation service capabilities, although other third-party providers can also be

used with their platforms.

It will be likely that you will end up with a number of dierent software

vendor relationships, irrespective of which technical approach you take. Even

with a platform-based strategy, it is probable that you will need to bring in

other vendors to supplement any gaps in the platform capabilities.

Implementation e Implementation Partners are usually brought in fol-

lowing successful prototyping (unless they are carrying out those activities as

well). ey are generally concerned with the industrialisation of the AI capa-

bility and can provide a full range of development resources. e Big Four

global consultancies can provide these sorts of services, as well as a number of

their smaller competitors.

Whether you need an Implementation Partner at all will depend on how

keen you are to build an internal capability, and how quickly you want to get

there. A Strategy Advisor and a Prototyping Partner can get you a long way

towards self-suciency, but if you want to move fast (or if you are not inter-

ested in building an internal capability at all) and have deep pockets then an

Implementation Partner makes sense.

An Implementation Partner will be able to continue the work started dur-

ing the Strategy and Prototyping stages, and help build and ‘bed in’ those

applications. Depending on their capabilities they may be able to build fur-

ther applications as well as deliver some or all the Change Management

requirements.

RPA and Associated Technologies Vendor As I detailed in Chap. 4, AI is

rarely the only technology you will need to deploy to realise the full benets

available. You may also need to bring in an RPA vendor, a Cloud vendor, a

Workow vendor, and a Crowd Sourcing vendor, depending on the complexity

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of your solution and what existing relationships you already have (many

enterprises looking at AI will already have a Cloud capability, for example).

Some vendors will work better together, either technically or culturally, so

do consider this if you have a choice. Build these eco-system members around

your AI vendors, looking at who they have worked well with before and

whether there may be any particular integration challenges or not.

Your RPA vendor, should you require one, will be an important choice.

Some AI vendors have strategic relationships with RPA vendors, and some

Implementation Partners may also have ‘preferred’ RPA vendors that they

work with. In a very few examples (to date) there are software vendors that

can provide both RPA and AI capabilities, with some of these also oering

Crowd Sourcing as well. Generally though, most enterprises bring in separate

vendors for these crucial capabilities.

Change Management is is an important capability for your eco-system

and should be considered early on in the lifecycle. Many large organisations

will be able to provide a general change management capability internally, but

the specic characteristics of an AI project should be borne in mind to deter-

mine whether this will be the most appropriate approach.

e capability can be provided by a generalist third-party provider but

those that can claim to have experience in the unique characteristics of an AI

implementation will usually be preferable (the exceptions being when you

have an existing, trusted provider or you need one with experience in your

specic industry sector).

Support With this I mean the ongoing management of the developed AI

solutions. at could mean ensuring that the applications are available

through management of the underlying infrastructure and networks, or

ensuring that the application is providing accurate and meaningful results. It

could also mean the debugging of any bespoke or procured software. Each of

these activities will generally be done by dierent parties and will depend

heavily on the technical approach that you take, any existing IT policies and

the complexity of the developed solutions.

For infrastructure support, you may have passed the responsibility to an

outsourcing provider or a Cloud provider, but if you have decided to keep it

in-house your IT team may have to support some unfamiliar technologies,

including the management of high-powered GPUs and large storage arrays.

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Management of the AI applications (apart from actual code errors) could

be carried out in-house if you have built up the necessary capability, probably

in an Automation Centre of Excellence, which I describe later in this chapter.

It may well involve recruiting AI developers and data scientists, neither of

which are readily available or cheap at the moment. ere are very few provid-

ers, though, that specialise in supporting AI applications—they tend to be

either the Prototyping rms or the Implementation providers. e skills will

need to include the ability to understand the business requirements, the data

and the technology, hence the challenge in nding the perfect provider, if that

is the route you are taking.

If the procured or bespoke-build software is faulty, then it will usually be

down to the software vendor to x it—this is the classic ‘third line’ support and

should be included in any contracts between your organisation and the vendor.

I have not included Sourcing Advisors in this eco-system. ey are likely to

be a short-term requirement at the start of the journey, and therefore would

not form part of an on-going capability. I do talk more about the role of

Sourcing Advisors in the next section though.

Whether you end up creating an eco-system with these elements, or you

break it into smaller chunks, it is important to consider all the aspects I have

mentioned above. For those elements that you plan to buy-in, you will need

to have a robust selection process, which I cover in the next section.

Selecting theRight AI Vendor

To a large extent, selecting AI software vendors and service providers will fol-

low the same rules as with any IT vendor or provider, but there are some key

dierences with AI that can present some additional challenges as well as

opportunities.

One of the rst questions that will need to be answered is what exactly is

being procured. For software, the answer to this will depend on the technical

approach that has been decided upon—o-the-shelf, platform or bespoke

build (or any combination of these). ere is likely to be one piece of software

that is at the centre of most things, particularly in the early stages, and this

should be the initial focus. is could be the o-the-shelf software capability

that is required for the pilot, or the platform that the bulk of the applications

will be built out from.

Your organisation may be constrained in what software it can choose. is

may be due to the IT strategy stipulating particular criteria (e.g. it must oper-

ate as-a-service), specic standards it wants to adhere to (e.g. no JavaScript) or

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general procurement rules (e.g. third-party company must be at least two

years old). Beyond these constraints though, it is important to consider the

following when selecting AI software vendors:

• Proof of capability—because there is so much hype in the market many

vendors will exaggerate their AI credentials in order to attract attention to

themselves. Some will have only a minimal AI capability as part of their

overall solution (maybe a bit of simple NLU embedded in it somewhere)

but claim it all to be ‘powered by AI’.

Now, of course, the solution you actually require may not necessarily

need full-on AI, but if you have a specic application in mind then you will

need to make sure you can cut through the hype. e understanding of AI

capabilities that you will have gained from this book will hopefully go a

long way to helping achieve that, but you can also call on external advice as

well if need be.

An important step in being able to understand and prove the capability

of the vendor will be to have a comprehensive demonstration of the system

and, if appropriate, a small test. Tests, as with pilots and PoCs, can be tricky

for AI solutions as they generally need a lot of data and training to get them

to work eectively. If the vendor can do something meaningful here

though, do take up the oer.

• Proof of value—you should already have at least an outline business case

by this stage, but it is useful to understand how the vendor measures the

value of their solution. Some will have an approach that allows you to

quickly assess their value whilst others may expect you to do all the hard

work on this (this may, of course, mean that they will struggle to create any

value from their solution, and it should be seen as a potential warning). If

the vendor does have a model that can validate your own business case

assumptions, then do take them up on that approach. e key thing is for

the value that they can demonstrate to align as much as possible with your

own business case.

• References—taking up client references should be an obvious thing to do,

but many enterprises don’t bother (and live to regret it later). e chal-

lenge with an immature market like AI means that client references and

case studies will be thinner on the ground. How far you want to pursue

this will depend on the perceived t of the vendor’s solution to your

requirements, their uniqueness and your appetite for risk. If you want to

bring in a vendor who has no existing clients, then you may want to see

what opportunities there are for you to be a ‘foundation client’ for them—

this is covered below.

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154

• Pricing model—the commercial aspects are obviously very important when

selecting a vendor, but it’s not all about the headline price. In Chap. 6

Idescribed some of the dierent pricing models that are being used, and you

should explore which ones might be the most relevant to you, and whether

the vendor can provide them. For variable pricing, model dierent scenar-

ios, especially if it materially impacts the business case—a price per API call

may sound attractive at rst, but if your application is very popular, those

escalating costs may start to outweigh the benets. Also, seriously consider

gain-share or risk-reward models, but make sure that they are based on real

numbers that you can measure easily.

• Cultural t—although you are just buying some software, it is always

worth nding a vendor company that has a good cultural t with yours.

e AI project may be a long, and sometimes stressful, journey for both

parties, so you need to know that there is some common ground and pur-

pose between both of you. Cultural t can be assessed simply from having

a good feeling about it, but it can also be worth trying to do this more

objectively, especially if you want it to form part of your selection criteria.

• Future proong—ensuring that the software you buy will still be relevant

and working in ve years’ time can be dicult to determine, but it should

denitely be considered carefully. In the previous chapter, I talked about

the risks of selecting the wrong technology and those considerations can be

applied here. But it is probably the more traditional elements of the tech-

nology that are the ones that are most at risk of going out of date. Ask what

technologies the software is built on, and what technology standards does

it rely on. External expert advice may also be useful here.

• Foundation client—procuring software that has not been used in anger

with any other clients may seem like an unnecessary risk, but there can be

benets from taking this approach. All other things being equal (you have

tested their capability, the technology is good and future-proofed and there

is a good cultural t) then, versus a more established vendor, a new vendor

may be willing to oer you some signicant benets in return for taking

the risk of being their rst client. Usually the benets amount to heavy

discounts, especially if you also agree to promote your use of the software.

But it can also mean that you get the opportunity to shape the develop-

ment of the solution, and make it more closely match your own require-

ments. is will also ensure that you get the undivided attention of the

vendor’s best people, and that can count for an awful lot in delivering a

successful implementation.

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• Technical requirements—it should go without saying that the technology

needs to t into your current environment and align with your IT stan-

dards and strategy. But with AI you will also need to consider the data. Is

your data the right sort of data for the vendor’s solution, is it of good

enough quality and quantity? ese are key questions that must be satised

early on in the selection process.

• Professional services requirements—the nal consideration is how the

vendor’s software will be implemented. What professional services are

required, and who can provide them (the vendor, third parties or your own

organisation)? If it requires a third party, then understand how many pro-

viders out there can do it, and which ones are most familiar with the spe-

cic vendor’s software. Some vendors may have a partnership program

where they will have certied or approved service providers. If you are

already developing your eco-system, then you will need to determine how

much commonality there is between these. You will also obviously need to

evaluate the cost of the services at this stage. e selection of service provid-

ers is covered in the next section.

Selecting theRight AI Service Provider

In the earlier section concerning the Automation Eco-System I also talked

about the type of service providers that may be required, including strategy,

change management and implementation consultancies—these will also

have their unique AI-related challenges and opportunities when procuring

them in.

Each of the services capabilities you require should be considered as sepa-

rate ‘work packages’, which can be procured individually or in groups. From

the Eco-System considerations, you should have a starting point for which

capabilities need to be bought in, and how they might be grouped together.

For example, you may think that you need to buy-in Strategic Advisory,

Implementation Services and Change Management, but you think that you

will be able to handle the Support Services in-house, and you also think that

a single provider could do Implementation and Change Management. In this

example then you would be looking for two providers: one for the Strategic

Advisory work package and one to handle the Implementation and Change

Management work packages.

During the selection process (or processes) these initial assumptions should

be tested, but they do provide a useful starting point. You should therefore

provide the exibility in your Request for Proposal (or whichever procurement

Selecting theRight AI Service Provider

156

method you are using) for providers to bid for each work package separately

and combined. You may nd as you go through the procurement process that

one provider is really good at Implementation, while another is better at

Change Management—in this case you can always adjust your approach to

instead procure two separate providers for these work packages.

You should also test your in-house capability (if you have one) against the

external providers if you are not sure whether to ‘buy or build’. You can treat

them as a bidder in the procurement process and even price up the cost of

their services.

Another important consideration when looking to identify potential pro-

viders is your incumbent or strategic providers. Many large organisations have

preferred suppliers and you may have to align with these. AI is such a special-

ised technology, though, that (unless your incumbents are actually AI special-

ists) you should argue strongly to bring in providers with the necessary skills

and experience in AI.

e strategic advisory role should be your rst procurement consideration,

as many of the other decisions will be based on the Automation Strategy that

is developed with your advisor. Robust, demonstrable methodologies, inde-

pendence and (maybe most crucially) cultural t will likely be strong factors

in your selection here. For the procurement of the remaining work packages,

I think the following are the key AI-specic considerations, whether the

resources come from an established provider, software vendors or contractors

(I have used the words ‘service provider’ below to cover these):

• Experience—it may be dicult for a lot of providers to demonstrate a real

depth of experience in implementing AI, simply because the market is so

young, so you will need to bear this in mind. You will need to look for

capability in the particular approaches and tools that you have decided

upon and any relevant partnerships with software vendors (see below).

ere will be some experience that is technically specic (skills in a certain

tool, for example) and others that is more industry sector specic (e.g. abil-

ity to understand customer data for telecoms rms), and there will realisti-

cally be a compromise that has to be made between these two (unless you

are lucky enough to nd the perfect match). It may be that you have the

data knowledge already in your organisation.

• Partnerships and Independence—for the tools and approaches that you

have selected, you will want to look for a service provider that has a strong

relationship with these. Many vendors have ‘partnerships’ with providers,

which can range from a loose connection to an almost symbiotic one—it is

important to understand the level and history of those partnerships.

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In most cases the service provider will be rewarded by the vendor for

selling their software licences. But you may also want your service provider

to be independent of the software vendors, and a balance has to be struck

here. Probably the ideal arrangement is where the service provider has

strong, independent relationships with a number of vendors and will be

happy to recommend and implement any of them. If you can’t nd true

independence in the service providers then you will need to rely on your

strategic advisor to provide this.

• Cultural Fit—this is often overlooked in a service provider selection pro-

cess but, in my experience, is the one factor that causes most relationships

to break down. Because your AI projects could be quite fraught at times,

you will want to work with a provider that shares your values and objec-

tives, and has people that get on with your people. Cultural t can be for-

mally measured during the selection process or it can be assessed informally,

but it should not be ignored.

• Approach—most service providers will have methodologies, and these

should, of course, be evaluated and compared carefully. But you should

also try and look ‘under the bonnet’ (or ‘under the hood’) to see how

they may approach dierent challenges, such as testing, poor data or

biased data. eir answers should tell you a lot about their underlying AI

capabilities.

• Pricing Model—as with the software vendors, pricing is going to be

important but so is the pricing model. With service providers, there is the

opportunity to introduce ‘gain share’ or ‘risk/reward’-type approaches

where the provider will share some of the risks but also some of the gains

of the project. is helps align their objectives with yours. e challenge

with these pricing models is getting practical metrics—they need to be

general enough that they relate to business outcomes but specic enough

that they can be measured. e metrics also need to be timely—service

providers will not agree to a target that can be measured only in two years’

time, for example. Your strategic advisor, as an independent resource,

should be able to help you identify and implement practical KPIs.

• Knowledge Transfer—the nal consideration is how the service provider

will transfer all the relevant knowledge to your internal team. is assumes

that, at some point in the future, you will not want to be dependent on a

third party for your AI capability—some enterprises want to get to this

point as quickly as possible, whilst others are happy to remain dependent

for as long as it takes. e knowledge that will be transferred from the ser-

vice provider to your teams will include a range of things, and will depend

on the relationship and contract you have with them. It may involve some

Selecting theRight AI Service Provider

158

licensing of Intellectual Property (IP) from the provider (such as method-

ologies and tools), but will generally be in the form of work shadowing and

the hands-on experience gained by your team. As your resources get more

skilled, and you bring new employee resources in, the service provider can

start to back away; as you ramp up, they will ramp down.

ere is another potential third party that you may wish to engage in.

Sourcing Advisors are experienced procurement specialists that can help you

with both the vendor and service provider selection processes. ey will be

able to help create a sourcing strategy, nd appropriate companies to evaluate,

build and manage the selection process, and support negotiations.

e over-riding requirement for any sourcing advisor is their indepen-

dence. en you should look for capability in AI and in your specic industry

sector. If they do not understand the AI market well enough (it is, after all,

sprawling and dynamic) then you may want to team them up with your stra-

tegic advisor (or nd someone that can do both).

With both the vendor and the service provider selection there will need to

be a ne balance between a robust selection process and the exibility that

dealing in a young, dynamic technology requires. Be prepared to change your

plans if things don’t initially work out, or better options come along. As the

cliché goes, be ready to ‘fail fast’. And this means ensuring that the arrange-

ments you have in place with your vendors and providers are as exible as

possible, whilst still demonstrating your commitment to them. Work closely

with your procurement department to make sure that they understand the

special nature of AI projects and the demands that it may place on their usual

methodologies.

Now that we have an approach for bringing in the necessary third parties,

we need to look at the internal organisation and the skills and people that

might be needed.

Building anAI Organisation

ere are a number of things to consider when building an organisational

capability to manage your AI (and other automation) eorts, all of which will

be based on how bold your AI ambitions are. You will remember from Chap. 6,

as part of building an Automation Strategy, I stressed how important it was to

understand your ultimate AI ambitions: do you want to simply ‘tick the AI

box’, to improve some processes, to transform your function or business, or

even create new businesses and service lines? Assuming that you are some-

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where in the middle of this range, that is, you want to improve processes and

transform some areas of your business through AI, then you will need to cre-

ate a team or even a CoE to ensure that you are successful and can extract the

maximum value from your eorts.

In this section I will describe what an AI CoE could look like. Based on the

skills, capabilities and ambitions of your own organisation you should be able

to assess which elements are relevant for you, and therefore what your own

CoE might look like. I will also describe how a CoE can be integrated into an

overall company organisation.

It is important for an Automation CoE to have a mission. is will describe,

and help others understand, its purpose. You will be able to nd the words

that are most relevant to your own requirements and objectives of your com-

pany, but generally the CoE’s mission should be focused on driving the intro-

duction and adoption of AI technologies. It should act as a central control

point for assessing AI technologies and monitoring progress of ongoing proj-

ects. Perhaps most importantly, it should provide leadership, best practices

and support for projects and teams implementing AI solutions within the

business.

Two of the key inputs to determine the scale and structure of your CoE,

apart from your AI ambition, are your AI Heat Map and Roadmap. ese will

list the functions, services and processes that are being targeted for automa-

tion, and will give the priority of how they are likely to be rolled out. Particular

aspects to consider will be the dierent technologies that will need to be

deployed, the complexity of the solutions and the current state of the data

that will be exploited.

e roles that are generally included within an Automation (and particu-

larly an AI) CoE fall into four main functions: the Management of the CoE,

an overall Architecture Management, the Implementation Teams and

Operations. Some of these roles, including, particularly, parts of the

Implementation Teams, can be provided by third-party providers or vendors,

especially in the early days of the CoE.e following is a guide for how some

organisations are structuring their automation CoEs, but can obviously be

adjusted for your own needs and aligned with your company culture.

e Management Team should ideally start out relatively small, including

at least a manager and some project management capability. e manager

should have responsibility for the whole CoE and the people assigned to it, as

well as the communications across the organisation and upwards (communi-

cations could be taken by an internal communications specialist later on). For

management and control of projects, the team (which could just be one per-

son at rst) should be responsible for planning, project management, resource

Building anAI Organisation

160

tracking and reporting across all the various AI projects in the CoE.Other key

areas that usually come under this team would include the co-ordination of

training and education for both members of the CoE and users of the

systems.

Taking on the formulation of the business case and solutions are the

Architect Team. is team is best headed up by someone who understands

the business functions and processes but also the technical aspects as well. e

team will look at the opportunities for automation in the wider context (so

would have control of the AI Heat Map) and would create business cases for

each of them, carrying out the initial scoping of eort and technologies, all of

which would be done in close collaboration with the Subject Matter Experts

(or Process Owners or Business Analysts) from the Implementation Team.

e Architect Team would be responsible for managing the pipeline of

opportunities, ensuring that these are being proactively sought, and reactively

received, from across the business. ey would also include technical archi-

tects (unless this role is retained within the IT Department), data scientists (if

required) as well as managing customer experience.

e Implementation Teams are where the bulk of the CoE resources

would reside. It would consist of a number of project teams, each focused on

a specic solution. Depending on the size and complexity of each project they

could include: a project manager, a project architect, developers (who would

have responsibility for importing and training data, creating and conguring

the models), SMEs/Business Analysts/Process Owners (carrying out solution

design and validation) and Quality Assurance. If appropriate, specialist

resources such as customer experience specialists, linguists, web developers

and integration developers could also be assigned to projects.

Agile development approaches are the most suited to building AI solutions.

e specic type of Agile (e.g. Kanban, Scrum) is not that important, just

that the developers and SMEs work closely together and on fast iterations.

Because of this, and the transformational nature of AI, the members of the

Implementation Team (and the CoE generally) should have a strong mix of

both business and technical knowledge. For regulated processes, which throw

up additional control requirements, there are a number of Agile approaches

that try to cater for these, including R-Scrum and SafeScrum.

Support and maintenance of the released applications (xing bugs and car-

rying out any enhancements) can be handled in a couple of dierent ways.

For small implementations, the original project team can retain responsibility

for support, whilst for larger or more complex projects a separate support

team should be created. erefore, young or small CoEs have the support

responsibility within the Implementation Team, whereas larger, more mature

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CoEs will generally create a new team for this purpose, or have it embedded

as part of the Operations Team.

e nal group is the Operations Team. is team will have responsibility

for the deployment, testing, updating and upgrading of any of the live sys-

tems. ey will also have responsibility for the technical integration of the AI

solutions with any other systems. For organisations used to taking a DevOps

approach (where the Operations and Development resources work as inte-

grated teams), then much of this Operations Team would be subsumed into

the Implementation Team.

As I mentioned earlier, the above groups are only meant as a guide and can

be exed to match your own company’s requirements and practices. Another

consideration regarding the setting up of a CoE is how it ts into the overall

organisation structure of the company.

Some companies, such as Aviva and Clydesdale and Yorkshire Bank, have

created ‘lab’ or ‘innovation hub’ environments (the Aviva one is actually called

a Digital Garage). ese are useful vehicles to generate additional buzz around

the initiatives and to foster a culture of innovation. ey usually focus on the

early stage development of ideas. CoEs though tend to be a little more practi-

cal and will include most, if not all, of the capabilities to identify, build and

run new technology initiatives.

A key consideration, especially for larger organisations, is whether to have

a single, centralised CoE or to spread the capability across the various divi-

sions or business units whilst retaining a central control function. e

approach that usually works best for AI, at least initially, is to keep everything

as centralised as possible. is is for a couple of reasons.

Firstly, the impetus and momentum for the early AI initiatives usually

come from one area of the business (because of greatest need, biggest oppor-

tunities, most enthusiastic management, etc.), and this can form the genesis

of the CoE.When other business units see the benets that are being gener-

ated, then they can tap into the existing capability rather than creating it

themselves from scratch.

Secondly, an ‘independent’ team not involved in the day-to-day running of

the function is much more likely to identify, and be able to implement, trans-

formational change. Left to their own devices, business units rarely consider

the full range of opportunities available and will instead focus on the simpler

process improvements. Both are valid types of opportunities, but AI has much

to oer with regard to transformational change and therefore it should be

promoted at every opportunity.

A nal consideration regarding the organisational structure of your indus-

trialised AI capability is whether to appoint a senior executive to oversee it all.

Building anAI Organisation

162

Many large companies, especially in the nancial services sector, already have

Chief Data Ocers (CDO). ese roles take responsibility for the enterprise-

wide governance and utilisation of information as an asset (including in some

cases a large element of revenue generation). For some companies, such as

Chartis, Allstate and Fidelity, the CDO has a big inuence on the overall

strategy of the company.

A relatively new position that is starting to be seen in some businesses is the

Chief Automation Ocer (CAO). ese can sometimes be called Chief

Robotics Ocers or Chief AI Ocers (CAIO). is role should try to embed

automation, and AI, at the centre of the business strategy. A CAO, more than

a CDO or a CTO, can look outward and forward. ey will tend to have a

greater focus on the business opportunities of automation than, say, a CIO.

(Some analysts believe that a CAO is more suited to a move up to CEO than

a CIO might be.)

CAOs are still relatively rare, and it may be that it is a role that is needed

for a certain period of time, to get automation up and running and rmly

embedded in an organisation, and then have those responsibilities eventually

absorbed back into business-as-usual.

I am not aware, at time of writing, of any organisations with CAIOs yet,

although there is plenty of talk about the role. Again, it might be a short-lived

necessity, and many people are sceptical that this level of focus is required in

the boardroom. It may be that the CDO or CAO (if they exist in a company)

are able to cover AI specically.

Whether an organisation chooses to bring in a CDO, CAO or CAIO, the

people carrying them out need to have some pretty special capabilities: they

will need at least a reasonable understanding of the technologies and the data

infrastructure; they will need to be able to work cross-functionally, since auto-

mation opportunities can exist across the whole business and will require the

cooperation of a number of departments; they will need to behave like

entrepreneurs within the business and they will need to have the industry

standing and inter-personal skills to attract and retain the best talent. at is

not an insubstantial shopping list.

We have now come to end of the journey to understand, create and indus-

trialise AI in businesses. roughout the book, I have focused specically on

what AI can do for organisations today, and the practical approaches that can

be taken to exploit the inherent value in this technology. But now, in the nal

chapter, it is time to look to the future: how AI may develop, what new

opportunities and risks it will create, and how we can best plan to get the

most from it.

9 Industrialising AI

163

The Data Scientist’s View

is is an extract from a dialogue with Richard Benjamins, who, at the time

of the interview, was working as Director, External Positioning & Big Data

for Social Good at LUCA, part of Telefonica, a European Telecoms Company.

He is now Group Chief Data Ocer & Head of the Data Innovation Lab,

AXA, a global insurance company.

AB: In your role at Telefonica’s data-led business, LUCA, you understand the

inherent value, as well as the challenges, in using Big Data for AI applica-

tions. Can you tell me more about this?

RB: Firstly, you have to look at the numbers. Six years ago, McKinsey esti-

mated that Big Data would bring $300bn in value for healthcare and

€250bn for the European Public Sector, but in their most recent update

at the end of 2016, they reckon that the numbers were actually 30%

higher than this.

But none of that tells you how you should get the value from your own

big data and how to measure it, and that’s the biggest challenge for organisa-

tions who want to be able to realise that value. I think there are four ways

that you can get at that inherent value.

Firstly, and probably most simply, you can reduce the cost of the IT infra-

structure that is being used to manage your big data, using open-source tools

such as Hadoop. is can save signicant amounts of money, and is easy to

measure. Secondly, big data can be used to improve the eciencies of your busi-

ness, that is, enable you to do more with less. irdly, it can be used to generate

new revenue streams from your existing business—this can be a challenge

though as it is dicult to know where to start, and it can be hard to measure the

value. e nal source of value is where completely new revenue streams are cre-

ated from big data, that is, new products where data is at its centre, or by creating

insights from data to help other organisations optimise their own businesses.

I believe that in the next few years, the dominant source of big data value

will be from business optimisation, that is, by transforming companies into

data-driven organisations that take data-driven decisions.

AB: So how do you start to put a number against business optimisation

through big data?

RB: is is the big challenge. Of course, you should have a base case and com-

pare the dierence before and after, but big data initiatives are rarely the

only reason for doing something, so it’s dicult to isolate the value. ere

are two ways to help with this though. e rst is to do a segmented

experiment; for example, have one group of customers that are being ana-

lysed through big data, and another that are not (and maybe even another

The Data Scientist’s View

164

where a dierent approach is being used). e second is to do something

with big data that has never been done before– that way you can compare

the results directly with your base case.

From my experience, though, mature organisations know that there is

value in big data and will not be overly obsessed with trying to measure

every bit of value. When companies reach the stage where big data has

become business-as-usual, then it changes the way that departments interact

and the value naturally ows from this.

AB: In order to manage all of this data and value, the role of the Chief Data

Ocer (CDO) must be crucial?

RB: Denitely. We are seeing a big increase in the number of businesses that

have a CDO.According to one survey I have seen, 54% of companies

now have a CDO, up from just 12% in 2012.

Where the CDO sits in the organisation is an open question still. It’s get-

ting closer to the CEO level every year—in Telefonica for example, ve

years ago the CDO was originally ve levels below CEO but now reports

directly into that position.

e best position for a CDO to be in is where they have responsibilities

across various functions or departments, so that their role, and objectives,

are not dened by a single department. Reporting into the COO is proba-

bly a good balance as they will then have good cross-functional visibility.

AB: Big Data is obviously closely related to Articial Intelligence. Do people

get confused between these terms?

RB: e term Articial Intelligence can be used for many things including Big

Data and Machine Learning. e hype that is around AI can be useful

because it brings an increase in interest and attention but it is important

to keep in mind what we are talking about. It is not just about building

wonderful applications, it is also about asking fundamental questions

related to how people think, how they solve problems and how they

approach new situations. When thinking about AI I think it is important

to consider these three things: AI can solve complex problems which used

to be performed by people only; what we consider today as AI may just

become commodity software; and, AI may help us shed light on how

humans think and solve problems.

And remember, in the big scheme of things, this is only the beginning...

9 Industrialising AI

165

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1_10

Where Next forAI?

Introduction

is book has always been about what executives need to do today to start

their AI journey. Rightly so, but it is also important to understand what is

coming down the line. Organisations need to be prepared as much as pos-

sible to exploit any future advancements as well as mitigate any of the devel-

oping risks.

is nal chapter tries to predict the near future for AI.is is, of course,

a bit of guessing game, but hopefully it is a well-informed one. Firstly, I look

at which of the AI capabilities I have discussed in the book are likely to our-

ish and thrive the most, in other words which should be watched most closely

for signicant developments.

I then have a go at predicting when AI might become ‘business as usual’,

that is, when we are no longer talking about it as a new and exciting thing, in

a similar way that outsourcing has now become simply another tool for the

executive to deploy. is section covers some of the more general predictions

about AI and its use in business.

And nally, I close the book with some words of advice about how to

future-proof your business (hint: it includes AI).

166

The Next Opportunities forAI

In Chap. 3 I described the core capabilities of AI and then in Chap. 5 I gave

numerous examples of how those capabilities are being used in businesses

today. But the technology will inevitably get better, and the speed, accuracy

and value of these capabilities will only increase, probably at an ever-

accelerating pace.

is section provides my summary of how each of those capabilities might

develop over the next few years—some will accelerate whilst others may hit

some bumps in the road.

• Image recognition has already advanced dramatically over the last ve

years and, particularly, over the last couple. Its development over the next

few years will continue at this heady pace as more image sets become avail-

able for training purposes, and better algorithms, faster processors and

ubiquitous storage will all contribute to the ability to improve accuracy but

also tag moving images as well. ere has already been some progress in this

area but I think that we will see objects in lms, clips and YouTube movies

identied and tagged automatically and eciently. is would mean you

could, for example, search for an image of a DeLorean car across all of the

movies online, and it would return the frames in the lms where the car

appeared.

e next development beyond that will be to identify faces in movies,

just as Google Images and Apple’s Photo applications can do today for still

photos. Being an optimist I am hoping that the medical applications of

image recognition, such as in radiology, will really start to take o—they

will have a huge impact on society, and therefore deserve to be promoted.

e technique of having AI systems play (or battle) with each other,

called GANs, will enable systems to create new images from scratch. Put

simply, one system, after an initial period of training, will create an image,

and another will try and work out what it is. e rst will keep adjusting

the image until the second system gets the right answer. is technique

could alsobe used to create scenery for video games or de-blur video images

that are too pixelated.

Image recognition will have to face the challenges around bias, which

are certainly coming to the fore at the time of writing. Ensuring unbiased

image sets should be a mission for all those creating and controlling data,

and it shouldn’t become an issue only when things have clearly gone

wrong.

10 Where Next forAI?

167

• Speech Recognition, as can be evidenced in domestic devices such as

Amazon’s Alexa, Google’s Home and Apple’s HomePod, is already very

capable, although there are plenty of examples of poor implementations as

well. I predict that this capability will become much more prevalent in a

business-to-business environment, powering, for example, an HR

Helpdesk, or a room booking system.

Real-time voice transcription already works reasonably well in a con-

trolled environment (a nice, quiet oce with a microphone on a headset)

but advances in microphone technology in parallel with better algorithms

will make speech recognition viable in most environments.

In the section on malicious acts in Chap. 8 I talked about voice cloning.

As long as this technology can stay out of trouble (or the people wanting to

use it for good can keep one step ahead of the hackers) then this has the

potential to help many people who have voice-crippling diseases.

• Search capability really took o in 2017, especially in specialised sectors

such as legal. ere are a number of very capable software vendors in the

market which means that this will be a useful introduction for businesses as

they look to adopt some level of AI capability. e algorithms will inevita-

bly get better and more accurate, but I think that the future for this capa-

bility is as part of a wider solution, either by extending its own capabilities

(e.g. to be able to auto-answer emails from customers) or by embedding it

in others (as part of an insurance claims processing system, for example).

• Natural language understanding is closely connected to both Speech

Recognition and Search, and will benet from the advances there, particu-

larly in areas such as real-time language translation. But I think the hype

and elevated expectations that have surrounded chatbots will have a detri-

mental eect on their adoption. I predict that there will be a backlash

against chatbots before they start to make a recovery and become widely

adopted and used every day. ose rms that implement chatbots properly

(i.e. for the right use cases, with appropriate training, and capable technol-

ogy) will see successes, but those that try and take shortcuts will speed the

oncoming backlash.

e sub-set of NLP that focuses on generating natural language (NLG)

will see some big strides, and therefore wider adoption, over the next few

years. It will benet from broader acceptance of AI-generated reports, par-

ticularly where they add real value to people’s lives, such as with person-

alised, hyper-local weather forecasts. As more data is made available

publicly, then more ideas will surface on how to extract value from it and

to communicate those insights to us using natural language.

The Next Opportunities forAI

168

• Clustering and prediction capabilities can, for this purpose, be considered

together. Two things will happen regarding the use of big data: rstly, as

businesses become more ‘data aware’, they will start to make better use of

the data they have, and also exploit publicly available data as well; and,

secondly, less data will be needed to get decent results from AI, as algorithms

become more ecient and data will become ‘cleaner’ and (hopefully) less

biased.

Financial Services will be the main beneciary from Prediction, being

used in more and more companies (not just the global banks) to combat

fraud and up-sell products and services. Cross- and up-selling will also

become the norm for retailers.

e challenge for these capabilities will be whether they will be used

productively and ethically. Increasing the number of click-throughs and

delivering targeted adverts may make sense for some businesses, but it is

never going to change the world, and will no doubt frustrate many custom-

ers. And, of course, the questions around bias and naivety will remain on

the table and will need to be addressed for every use case.

• Optimisation, out of all the capabilities, has, for me, the greatest promise.

e work that is being done with Reinforcement Learning (that was at the

heart of AlphaGo’s victory) has enormous potential in the worlds of risk

modelling and process modelling, and specic examples where energy bills

can be signicantly reduced (as at Google’s data centre) demonstrate that it

should have broad and important applications for us all.

One of the biggest constraints on AI right now is the availability of

properly labelled, high-quality data sets. GANs, which I mentioned earlier,

have the potential to allow AI systems to learn with unlabelled data. ey

would, in eect, make assumptions about unknown data, which can then

be checked (or, more accurately, challenged) by the adversarial system.

ey could, for example, create ‘fake but realistic’ medical records that

could then be used to train other AI systems, which would also avoid the

tricky problem of patient condentiality.

But reinforcement learning and GANs are the AI technologies that

could still be described as ‘experimental’, so we may not see these being

used on a day-to-day basis for a few years yet. In the meantime, we have

systems that can make important decisions on our behalf, including

whether we should be short-listed for a job, or approved for a loan. e

main advances here, I think, will be in how they are used, rather than any

major advances in the underlying technologies. Eliminating bias and pro-

viding some level of transparency will be the key areas to focus on.

10 Where Next forAI?

169

• As we are looking into the future, it is worth considering some of the ques-

tions around Understanding, which I stated in Chap. 3 was still, as an AI

capability, rmly based in the AI labs, and may be there for a very long

time, if not forever. Having said that, a really interesting area of develop-

ment to keep an eye on is that of Continual Learning. is is an approach,

currently being developed by DeepMind, that hopes to avoid ‘Catastrophic

Forgetting’, the inherent aw in neural networks that means that a system

designed to do one thing won’t be able to learn to do another unless it

completely forgets how to do the rst.

Because the human brain learns incrementally it is able to apply learn-

ings from one experience and apply it to another, but neural networks are

extremely specialised and can learn only one task. DeepMind’s approach,

using an algorithm they call Elastic Weight Consolidation (EWC), allows

the neural network to attach weight to certain connections that will be use-

ful to learn new tasks. So far, they have demonstrated that the approach

works with Atari video games, where the system was able to play a game

better once it had been trained on a dierent game.

Although there are a number of labs looking at tackling Articial General

Intelligence, EWC is probably the most exciting opportunity we have for

getting closer to an answer to this most intriguing of challenges.

When Will AI BeBusiness asUsual?

In the previous section I talked about the developments I expect to see in the

next few years for each of the AI capabilities. In this section I want to be able

to generalise those predictions and look at the wider context around

AI.Specically, I will look at the question of when (if ever) AI will become

‘business as usual’.

e traditional way of understanding the maturing of any technology is to

see where it is on the Gartner Hype Cycle. is is a well-used graph which

plots the life of a technology from its initial ‘technology trigger’, through its

rise in popularity to a ‘peak of inated expectations’ that is then followed by a

decline into the ‘trough of disillusionment’. After that it recovers through the

‘slope of enlightenment’ and nally reaches the ‘plateau of productivity’. It is

this nal stage that can be dened as business-as-usual.

e challenge with plotting AI on this curve is that AI is many dierent

things and can be dened in many dierent ways. We could plot some of the

individual capabilities, such as NLU (heading into the trough of disillusion-

ment) or some of the technologies, such as machine learning (right at the peak

When Will AI BeBusiness asUsual?

170

of inated expectations) or GANs (as a technology trigger). To generalise

though, most of the machine-learning-based AI technologies are pretty much

peaking with regard to expectations and are therefore primed to enter the

steep slide into disillusionment.

Being in the trough doesn’t mean that everyone abandons the technology

because it doesn’t work anymore—in fact it’s usually quite the opposite.

Developments will continue apace and great work will be done to create new

applications and use cases. It’s just that the perception of the technology across

businesses and society will shift; there will be less hype, with people taking a

more sober, considered approach to AI.e best thing about being in the

trough of disillusionment is that everyone’s expectations are now much more

realistic. And that surely must be a good thing.

How long it stays in the trough will depend on a number of factors. e

four key drivers I described in Chap. 2 (big data, cheaper storage, faster pro-

cessors and ubiquitous connectivity) will only continue on their current tra-

jectory. Big data may be the one area that makes or breaks it through.

I think the greater availability of public data will be one of the main reasons

that AI is adopted by the mainstream and reaches the plateau of productivity

quicker. But there are challenges to this, as the tech giants hoard their own

proprietary data sets farmed from their billions of ‘customers’ (who, by the

way, should more accurately be called suppliers, since they provide all the data

that is used by the tech companies to sell advertising space to the advertisers,

their actual customers).

But even their data will become more dicult to farm—there is a complicit

deal between the data provider (you and me) and the user (tech giants) and

this will need to continue despite being constantly eroded by examples of

misuse or abuse or hacking. It’s clear what the tech giants need to do to keep

their supply of data coming in: there is a balance of utility, trust and transpar-

ency in the use data, and increased transparency can often compensate for

lower levels of trust.

Once we have more publicly available data and higher levels of transpar-

ency from the tech giants we can start to exploit all the connections between

these data. Rather than just having public transport data, for instance, we can

bring in our own personal data that might include our inherent preferences

for how we like to travel. (ese could be set manually, of course, but better

to pull that information from actual, real-time behaviours rather than what

we think we liked a year ago.)

is idea of hyper-localised, hyper-personalised information may well

be the key to widespread acceptance of the benets of AI.Some examples

can already be seen (e.g. hyper-localised next-hour weather forecasting,

10 Where Next forAI?

171

hyper- personalised what-to-wear recommendations) but the bringing to

together of all the dierent sources of data to provide really useful informa-

tion that is relevant to just you at that specic time in that specic location

will be, to use a cliché, a game changer.

If there is trust and transparency around the data that consumers nd useful,

then they are more likely to allow businesses open access to that, therefore

increasing the utility even further. (is is a mind-set change from today where

consumers know their data is being exploited but don’t ask too many questions

because they believe the benets are usually worth it.) And businesses will ben-

et from this as well, and can use the same ‘three-dimensional data’ approach

to help run their businesses better internally—salespeople can be much better

informed, as can couriers, caterers, HR managers, executives and so on.

So, there is a huge dependency on the levels of trust in data usage, which

will be driven by the level of transparency that there is in how the data is

farmed and used. Other areas that might delay the date at which AI reaches

the plateau of productivity could be a general backlash against the technology,

especially as the amount and types of jobs begin to be materially aected.

Right now, though, one of the biggest challenges to the widespread adop-

tion of AI is actually a shortage of skilled and experienced people. Data scien-

tists and AI developers will become superstars (with superstar salaries) as the

tech giants and AI organisations, such as OpenAI, ght over these scarce

resources. e pipeline for supplying skilled people, which includes the edu-

cation systems of the world, will take time to adapt, with quick xes such as

online training courses taking the immediate strain.

People will also make a dierence on the demand side as well. e

Millennials, as we all know, have grown up with many of these technologies

and are both familiar and dependent on them. ey will absorb new AI appli-

cations without thinking and will have fewer issues with some of the ethical

challenges I have described (although this is clearly a generalisation—you

only have to read the interview with Daniel Hulme in this book to realise that

there are people who care deeply about the dangers of AI and how it can ben-

et the world).

Customers for AI will therefore have a much bigger impact on the adoption

of the technology. It is fair to say that AI is currently a vendor-driven mar-

ket—the innovations and ideas in the market are mainly borne out of the fact

that it can be done, rather than it needs to be done. is balance will slowly

shift over the coming years as people became more familiar with the capabili-

ties of AI and there are more examples of its use in society and in the

workplace.

When Will AI BeBusiness asUsual?

172

ings like speech recognition, and particularly voice authentication, have

already become part of some people’s normal lives as they become familiar

with, and then maybe dependent on, their Google Home, Amazon Alexa or

Apple HomePod devices. Once this technology enters the business

environment, in, say, an HR Helpdesk, we will start to see the beginnings of

AI becoming ‘business as usual’.

e ‘fully connected home’ dream that AI product vendors try and sell cur-

rentlyfalls far short of expectations—there are lots of dierent products and

standards out there which rarely work together and can be dicult to set

up—but one can see signs that the seamless and eortless use of AI to help

run our homes is on the horizon. So when people go to work, they will expect

the same sort of experience, and this will drive further developments, and the

wider acceptance, of AI in business.

One of the most interesting developments I am starting to see is the cre-

ation of ‘vertical’ AI solutions, where a number of dierent AI (and associated

technology) capabilities have been brought together to solve a particular chal-

lenge in a particular domain, such as nancial services, or HR, or healthcare.

Many of the AI ‘solutions’ being developed right now fall short of being com-

plete solutions—they just look at a very specic capability or service—and

these will quickly become commoditised or embedded into existing enterprise

systems. ‘Full stack’ vertical solutions, however, will have AI at their core, and

will focus on high-level customer requirements that can be better met using

AI, or will be used to discover new requirements that can be satised only by

using AI.ese solutions will take a much more holistic approach and draw

upon subject matter expertise just as much as they do technical know-how. In

my mind, once we start to see more and more of these types of solutions, the

quicker AI will achieve business-as-usual status.

Another bellwether for AI’s acceptance onto the plateau of productivity

will be how business’s organisational structure changes to accommodate

AI.ere are already a number of enterprises that have created Automation

(and AI) Centres of Excellence (I discussed these in Chap. 9) and these will

become much more commonplace in the next few years. Full acceptance

though will come when enterprises no longer need these CoEs—when AI is

seen as the normal way to do business. By this point we won’t have AI special-

ists—everyone will be a generalist with AI skills. Just as in the 1970s there

were people who had jobs ‘working the computer’ and now everyone just

‘works with computers’, so the same will be true for AI.

ere is a slightly tongue-in-cheek denition of AI which says it is ‘any-

thing that is 20years in the future’, but there is probably some truth in that.

e AI capability that we have now and use every day would have seemed like

10 Where Next forAI?

173

black magic 20years ago, yet we don’t really consider it to be AI anymore. We

are always looking forward for the new shiny thing that we can label AI.So it

is almost a self-defeating exercise to try and predict when AI will become busi-

ness as usual. I have, though, tried in this section to identify those things that

will signal when AI (as we consider it today) has reached the plateau of pro-

ductivity. But if your business is waiting until then before looking into or

adopting AI then you will be too late. e time to start your AI journey is

now: to future-proof your business so that you can defend against, and pre-

pare for, the new AI-driven business models and opportunities that will inevi-

tably come.

Future-Prooﬁng Your Business

In the very rst chapter of this book I implored you to ‘not believe the hype’.

Over the subsequent nine chapters I have hopefully provided you with a prac-

tical guide on how to approach AI so that you are as informed and prepared

as you can be. I also hope that you are now as excited as I am about the ben-

ets that AI can bring to business and society, and that you can see how AI

could be benecial to your company.

But the most important thing I want you to have taken away from this is

that every company, every senior executive, needs to be thinking about AI

now, today. e development of this technology is already moving apace but

it will only get faster. ose who wait will be left behind. Now is the time to

start future-proong your business to capture the full value from AI.

ere are three broad phases to this: understand the art of the possible,

develop an AI strategy and start building stu.

Understanding the art of the possible is the rst stage in future-proong

your business. By reading this book you will have taken an important step

forward, but you should also be reading many of the very decent books and

resources that are also available, many of them for free on the internet. ere

are, of course, signicantly more poor resources on the internet than there are

decent ones, but sites such as Wired, Quartz, Aeon, Disruption, CognitionX,

Neurons, MIT Technology Review, e Economist, e Guardian and e

NewYork Times make very good starting points. You should also consider

attending conferences (there are now a plethora of automation and AI confer-

ences available, some of which you may nd me speaking at) and also signing

up for AI masterclasses and/or boot camps.

e ‘art of the possible’ should, of course, be grounded with some level of

realism. erefore, it is important to understand what other enterprises are

Future-Prooﬁng Your Business

174

doing, particularly those in the same sector or with the same challenges.

Advisors can be useful for this as they (we) tend to have further insights than

those that are available publicly. Advisors will also be able to challenge you on

what can be done and to help you ‘open your mind’ to all the available

opportunities.

Some useful thought experiments that can be done at this stage include

imagining what a company like yours might look like in 10 or 20years’ time,

or to try and imagine how you would build a company with the same business

model as yours from a blank sheet of paper—what are the core things you

would need, what could you discard and what could be delivered through

automation? Holding ‘Innovation Days’ can also work—these would bring

together stakeholders from the business to listen to the art of the possible

(from experienced third parties) and have demonstrations from AI vendors of

their solutions. is should then spark new ideas that can be fed into the AI

Strategy.

Following on from the initial thinking, the next phase will all be about

building the AI Strategy. A good chunk of this book describes this process in

detail, so I won’t repeat myself here. Suce it say that the strategy should be

built around identifying and addressing existing challenges, as well as identi-

fying new opportunities that can be addressed by AI.

Creating and delivering innovation within any business is never an easy

ride. It takes time and eort, with planned investments and a clear mandate.

For AI, you could create a small working group with that specic focus, or

you could sit on the back of any innovation capability you may already have

in-house. e risk with the latter of these is that you may end up with the

‘lowest common denominator’ and miss out on some of the bigger AI oppor-

tunities. e advantage is that you can call upon additional resources and

other technologies that may be instrumental to your solutions.

e important thing to remember about any program to implement inno-

vation, but particularly with AI, is that it will require a cultural shift. People

will need to think very dierently, and they will be challenged about many of

their core beliefs of how a business should be run. Be prepared for some heavy

discussions.

e nal phase to start future-proong your business is to build some AI

capability. Based on your AI Strategy, building some prototypes will be the

rst real test for AI, and the rst time that many people will have seen AI in

action in your business. is act of disruption will have a profound aect and

will be a great catalyst to start new conversations across the business. Ensure

that you make the most of this opportunity to showcase AI and bring the rest

of the business along with you.

10 Where Next forAI?

175

You should always be ready for new developments in the technology. AI is

always getting better—advances that we weren’t expecting for decades are sud-

denly with us, and you need to be informed and ready to bring those into

your business if they are relevant. Having an AI, or automation, CoE, perhaps

backed up with external advice, will ensure that the market is being constantly

scanned and all potential opportunities are captured as soon as possible.

Some businesses, once they have some prototypes under their belt, take the

disruptive power of AI to the next level and build parallel digital businesses.

ese in eect compete with the legacy business, and are therefore not encum-

bered by existing systems or culture. Even if you do not create a new business

unit, this healthy competitive mind-set can be useful to bring out the full

potential of the technology.

Final Thoughts

is has been a fascinating book to write, and I hope that you have, at least,

found it an interesting and thought-provoking read. I’d like to think that you

are now much better prepared and informed to start your AI journey with

your business.

at journey won’t be an easy one—disrupting any business with new tech-

nology has more than its fair share of challenges—but the rewards should

certainly be worth it. Be very aware that all your competitors will be consider-

ing how AI can help their businesses, but the advantage will be to those who

actually start doing something about itnow. As I said in the previous sections,

if you are waiting for a mature and stable AI market then you will be too late.

ank you for spending your precious time in reading my book. If you

need any further help or advice, then you know where to nd me. But for

now, put down the book, and go start your AI journey.

Final Thoughts

177

A. Burgess, e Executive Guide to Articial Intelligence,

https://doi.org/10.1007/978-3-319-63820-1

NUMBERS AND SYMBOLS

1-800-Flowers, 76

Advanced Research Projects Agency

(ARPA), 12

Ageas, 80

AIA, 78, 79

Alexa, 2, 17, 27, 34, 39, 76, 167, 172

Alexander Mann, 86

algorithms, 1, 2, 6, 9, 14, 18–20, 26,

27, 36, 43, 45, 47, 50, 54, 57,

58, 68, 83–5, 114, 119, 124,

125, 129, 132, 136, 138, 144,

145, 166–9

Amazon, 2, 16–18, 27, 34, 56, 57, 68,

72, 76, 119, 167, 172

ambitions, AI, 100, 106, 113, 147,

158, 159

Apple, 17, 27, 140, 166, 167, 172

Associated Press (AP), 82

automation, 2, 3, 5, 6, 24, 53, 58, 71,

85, 91–4, 96–8, 100, 107, 108,

110–13, 119, 123, 149, 158–60,

162, 173–5

Axa, 84, 163

Baptist Health South Florida, 81

Barclays, 60, 77

Baxter, 64

benets, 1, 6, 18, 22, 30, 37–9, 41, 50,

51, 59, 60, 63, 64, 70, 73, 77,

78, 80–2, 84, 87, 89, 92–5, 98,

100, 102, 103, 105–9, 112, 113,

129, 141, 143, 149, 150, 154,

161, 167, 170, 171, 173

Berg, 87

Berkeley, 18

bias, 27, 47, 48, 77, 86, 87, 125,

130, 131, 134, 136, 144,

157, 166, 168

big data, 3, 11, 13–15, 19, 25–7, 41,

56, 66, 87, 95, 114, 125, 129,

136, 139, 163, 164, 168, 170

Brown, 18

business case, 27, 73, 98, 103,

105–13, 115, 118, 149,

153, 154, 160

Care Quality Commission (CQC), 87

Celaton, 70, 78, 79, 88, 89

Index

178 Index

change management, 8, 89, 110,

112, 113, 120, 147, 148,

150, 151, 155, 156

chatbots, 5, 12, 39, 45, 57, 63,

74–8, 82, 83, 93–5, 97, 101,

102, 115, 132, 140, 143, 167

cloud, 16, 18, 19, 26, 52, 55–8, 76,

91, 114, 119, 150, 151

clustering, 3, 4, 37, 38, 41, 46,

51, 66, 93, 95, 108, 136,

137, 141, 168

Clydesdale and Yorkshire Banking

Group (CYBG), 77, 161

Cognitiv+, 53

cognitive reasoning, 6, 43, 62, 74,

80, 101, 107, 114–16, 124

consultancy, AI, 144

Cortana, 2, 39

crowd sourcing, 55, 67–9, 102,

125, 131, 150, 151

Customer Satisfaction (CSAT), 93,

103, 107

customer service, 4, 18, 24, 30, 36,

52, 61, 63, 73–8, 84, 89, 93,

99, 101, 102

DARPA, 12

data, 1, 13, 30, 55, 74, 95, 118,

129–32, 152, 166

data scientists, 23, 54, 57, 119–21,

124, 130–2, 137–9, 143,

152, 160, 163, 171

Davies Group, 79

DeepMind, 42, 45, 50, 66, 83, 169

Deep Neural Networks, 8

Deutsche Bank, 81

developer, AI, 3, 18, 23, 55–7,

135, 137–40, 144,

152, 171

Durham, Police Force, 86, 130

eco-system, 148–52, 155

expert systems, 11–13, 41, 43, 124,

132, 133, 140

Facebook, 13, 15, 16, 32, 45, 83

Farfetch, 87

framework, AI, 3, 4, 7, 29–54, 57, 73,

101, 113, 120, 140

GANs. See Generative Adversarial

Networks (GANs)

general articial intelligence, 4, 23, 48,

56, 65, 70, 73, 145, 165, 169

Generative Adversarial Networks

(GANs), 45, 131, 166, 168, 170

Goldman Sachs, 85

Google, 2, 13–15, 18, 32, 40, 42, 50,

57, 66, 68, 83, 84, 86, 94, 119,

166–8, 172

graphical processing units (GPUs), 17,

26, 57, 151

hacking, 141, 170

heat map, AI, 100–6, 112, 113, 159, 160

Hidden Markov Model (HMM), 34

HMM. See Hidden Markov Model

(HMM)

HSBC, 77, 86

IBM, 16, 18, 19, 25, 26, 32, 75–7, 80,

119, 120, 150

image recognition, 3, 7, 20, 32, 33, 35,

40, 65, 79, 80, 96, 125, 141,

142, 166

179 Index

infrastructure, 26, 53, 57, 63, 76, 82,

83, 103, 119, 151, 162, 163

Internet of ings (IoT), 55, 65–7,

72, 87, 91, 97

IoT. See Internet of ings (IoT)

London School of Economics, 26

machine learning, 3, 7, 8, 13–15,

19–22, 25, 29, 32, 39, 41, 43,

54, 62, 85, 114, 124, 130–2,

140, 164, 169, 170

machine translation, 40

Maturity Matrix, AI, 96, 97, 99,

100, 105

McKesson, 81

Minimum Viable Product (MVP),

122, 123, 149

MIT, 5

MNIST, 15

MVP (see Minimum Viable Product

(MVP))

naivety, 47, 48, 136, 142, 168

narrow articial intelligence, 4, 23, 70

Natural Language Processing (NLP),

35, 51, 54, 64, 107, 167

Natural Language Understanding

(NLU), 2–4, 25, 31, 33–5,

38–41, 51, 54, 56, 57, 64, 65,

74, 76, 78, 81, 82, 87, 114, 167

Netix, 76, 96

neural networks, 8, 11, 13, 21,

22, 50, 169

neurons, 8, 20

Nexar, 80

NLP. See Natural Language Processing

(NLP)

NLU. See Natural Language

Understanding (NLU)

North Face, 77

NVidia, 17

opaqueness, 23, 47, 132, 137

optimisation, 3, 4, 9, 19, 25, 30,

31, 41–6, 51, 63–6, 74, 79,

86, 93, 95, 97, 102, 108,

132, 140, 163, 168

Otto, 85

pilots, 12, 92, 122, 123, 149, 152, 153

Pinsent Masons, 82

planning, 3, 5, 41, 46, 51, 79, 107,

132, 148, 159

platform, AI, 18, 117–21, 140

PoC. See proof of concept (PoC)

prediction, 3, 4, 7, 31, 37, 46–8, 51,

54, 62, 83, 84, 95, 108, 130,

132, 165, 168, 169

processors, 16, 17, 19, 20, 22, 25, 30,

57, 140, 166, 170

proof of concept (PoC), 86, 91, 92,

115, 122, 123, 149, 153

prototypes, 89, 92, 111, 112, 117,

122–4, 147, 149, 174, 175

Rainbird, 114, 115

RAT. See Riskiest assumption test

(RAT)

reasoning, 95

reinforcement learning, 8, 42, 44,

45, 168

180 Index

Reink Robotics, 64

riskiest assumption test (RAT),

122–4, 149

risks, 1, 2, 26, 27, 33, 39, 48, 53, 54,

60–2, 65, 73, 75, 79, 81, 84, 86,

87, 93, 95, 98, 103, 106, 107,

118, 123–5, 129, 130, 134, 138,

139, 142, 143, 145, 147, 153,

154, 157, 162, 165, 168, 174

roadmap, AI, 9, 92, 111–13, 136, 159

robotic process automation (RPA), 8,

27, 35, 36, 52, 55, 58–63, 76,

79, 91, 93, 95, 97, 98, 100, 102,

107, 108, 119, 131, 149, 150

robotics, 60, 64, 65, 83, 97

robust, 152

ROSS, 25

Royal Bank of Scotland (RBS), 75, 77

RPA. See robotic process automation

(RPA)

Rue La La, 85

Satalia, 144

search, 3, 7, 13, 14, 25, 26, 32,

35, 36, 38, 46, 59, 62, 79,

81, 82, 93, 95, 97, 106,

107, 142, 166, 167

SEB, 75, 76, 83

selection, 19, 47, 77, 118, 141,

148, 149, 152, 154–8

self-service, 63, 93, 95, 102, 108

Sensai, 126

service providers, 149, 152, 155–8

Siri, 2, 17, 27, 39

Slaughter & May, 82

speech recognition, 2–4, 7, 15, 18,

31, 33–5, 38, 39, 51, 56, 64, 65,

81, 95, 102, 107, 108, 167, 172

Stanford, 19, 64

Stanford Articial Intelligence Lab

(SAIL), 11

Staples, 76

Stitch Fix, 77

storage, 16, 19, 22, 25, 32, 56, 140,

151, 166, 170

strategy

AI, 9, 91, 92, 96, 100, 110, 112,

113, 117, 120, 121, 125, 126,

139, 147, 148, 174

automation, 91–4, 99, 105, 123,

149, 156, 158

business, 91–4, 100, 112, 113, 127,

149, 162

supervised learning, 7, 20, 32, 34, 35,

39, 44, 45, 51, 68

Telefonica, 163, 164

TeliaSonera, 83

training, data, 7, 14, 15, 18, 23, 30,

34, 47, 68, 75, 86, 123–5, 131

transparency, 8, 27, 87, 132, 135, 138,

144, 168, 170, 171

UC San Diego Health, 81

UK Meteorological Oce, 82

Under Armour, 78

understanding, 3, 4, 9, 11, 23, 29–31,

40, 41, 47–50, 71, 74, 83, 87,

88, 94–6, 101, 102, 110, 111,

123–5, 132–4, 136–8, 147, 153,

162, 169, 173

unintended bias, 47, 48, 130, 134–6,

144

unsupervised learning, 7, 20, 29, 30,

32, 37

USAA, 84, 86

181 Index

vendors, 2, 9, 19, 36, 52, 70–2, 89, 96,

114, 117–20, 131, 148–50,

152–9, 167, 172, 174

Virgin Trains, 78, 88

Watson, 18, 19, 25, 26, 32,

33, 75–7, 80

Winter, AI, 11–13, 17, 19,

22, 23, 126

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