The Executive Guide to Artificial Intelligence

THE EXECUTIVE GUIDE TO

ARTIFICIAL INTELLIGENCE

How to identify and implement applications

for AI in your organization

ANDREW BURGESS

THE EXECUTIVE GUIDE TO

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

© The Editor(s) (if applicable) and The Author(s) 2018

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Andrew Burgess

AJBurgess Ltd

London, United Kingdom

This 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 different 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 flat or a bungalow. Somewhat impractical, but a great learning experi￾ence for me, particularly in understanding how AI is different 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 Things, cloud services, storage, ubiquitous

connectivity and much more. These 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.

The 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). Things 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 finally 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 effectiveness 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 different.

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. This 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

five 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. This 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

ix

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, The Law Society

Sarah Clayton, Kisaco Research

Dana Cuffe, 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

xi

Contents

1 Don’t Believe the Hype 1

2 Why Now? 11

3 AI Capabilities Framework 29

4 Associated Technologies 55

5 AI in Action 73

6 Starting an AI Journey 91

7 AI Prototyping 117

8 What Could Possibly Go Wrong? 129

9 Industrialising AI 147

10 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 The 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 first pass 102

Fig. 6.4 AI heat map 104

Fig. 9.1 AI Eco-System 148

© The Author(s) 2018 1

A. Burgess, The Executive Guide to Artificial Intelligence,

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

1

Don’t Believe the Hype

Introduction

Read any current affairs newspaper, magazine or journal, and you are likely to

find an article on artificial 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 benefit 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.

Artificial 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

effectively. 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.

2

Artificial 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 different

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

Many of those efforts 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 find all the relevant information without resorting to fanciful articles,

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

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

People generally experience AI first 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; find 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.

Artificial 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 efficiently 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.

Those 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. That 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 efficient and effective 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

3

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 specific terminology in articles that

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

confused than they were before. Terms like Artificial Intelligence, Cognitive

Automation and Machine Learning were being used interchangeably, despite

them being quite different things.

Through 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.

The first four of these are all to do with capturing information—getting

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

the most mature today. There 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 efficiently 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).

The 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. They 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? The 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

4

Once we get to Understanding, it’s a different 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 different 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 specific (these are usually termed Narrow AI) but Understanding

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

Artificial 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 first 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). That’s a fairly involved process that

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

being at all. The 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 efficient and effective 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.

The 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 efficient? 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