CIO Straight Talk - Issue 9 - 10
Machine learning in cybersecurity will boost big data, intelligence,
and analytics spending to $96 billion by 2021. (ABI Research)
More important, they frequently share open
source software that can be used by both
newcomers and experienced machine learning
practitioners, adding to the extensive library of
machine learning tools long supported by the
open software community.
we started to transform it into a pure softwaredefined network that rides on a commodity cloud
hardware." The software-defined network allows
AT&T to embed intelligence within the network,
and create new infrastructure management
The availability of tools, resources, and talent
have enabled many enterprises to take advantage
of the most recent advances in machine learning
and apply its power to a wide range of activities,
interactions, and transactions.
Just as machine learning tools allow Catalyst
Paper to predict future required capacity, they
help AT&T predict a business customer's desired
bandwidth, based on usage patterns. In addition,
the software learns the patterns of normal activity
on the network, helping it detect and respond to
cyberattacks before they become a major issue.
"We call it closed-loop automation," says Gilbert.
"These AI software agents are continuously
monitoring and taking action."
Here are some ways that enterprises are leveraging
modern machine learning to improve operations,
increase productivity, and delight customers.
"Machine learning is a safety net," says Catalyst
Paper CIO Paul Einarson. "It helps me, as a
CIO, sleep better at night." Why? "It's difficult
for humans to predict," says Einarson-in his
case, how much the amount of data generated
and consumed by Catalyst Paper, one of North
America's largest producers of mechanical
printing paper, will grow over the next few
months. It's something that the IT staff needs
to get right in order to ensure the availability
of adequate computing and storage capacity.
Machine learning algorithms, monitoring and
analyzing consumption patterns, alert Einarson
when he needs to add capacity.
With data growing by leaps and bounds,
accurately forecasting the needs of the
IT infrastructure is crucial to its optimal
management-to say nothing about improving
the sleeping patterns of CIOs. This is even more
challenging when your IT infrastructure serves
millions of consumers and businesses. The
amount of data flowing over AT&T's network
has grown by 150,000% since 2007, according
to Mazin Gilbert, Vice President of Advanced
Technology at AT&T Labs.
"Our network has been put together over
the past century using a lot of different
technologies," says Gilbert. "A few years ago,
39% of CIOs say AI is on their radar or they are actively
researching it. (CIO Magazine)
In a manufacturing environment where data is
collected and analyzed, "an automation system
can self-learn or self-tune and provide alarms-'I'm
going to fail, come and fix me'," says Srinivas
Nidamarthy, CTO at ABB Robotics Systems. In
fact, he says, machine learning has given rise to
an entire service industry on top of manufacturing
automation, one that provides monitoring and
proactive equipment maintenance.
The emphasis should be
of human capabilities not
"automation" of them.
Executive Vice President & CTO - IT Services,
A Pragmatic Approach to Adopting AI
Artificial intelligence, and the related concept of
automation, have moved from discussion topic for
CTOs and CIOs to agenda item in the board-room.
Investments are flowing rapidly into the development
of AI technologies-including deep learning, machine
learning, natural language processing, cognitive
and computer vision-both at the startup level as
well as within some of the world's leading
In this interview with Kalyan Kumar, Executive Vice
President & CTO - IT Services, HCL Technologies, he
offers some guiding principles for companies as they
adopt AI technologies.
How can enterprises begin to implement and reap
the benefits of AI?
You start with automation, which allows companies
to improve efficiency and reduce costs by
streamlining repetitive processes. But that's just the
beginning. You need to combine automation with
artificial intelligence/analytics, then orchestrate
the application of those technologies in an
interconnected manner to business tasks across
infrastructure, applications, business processes, and
product engineering. This AI-enabled automation,
or autonomics, can "set an enterprise free,"
transforming it into a true 21st-century enterprise, a
lean operation that is as agile as a start-up.
Why is orchestration so important?
Chief Digital Officer
at Maersk Group
Prith Banerjee, until recently Executive Vice
President and CTO at Schneider Electric, has
used machine learning for proactive maintenance
on the systems it maintains for its customers.
"Instead of waiting to replace an asset after it
has failed," says Banerjee, "we are using machine
learning algorithms to predict when assets
such as drives, PLCs, breakers, or transformers
will fail." Rather than waiting for a customer to
report a failure, the equipment "calls home" with
an alert about an emerging or imminent issue.
Being able to predict when an asset will fail, "we
can send a replacement part and a repair person
before that happens."
(continued on page 14)
The intelligent automation of an individual business
task has benefits that are limited by the nature of
that task. But when autonomics is applied to a task
in concert with other tasks-when that task becomes
part of a streamlined workflow of tasks-the
results are powerful. You end up with a connected
organization in which different functions work
collaboratively across traditional silos, using stateless
digital applications, and an increasingly software
defined infrastructure. Through orchestration, you
end up with a lean and agile enterprise that can
quickly respond to opportunities-one that moves
beyond simply greater efficiency and becomes truly
alive, able to respond quickly to changing
musicians. The onus of producing the desired overall
musical effect is on the orchestrator, not on the
individual performers. In the context of IT Services,
orchestration involves coordinating and directing IT
operations in an intelligent and holistic manner. And,
as in music, the onus of delivering the desired overall
business outcomes lies with the orchestrator.
How should companies go about deploying
autonomics to ensure a return on their investment?
The approach that will generate the greatest
business value will be pragmatic rather than rigidly
prescriptive. It will leverage both proprietary
and open-source technologies. It will draw on a
collaborative ecosystem of expertise rather than a
single source. And it will involve a careful assessment
of both an enterprise's current IT system maturity
and its business goals. This assessment will allow
for the segmentation of complex and mixed IT
environments into "zones" that permit the creation of
a practical IT autonomics road map, one that will help
IT to control spending and set realistic automation
goals for its business stakeholders. These principles
hold true whether you're looking at a standard
environment, in which a solution can be essentially
plug-n-play, or a complex legacy environment, in
which the solution will need to be customized to
integrate with the legacy structures.
What about the concern that AI will make human
Adopting AI isn't about replacing people, it's about
augmenting human capabilities. Artificial intelligence
technologies enable business transformation by
doing the unglamorous work that humans are not
so good at-for example, processing huge amounts
of data quickly, efficiently, and accurately. The
relationship between humans and AI is actually
mutually empowering. The question that IT and
business leaders need to ask is: Are we using AI to
replace people or are we using AI to make them work
better, faster and more efficiently than ever before. In
other words, are we using AI to divide or to multiply
Achieving this requires an "orchestrator" whose
vision is broad enough to see the relationships
between those tasks. In music, orchestration
involves the writing and the arrangement of music
for an ensemble. The orchestrator assigns different
instruments and different musical parts to individual