Table Of ContentMost organizations that are just starting out in AI believe that the key to success lies in
advanced algorithms or Big Data. Our consulting experience shows that this philosophy is
flawed: sophisticated technology is never a free pass for business success. As AI technology
becomes more widely available, the real leaders of the upcoming AI wave won’t be Data Scien-
tists or researchers. Instead, the keys for long-term impact are in the hands of the enlightened
group who understands business and at the same time speaks enough technology to develop a
consistent and achievable AI vision for their organization.
Domain AI
expertise knowledge
The protagonists of the next
chapter of the AI era
Zero to AI
A , -
NONTECHNICAL HYPE FREE GUIDE
AI
TO PROSPERING IN THE ERA
GIANLUCA MAURO AND NICOLÒ VALIGI
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“The future is already here—it’s just not very evenly distributed.”
—William Gibson
brief contents
1 ■ An introduction to artificial intelligence 1
P 1 U AI .......................................................11
ART NDERSTANDING
2 ■ Artificial intelligence for core business data 13
3 ■ AI for sales and marketing 36
4 ■ AI for media 68
5 ■ AI for natural language 91
6 ■ AI for content curation and community building 119
P 2 B AI...............................................................137
ART UILDING
7 ■ Ready—finding AI opportunities 139
8 ■ Set—preparing data, technology, and people 167
9 ■ Go—AI implementation strategy 185
10 ■ What lies ahead 210
v
contents
preface vii
acknowledgments xv
about this book xvii
about the authors xxi
1 An introduction to artificial intelligence 1
1.1 The path to modern AI 2
1.2 The engine of the AI revolution: machine learning 4
1.3 What is artificial intelligence, after all? 6
1.4 Our teaching method 8
P 1 U AI..............................................11
ART NDERSTANDING
2 Artificial intelligence for core business data 13
2.1 Unleashing AI on core business data 14
2.2 Using AI with core business data 15
The real estate marketplace example 16 ■ Adding AI capabilities to
FutureHouse 18 ■ The machine learning advantage 22
Applying AI to general core business data 24
vii
viii CONTENTS
2.3 Case studies 25
How Google used AI to cut its energy bill 25 ■ How Square used AI
to lend billions to small businesses 29 ■ Case studies lessons 32
2.4 Evaluating performance and risk 33
3 AI for sales and marketing 36
3.1 Why AI for sales and marketing 37
3.2 Predicting churning customers 38
3.3 Using AI to boost conversion rates and upselling 42
3.4 Performing automated customer segmentation 44
Unsupervised learning (or clustering) 45 ■ Unsupervised learning
for customer segmentation 49
3.5 Measuring performance 52
Classification algorithms 52 ■ Clustering algorithms 55
3.6 Tying ML metrics to business outcomes and risks 56
3.7 Case studies 58
AI to refine targeting and positioning: Opower 58 ■ AI to
anticipate customer needs: Target 64
4 AI for media 68
4.1 Improving products with computer vision 69
4.2 Using AI for image classification: deep learning? 73
4.3 Using transfer learning with small datasets 76
4.4 Face recognition: teaching computers to recognize people 78
4.5 Using content generation and style transfer 81
4.6 What to watch out for 83
4.7 AI for audio 84
4.8 Case study: optimizing agriculture with deep learning 85
Case questions 88 ■ Case discussion 88
5 AI for natural language 91
5.1 The allure of natural language understanding 92
5.2 Breaking down NLP: measuring complexity 93
