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ARTIFICIAL
INTELLIGENCE:
APPLYING BIG DATA MACHINE LEARNING &
,
,
CAUSAL REASONING TO DIGITAL TRANSFORMATION
JOSH SUTTON, RITESH SONI & SCOTT PETRY
It’s been five years since IBM’s artificial
intelligence, Watson, beat human contestants on the game show Jeopardy!
Still, to this day, many people think of
artificial intelligence (AI) as science
fiction — a cunning computer run amok
or a loyal companion robot.
The reality is much more practical.
Artificial intelligence technologies are
largely designed to help humans work
better – first, by generating insight
from data more quickly and accurately
than is humanly possible and second,
by acting automatically on that insight.
Invisible to the human eye for years,
these technologies have been completing a broad range of tasks, from
correctly routing mail to interpreting
handwriting.
Today, AI technologies are used everywhere you turn: Siri on your iPhone,
rear parking assist in your car, automatically re-ordering supplies on Amazon,
and suggesting clothes you may like on
your favorite retail websites.
Indeed, enhancing the human ability to
process remains a top strategic priority
at early AI innovators such as Facebook,
IBM, Microsoft, and Google.
These technologies (including image
recognition, natural language processing, machine learning, causal reasoning, and robotics) can help businesses
increase revenues, reduce costs, and
mitigate risks.
“Enhancing the human ability to
process” is a strong statement and
the stakes are enormous for the early
innovators, as well as for the broader
economy. Artificial intelligence technologies have the potential to transform
entire business models at a clip reminiscent of the industrial revolution. The
question is: How can companies take
full advantage of such a fundamentally
disruptive group of technologies?
1
IMRG. “Over Half of Online Sales Now Made through Mobile Devices.”
http://www.imrg.org/media-and-commentpress-releases/over-half-of-online-sales-now-made-through-mobile-devices/.
BBC. “Cashless Payments Overtake the Use of Notes and Coins.” http://www.bbc.com/news/business-32778196.
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TRENDS AT THE INTERSECTION OF TECHNOLOGY & STORY
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Our view of AI
For business leaders, it is important
to have a basic understanding of how
the major technologies that constitute
AI can deliver optimal business impact
by enabling their companies to provide
products and services to meet their
customers’ needs when and where
they need them.
To provide such products and services
requires the ability to collect and analyze
vast amounts of structured and unstructured data, and to use the insights
gained from that data to inform business
decisions and take action in real time.
Broadly speaking, AI technologies fall
into two principal categories:
Machine learning (correlation-based
analyses and predictions) through
which complex patterns can be
identified and acted upon. Machine
learning tools allow businesses to
understand what is happening in
the data.
Causal reasoning (otherwise known
as “common-sense AI”) platforms
that apply real world understanding
to information, test hypotheses, draw
conclusions, and allow executives
to better understand why things
are happening.
Although the application of each technology on its own can result in improved
business performance, the truly
transformative value and future of AI
will lie in the ability to seamlessly layer
data analytics, machine learning, and
causal reasoning platforms to deliver
insight-driven, personalized products
and services.
In particular, this combination offers
great promise for changing the way
that brands approach marketing. By
using these technologies in concert
with each other, marketers can obtain
a better understanding of consumer
and behavioral data, and enable far
more granular personalization of the
customer experience.
Most of the
knowledge in
the world in the
future is going to
be extracted by
machines and will
reside in machines.
– Yann Lecun,
Director of AI Research,
Facebook1
For example, Walmart knows that a
sunny weekend forecast in May brings
out gardeners, so the company combines data from localized weather
forecasts with that of consumers’
buying histories to send personalized
mobile promotions.
Gutierrez, Sebastian. Data Scientists at Work. Apress. December, 2014.
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TRENDS AT THE INTERSECTION OF TECHNOLOGY & STORY
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In fact, machine learning tools help
the company do everything from
improving store layouts to optimizing
the efficiency of delivery routes and
warehouse operations.
Most companies that are leveraging
AI successfully have found that the
key to realizing meaningful results is
to mix and match complementary AI
technologies geared toward specific
markets or roles (see Figure 1). Facebook’s newest AI tool, for instance,
draws on both image recognition and
natural language processing tools
to help describe photos to visually
impaired users.
FIGURE01
AI point solutions are available for various markets and roles
AI technologies support solutions that span across various business cases, challenges, and teams.
Intelligent Assistance
AI will provide expert assistance
for users during various activities,
including product support, Q&A, and
providing recommendations. They
can also provide suggestions to
experts – for example, recognizing
patterns in complex data more quickly.
Summarization Services
Natural language processing
can be used to automatically
create summaries of both
highly technical and nontechnical information.
Custom Digital Experiences
By evaluating humans’ emotions,
moods, attitudes, and intents, AI
can then create and modify the
experiences to match.
Support for Accessibility
Anticipate Customer Needs
Based on a variety of inputs,
AI technology can anticipate
customers’ needs and proactively make suggestions
for how customer service or
management should support
those customers.
On-the-go tools to support
the accessibility needs of the
disabled or impaired. These
tools can provide real-time
interpretation of signs,
papers, books, etc., in the
messy “real world.”
Source: SapientNitro, 2016.
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Combining machine learning
and causal reasoning
Until recently, big data and machine
learning have been the primary focus
for many big data analytics projects,
but causal AI will emerge as an
important complementary tool in
the 2018–2020 timeframe.
Here’s why.
Machine learning
Machine learning platforms such as
IBM’s Watson or Google’s TensorFlow
use algorithms to find particular patterns in huge data sets, and learn from
the results.
For example, a machine learning
platform can look at a million tagged
pictures of house cats to learn the attributes of something called a cat. Then,
when it sees another cat picture, it will
recognize it as a feline. This is what
drives the impressive accuracy of apps
like Google Photos, which can identify
pictures of you and your family members based on its analytical learnings.
In marketing applications, machine
learning algorithms are particularly
useful for finding unexpected patterns
that help companies more accurately
build market segmentations or optimize
ad spend. For example, using machine
learning to better target online display
ads can dramatically improve clickthrough rates.
machines to think like humans by
showing them how things relate and,
subsequently, allowing them to reason
contextually. In other words, the computers have to learn common sense.
So while a machine learning platform
can identify that cat picture, causal
AI platforms – like MIT’s ConceptNet
and Cycorp’s Cyc platform – apply
context to that image by drawing on an
extremely large model of relationships
that reflect a human being’s understanding of how the world works in
relation to that image. In other words,
rather than simply identifying the
picture, causal AI tools also understand
a cat’s place in the world (e.g., they
make great pets, are great hunters, and
sometimes trigger allergies in humans).
Companies
will be able
to influence
consumer
behavior by
responding in
context to what
an individual
is doing in
the moment.
Fusing multiple technologies
to form the optimal solution
The union of multiple forms of AI
allows companies to achieve digital
transformation through insight generation, customer engagement, and
business acceleration.
Insight generation
Causal reasoning: Common sense AI
Insight generation involves extracting
meaningful and actionable intelligence
from ever-increasing quantities of
available raw data. With the amount of
information in the world nearly doubling
each year, it is no surprise that companies are scrambling to capture and
make sense of it.
On the other hand, causal reasoning
systems (or common sense artificial
intelligence) use more of a teachingbased approach. Causal AI teaches
One of the fastest growing uses of AI is
to “listen” to all customer communications, both directly with a company and
about that company in the market
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at large – ranging from call center conversations to chat sessions and even
social media activity. AI tools are able
to perform what no single human – or
even team of people – could hope to
do; they can read, review, and analyze vast quantities of disparate data,
providing insight into how customers
feel about a company’s products or
services and why they feel the way they
do. Luminoso, an AI company with its
roots in MIT’s Media Lab, has built a
robust business performing precisely
this task.
Customer engagement
Customer engagement has long been
the “holy grail” for marketing and CRM
programs. Today, AI is radically enhancing the personalization of information
that fuels such engagement. Nowhere
is this more evident than in AI’s “next big
thing”: chatbots and virtual assistants.
Chatbots are software programs that
use messaging as an interface through
which companies can answer customers’ questions, help their customers
find information, and offer personalized
deals and sales. They are ideally suited
to a mobile platform and have been
made significantly more powerful by
advances in machine learning and
natural-language processing.
Multiple companies such as Viv,
Facebook, and Nuance are providing
frameworks and turnkey solutions in
this space, allowing for services as
diverse as media content distribution,
customer service support, and customized marketing campaigns. While the
technology advances are exciting –
and bode well for business applications – successful use cases will be
grounded in a strong, user-centered
design process, leveraging the input of
business and marketing experts as well
as those of the information technology
(IT) division.
Business acceleration
Business acceleration refers to how
companies use AI to expedite knowledge-based activities to improve
efficiency and performance. Examples
range from hospitals finding potential
patients for drug trials to financial institutions creating investment strategies
for their investors.
While these types of activities are often
viewed as opportunities to reduce
costs through the automation of internal
processes, they also should be considered in terms of their ability to transform
the customer experience. For example,
if a bank can use AI to reduce the time
it takes to approve a loan, it not only reduces its own costs but also provides
an improved customer experience.
As a result, when AI tools such as
Watson from IBM and Cyc from
Cycorp are deployed, today’s market
leaders ensure that they leverage the
technologies with both cost-cutting and
customer satisfaction in mind.
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The road ahead: What leaders
should do today
According to International Data Corporation (IDC), by 2018 about half of
all apps developed will incorporate AI,
as firms increasingly experiment and
explore the use of this technology.2
After all, there are no tried-and-true
implementation methods for such a
rapidly evolving set of technologies.
While many companies know that
AI is important in a general sense,
most haven’t figured out specific
business applications.
That, however, will change. AI is
rapidly becoming a top business
priority, and brands should consider
how to get in front of the trend rather
than react to it later (see Figure 2).
Explore AI capabilities
As with any significant technology
trend, companies need to learn the key
elements of AI and dig into its possible
impact on specific business functions
as well as overarching industries.
Explore ways of making AI part of the
overall business conversation — for
example, through the use of exploratory
projects or innovation labs that look for
the best areas for AI applications, both
within your company and across its
products and/or services.
FIGURE02
Smart machines’ rapid impact
Leading predictions show the imminent rise of smart machines and their impact
on business investments and applications.3
By year-end
By
2018
2020
25% of durable goods manufacturers
will utilize data generated by smart
machines in their customer-facing
sales, billing, and service workflows.
Smart machines will be a top five
investment priority for more than
30% of CIOs.
R&D-based, end-user approaches
to smart machine deployment will be
three times more likely to produce
business value than IT project-based
approaches.
CFOs will need to address the valuations derived by smart machine data
and “algorithmic business.”
CIO Magazine. “Who’s in Charge of AI in the Enterprise?” http://www.cio.com/article/3033141/robotics/whos-in-charge-of-ai-in-the-enterprise.html.
2
Graphic created by SapientNitro based on Gartner research: Predicts 2016: Smart Machines. December, 2015. https://www.gartner.com/doc/3175120/predicts--smart-machines.
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Start with small, targeted projects
to learn about the technology
Where is your money best allocated?
Smart AI investors tend to follow the
data breadcrumb trail. That’s why
data-intensive industries like financial
services and healthcare are early innovators in this field: They feel more pressure to extract full value from their large
volumes of data, making it easier to
balance the risk and reward of such a
significant investment. Most companies
start with smaller, targeted projects that
can improve existing business processes, particularly in areas that place
a high premium on real-time decision
making or have large volumes of data
that are not being effectively utilized.
For example, machine learning models
can be used to improve product
recommendations by predicting, based
on previous behavior, what item a customer is most likely to buy. The same
concept can also apply to predicting
customer churn, helping companies
know when to offer incentives to keep
customers on board. Another likely
application area is in dynamic pricing,
where AI can be used to more accurately predict product demand at
a given price.
Search for knowledge bottlenecks
When it comes to identifying and
prioritizing near-term prospects, look for
knowledge bottlenecks — areas where
humans either can’t absorb the information fast enough, or where there are
large streams of data to integrate and
analyze. Go after an obvious optimization problem as opposed to solving for
not very well-defined problems. This is
a long journey, not a six-month effort.
As tools mature and commercial machine learning and causal AI platforms
become more affordable, the riskreward ratio will flatten considerably,
lowering the barrier to entry at many
companies. Those who have learned
from working with smaller subsets of
data will be well-equipped to jump
aboard the express.
Machine learning
models can
improve product
recommendations,
predict customer
churn, and support
dynamic pricing.
Design with the customer in mind
Perhaps most important, successful
companies of tomorrow will fuse
together AI solutions with the customer
in mind. Rather than seeing each
emerging AI technology as an exciting
new tool in and of itself, they will seek
to determine which combination of AI
technologies can generate the most
actionable insight into their customers
and clients. They will use that insight
to provide consumers with products
and services that meet their needs
when and where they need them. And,
they will design these AI solutions from
the outside in – from the perspective
of their customers – as opposed to
inside out, via traditional divisional and
product silos.
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Conclusion
AI as a technology sector is evolving rapidly, providing tools that can
generate insight, engage customers,
and accelerate business growth. Early
leaders like Google, Facebook, and
Amazon are building their business
models around AI, and driving further
AI expansion by opening up their platforms to outside developers.
Meanwhile, significant venture capital
activity, coupled with substantial
improvements in accuracy and performance, have driven robust market
expansion for AI technologies.
All of this adds up to huge opportunities for businesses that can successfully leverage AI – either across functions such as marketing and sales
or for business transformation. The
tools that are currently available offer
significant potential to boost revenue,
cut costs, and reduce risk. And, when
combined and designed with the
consumer in mind, AI technologies can
deliver solutions that drive customer
loyalty, engagement, consumption, and
satisfaction. In fact, AI technologies
may become a key driver for the digital
transformation of tomorrow’s most
successful businesses.
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Josh Sutton
Global Head, Artificial
Intelligence Practice,
Publicis.Sapient
[email protected]
Josh is the Global Head of Publicis.Sapient’s Data and Artificial Intelligence Practice. In this role, he is responsible for
leveraging big data tools as well as correlation-based and
causal-based AI platforms to help clients transform their
businesses.
Ritesh Soni
Vice President, Data Science,
SapientNitro Washington, D.C.
[email protected]
Ritesh focuses on applying methods in machine learning
to opportunities in retail, e-commerce, marketing, and
operational optimization. His Data Sciences team combines
the latest methods to develop highly scalable systems with
machine learning at their core.
Scott Petry
Vice President, Technology,
SapientNitro Atlanta
[email protected]
Scott drives effective technology solutions as part of a
cross-functional team helping brands connect with their
customers through experience, media, and technology. He
works with great brands like UPS, ADT, MD Anderson, AT&T,
Universal Orlando, and Carnival.
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