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EDITOR’S QUESTION
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CYRIL PERDUCAT, EXECUTIVE VICE
PRESIDENT OF IOT AND DIGITAL
OFFERS, SCHNEIDER ELECTRIC
L
ast year, Forrester noted that: “The
honeymoon for enterprises naively
celebrating the cure-all promises of
Artificial Intelligence (AI) technologies is over.
AI and all other new technologies like Big Data
and cloud computing still require hard work.”
Given that 70% of enterprises expect to
implement AI this year, including Schneider
Electric, I would like to offer three concrete
ways companies can seize the business value
that I strongly believe AI promises.
Lesson 1: Be pragmatic
Integrating an AI strategy can seem like a
daunting task as Forrester analysts point
out, so we recommend that any company
embarking on this journey start with a
pragmatic, practical approach to individual
AI projects.
Ask upfront: “Which problem can I solve
with an AI-enabled digital solution?” This
question always prompts our R&D process
to lead with the customer challenge in mind.
This approach worked well, for example,
when an onshore oil and gas customer
needed a better way to manage the
productivity and maintenance of extremely
remote oil pumps. We worked with Microsoft
to develop a solution that uses local analytics
enabled by Machine Learning.
Lesson 2: See the value in new digital
business models enabled by AI
For all of us, Digital Transformation at
large is about finding ways to create new
business value from digitisation. I had the
opportunity to discuss this topic at length
with Microsoft’s General Manager of
Manufacturing Çağlayan Arkan.
We agree that it’s often challenging for any
legacy company, however, to see beyond
its core business model to launch and
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accelerate a digital journey. Will I cannibalise
my business? How do I circulate new value
for customers?
AI applications can help customers
understand what it really means to push
forward new digital business models in a
disruptive yet profitable way.
Take long-time machine builders or Original
Equipment Manufacturers (or OEMs) as an
example. Most OEMs build highly specialised
equipment, such as the smaller-footprint
coffee pod machine we co-innovated with
our customer SOMIC.
In many cases, though, the CapEx
commitment for such tailored machines is
high. But what if a machine builder could
leverage AI, coupled with remote monitoring
capabilities, to begin offering ‘uptime as
a service’ to its end-users? This is a way to
lower the CapEx burden for end-users.
Only AI makes this business model possible,
as trained data models can qualify whether
a machine’s downtime is really a machine
issue vs. human or other error. See AI’s value
here for driving new business models? expertise across industry, buildings, data
centres, grid, plant and machine. We drill
down even more to the segment level to
ensure that our customer-driven AI projects
have a worthwhile business impact.
Lesson 3: Build strengths on top of
your domain expertise My point here is to create AI project teams
that include an AI expert, a computer
scientist, and, just as crucial, a domain
expert. It is the domain expert who can ask
the right questions for AI to solve and, more
importantly, know how to best respond to
what the AI models reveal (e.g predictive
maintenance applications).
Having strong domain expertise is critical
to making AI projects successful. Do not
underestimate its value. Why? Data overload
is a known reality, so it’s clear that we don’t
need more data. What we do need are much
better ways to tap the business value of that
data. Most companies are not AI experts;
channelling domain expertise it what will
make AI projects relevant to companies and
their customers.
Schneider Electric’s EcoStruxure architecture
is founded on our own deep domain
AI’s promising future is here
So is the honeymoon for AI over? With
the proper attention to integration and
deployment issues, we believe that AI as
the next wave of IoT innovation has only
just begun. n
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