i3 - March/April 2018 - 13

By Cindy Loffler Stevens

Q How do you define
artificial intelligence?
MY: AI is a huge umbrella term,
much abused in the media.
That's why we spend two
chapters to clarify what is
artificial intelligence. On a
high-level, AI refers to a set of
techniques within computer
science that are used to either
replicate or even exceed
human decision-making and
human perception. These can
include techniques like data
science, data mining, symbolic
and expert systems, machine
learning, deep learning,
evolutionary strategies, and
the list goes on. We dedicate
another chapter to a friendly
non-technical introduction so
that a business executive can
understand how these
technologies differ and what
they're good for.

Q How can executives

incorporate AI into their
business model?
MY: There are two layers that
need to happen if you want to
transform into a fully AI-ready
organization. The first layer is
leadership. You need to
educate your executives, bring
together budgets and gain
political buy-ins from stakeholders. The stakeholders can
be anyone from your frontline
employees to middle managers who may be resistant to the
idea of AI "taking over their
jobs." We spend time in the
book discussing, from a
strategic level, how to prepare
your organization from a
leadership standpoint.
The second layer is the technical layer. We find that most
traditional non-technology companies often lack the requisite
proprietary data to train useful
computer models. Preparing
and collecting this data can
be a months-long, years-long
procedure. You should get
started now. Our book helps
you figure out what are the right
steps to get started.

C TA . t e c h / i 3

The biggest misconception
is that we're really close
to superhuman AI and
so people are often afraid,
"Am I going to lose my job if we
adopt this AI system?"
Don't worry about it.

Q Are there examples of busi-

nesses that have successfully
incorporated AI?
AZ: One of the most common
areas of application of AI is in
customer service. I'm sure
everyone has had a frustrating
time when they called
customer service and got stuck
in an endless loop. With
artificial intelligence, you're
able to now create virtual
agents that are better suited to
answering questions in natural
language. Virtual agents can
alleviate cost center pressures,
especially during spikes in
contact volume. This can help
you achieve higher customer
satisfaction and better
customer service results.


MY: There are AI applications
for every enterprise function as
long as you have the data and
the technology infrastructure.
Applications fall into three
different categories: The first is
automation. Low-level that a
human can do in one second
can be automated in the near
term. The second is augmentation. For example, some AI
systems that can give very
accurate medical diagnoses
and support doctors in their
decision-making capabilities
for disease identification and
treatment planning. The third is
completely new functionality.
We made a couple of major
breakthroughs in AI in the last
few years. We can train

Are some businesses better
suited for integrating AI?

MY: Absolutely. One of the reasons why technology
companies have been at the forefront of AI is data tracking.
When we use online platforms such as Facebook, they record
every action you take. Whereas, if you are selling consumer
packaged goods, you are not putting sensors in shampoo.
You don't know what the user is doing at that granular level.
Companies naturally collect a lot of data then feed this data
into unique machine learning models. However, less tech
savvy companies are even missing the data capture and the
data collection procedures. Those types of companies tend
to be further behind.
AZ: It's not too late for them to start. If you are aware of your
data problems now, then you can start the processes to
collect, process, and store the data today. Data is the new oil.

Women In Tech
machine learning systems that
can identify images and classify
objects and images to about
human parity. We can do the
same for speech recognition
and also text-to-speech. That
enables a whole frontier of new
functions that we weren't able
to do before.

Q What are the

MY: The biggest misconception
is that we're really close to
superhuman AI and so people
are often afraid, "Am I going
to lose my job if we adopt this AI
system?" Don't worry about it.
Systems that we can use right
now can improve your predictive
abilities, but today we simply
don't have artificial intelligence
that's at the human level that
can actually replace the kind
of strategic and creative thinking
that you need an executive or
an expert for.
AZ: Many times, we find that
when businesses begin to use
machine learning, they don't
replace their workers. Instead,
they realign workers to higher
level strategic types of work.
For example, rather than have
a contact center representative
reset passwords all day, the
agent can now help customers
with banking finance or
consultative advice which is
higher value add to the business.

Q Where do you see the

industry in five years?
MY: If you ask experts any
questions about AI predictions,
the guesses will be all over the
place. What I will say is if you
don't start adopting, if you
don't already have a big data
strategy, if you don't already
have centralized data infrastructure, you're already
behind the curve. In five to
10 years, companies that have
not made a sufficient technical
transformation at the enterprise level to start adopting
machine intelligence and more
modern techniques are going
to be behind.



Table of Contents for the Digital Edition of i3 - March/April 2018

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