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1. AI in the Enterprise
Real Strategies for
Artificial Intelligence
JUNE 12, 2018
BY SUSAN ETLINGER
RESEARCH REPORT
PREVIEW VERSION

2. 1
Table of Contents
2 Executive Summary
3 AI: Where We Are Now
5 Use Cases for Artificial Intelligence
8 Stripe Case Study: Improving Customer Experience While Preventing Fraud
14 DBS Case Study: AI Supporting Digital Transformation
17 Recommendations
20 Endnotes
22 Methodology
23 About Us
23 How to Work With Us

3. 2
Executive Summary
For enterprise companies considering investing in AI and
implementing AI applications, the current landscape can
seem overwhelming. Companies like Amazon, Facebook,
Google, Apple, and Microsoft dominate the news, but
how applicable are their strategies to companies with
vastly different business models? This report examines
the real use cases, challenges, and opportunities of AI for
organizations. It includes interviews with executives from
large, well-known companies and start-up entrepreneurs
who are envisioning the many ways that machine
intelligence can fuel innovation and growth. Finally, the
report offers recommendations for companies thinking
about where to focus, how to build their partnership
ecosystem, and how to measure value in the short and
long term as AI becomes a critical driver of
digital transformation.

4. 3
AI: Where We Are Now
Progress toward developing and utilizing Artificial Intelligence (AI) has been uneven
in the years since Alan Turing asked the question, “Can machines think?” But after
seven decades of research and sporadic progress, AI is finally coming into its own. The
availability of massive amounts of data, relatively inexpensive parallel processing, and
improved algorithms have sparked technology and market momentum that is different
from the critical yet discrete breakthroughs of the past.
Against this backdrop, investment in AI continues to escalate, with explosive growth
expected during the next several years. Industry research firm IDC expects global
spending on cognitive and AI solutions to achieve a Compound Annual Growth Rate
(CAGR) of 54.4% through 2020, when revenues will exceed $46 billion. Statista forecasts
that the global AI market will reach $59.75B by 2025.1
This combination of innovation, experience, and investments means that AI is poised to
gain momentum and become a core driver of growth in the enterprise.

5. 4
DEFINING ARTIFICIAL INTELLIGENCE
Defining exactly which technologies comprise “AI” can be a contentious process. This
report defines AI in a business context as follows:
Artificial intelligence refers to a set of technologies that enable machines to reproduce
certain types of human capabilities; for example, the ability to see, listen, speak, move,
reason, decide, predict, act, and — most importantly — learn from past experience.
Today, AI is used in text messaging, search, eCommerce, social media, and in vertical
industries from heavy manufacturing to financial services, healthcare and retail.
Sometimes we are aware of its presence — as with Alexa or an autonomous car —
and other times we aren’t, because it’s working behind the scenes in websites, apps,
messaging, search, and a range of other tools and services.
RECENT TECHNOLOGY ADVANCEMENTS
Driven by research and experimentation in academia, startups, and large companies,
advancements in AI continue to accelerate in many notable ways, including the following:
These advancements hint at the ways that AI will evolve further in the future. As part of
this evolution, it is important to understand the different types of AI and how they are
being implemented across industries globally.
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Language Understanding and Translation
In 2016, researchers at Microsoft announced that their speech
recognition technology had achieved human parity; that is, the ability
to transcribe speech about as well as a human. In March 2017, Microsoft
announced a similar breakthrough in Chinese-to-English translation.2 In
May 2018, Google demonstrated its “Duplex” technology, which enables
a voice agent to conduct a naturalistic conversation over the phone. AI-
based systems will eventually be able to recognize, translate, and speak
or chat in any language at any time on any device.
Strategy and Decision-Making
DeepMind unveiled AlphaGo Zero, the first computer program to defeat
a world champion at the ancient Chinese game of Go.3
Maturing Deep Learning Frameworks
Deep learning frameworks, such as TensorFlow, which enable developers
to build AI-enabled systems more quickly, consistently, and scalably, are
becoming more accessible and user-friendly. This lowers barriers to entry
for programmers to experiment with and use AI for a broader range of
business use cases.4

6. 5
Use Cases for Artificial Intelligence
Although artificial intelligence enables machines to reproduce certain types of human
capabilities or “intelligences,” it’s critical to remember how machines fundamentally differ
from human beings. Today, at least, machines excel at certain things (computation and
pattern matching, for example), but lack the attributes we think of as innately human
(feelings, context, values). To approximate these attributes in machines, we must “train”
them with massive amounts of data so they can recognize objects, languages, and
relationships and understand things that humans take for granted.
While some futurists believe in the idea of a “technological singularity” in which machines
will overtake the capabilities of humans, the question today is more pragmatic: How can
we use AI? What is it good for and not as good for? Where are the real opportunities and
use cases? What are the moon shots? How will it impact customers, employees,
and shareholders?
To assess the types of use cases that AI is best equipped to handle, it’s important to
think about how human intelligence and computer intelligence compare to each other.
“The Theory of Multiple Intelligences,” proposed in 1983 by developmental psychologist
Howard Gardner, offers a useful guide.
Rather than thinking of intelligence as a single attribute, it proposes nine specific types of
intelligence that humans possess:5
1. Logical-mathematical (number/reasoning smart)
2. Naturalist (nature smart)
3. Musical (sound smart)
4. Bodily-kinesthetic (body smart)
5. Linguistic (word smart)
6. Spatial (picture smart)
7. Interpersonal (people smart)
8. Intra-personal (self smart)
9. Existential (life smart)

7. 6
What Gardner refers to as “logical-mathematical” intelligence is the first thing we think
of when we think about machine intelligence: the ability to classify data, process it,
draw inferences from it, and even make decisions. But machines can also “see” (using
computer vision); listen (using Natural Language Understanding [NLU] technologies);
walk, run, jump, and fly (using robotics); communicate (using audio and NLU); detect and
interpret environmental changes (using sensors and analytics); and a host of other things.
The key is to use these capabilities in a way that is valuable to people and businesses.
Shivon Zilis’ “Machine Intelligence 3.0” is an excellent framework for understanding the
AI technology landscape (see Figure 1).6 On the left are applications enabled by AI and, on
the right, the continuously evolving technology stack.
Figure 1: Machine Intelligence 3.0, by Shivon Zilis, Bloomberg BETA
As one would expect with such a new and dynamic market, these applications are
maturing and proving value in different ways and at different rates. From an enterprise
point of view, however, there are three primary areas that are broadly applicable to
enterprise-class companies today:7
Enterprise Intelligence:
AI that can classify, predict,
analyze, recommend, act, and
learn based on visual, audio,
sensor, internal (enterprise), and
market (macro) data.
Computer Vision:
AI that can “see” and, in
conjunction with enterprise
intelligence, be used to
interpret and extract insights
from images.
Conversational AI:
AI that can “listen,”
understand, and
communicate using
natural language.

8. 7
The following section describes these technologies and use cases in more detail.
ENTERPRISE INTELLIGENCE
One of the greatest challenges in the enterprise is the difficulty in bringing together
data from business systems—such as Business Intelligence (BI), Customer Relationship
Management (CRM), market research, web analytics, and even external sources—and
analyzing them in an integrated manner.
For example, while social media might be a leading indicator of product popularity, it
is difficult if not impossible to connect that insight with actual transactions, and harder
still to use that data to forecast impact to the supply chain, especially for companies
that sell through channels (e.g., not directly to customers). AI can also be used to make
predictions about products based on sensor data.
Enterprise intelligence looks at multiple datasets in context, using machine learning
algorithms to extract not only insights but predictions, recommendations, and actions
based on those findings. Most importantly, intelligence systems use the outcomes of
these actions to (re)train their algorithms.
Enterprise intelligence is used for a variety of functions, including marketing, security,
enterprise performance management, analytics, and information management (see
Figure 2).8 For example, Hewlett Packard uses vibration sensors that detect and evaluate
printer sounds and alert users if the machine is likely to run out of ink or fail within the
next two weeks.9
Customer Experience and Service
Identify Customer Service Issues
Predict Potential Churn
Sales and Marketing / eCommerce
Customer Acquisition
Optimize Customer Onboarding
Predict Campaign Performance
Identify Upsell and Cross-Sell Opportunities
Optimize Customer Lifetime Value
Optimize CRM Strategy
Optimize Shopping Cart Value
Optimize Customer Retention
Optimize Customer Loyalty
Advertising
Ad Targeting
Revenue Optimization
Figure 2: Use Cases for Enterprise Intelligence
Employee Productivity
Optimize Employee Onboarding
Predict Potential Churn
Optimize Resource Planning
Recruitment
Identify Qualified Candidates
IT and Operations
Workflow Management
Enterprise Performance Management
Security
Information Management
Identify Potential Fraud
Optimize Data Management Processes
Manufacturing
Predict Maintenance Issues
Supply Chain
Optimize Demand Forecasting