CIO Straight Talk - Issue 9 - 18
By 2020, algorithms will positively alter the behavior of more
than 1 billion global workers. (Gartner)
seen, data-and the quantity of data-plays a
major role in ensuring a significant return on
investment. Says Prith Banerjee: "The challenges
have always been training the machine learning
algorithms. The more real data you have
available, the better the accuracy."
The challenge for an industry like Schneider
Electric's is that failures are rare, and it is difficult
to train machine learning algorithms with very
Furthermore, besides challenges concerning
the quantity of data, there are challenges
involving data quality. And the more data you
have available, the more vigilant you must be
to ensure that the analysis and predictions are
based on valid observations.
Another challenge involves the fundamental
differences between the familiar-developing
traditional software-and the new-developing
and managing AI applications. For example,
debugging is harder because it's difficult to
isolate a bug in a machine learning program. And
unlike traditional software, when you change
anything, you end up changing everything.
Most important, the trove of tools and tested
processes that have been accumulated
throughout the years for software development
does not exist for modern machine learning.
Learning from the experience of others
and staying up-to-date regarding the latest
developments in the practice of machine
learning is crucial.
In many enterprises, this expertise exists in
the analytics and data science team, so that's
a natural place to incubate and drive early AI
initiatives. Depending on the organization, it
could be useful to select a senior member of
that team to act as a "Chief AI Executive," with
enterprise-wide responsibilities for introducing
modern machine learning methods.
As always, your people are the most important
element in ensuring the successful introduction
of new tools and practices.
And that doesn't mean only the people
managing them but also the people on the
receiving end, the employees that must adjust
the way they work and understand the potential
benefits of using the new tools. Given the bad
rap AI sometimes gets in the press-and the
ominous-sounding, human-replacing overtones
of "artificial intelligence"-carefully introducing it
in the organization is even more important than
it typically is with other new technologies.
The emphasis should be on "augmentation" of
human capabilities not "automation" of them,
as well as on the creation of new roles and
responsibilities that come with the adoption
of AI technologies. Says Maersk's Gokcen: "In
the industrial revolution, certain jobs ceased
to exist but other jobs were created. The same
thing is happening with the Internet revolution.
We will see an impact on certain roles but also
the creation of new high-impact jobs and the
creation of a lot more value from existing work."
"An automation system can
self-learn or self-tune and
[in case of failure]."
CTO at ABB Robotics Systems
At AT&T, the focus is on bringing human experts
and AI agents together "in a hybrid model,"
says Mazin Gilbert, and taking advantage of
what each does best: "Humans are very good
at understanding context. Machines are good at
processing lots of data and finding patterns in
it." Similarly, at KLM, "we don't replace human
agents with technology-we use technology to
facilitate the dialog with our customers," says
Smit. "Human interaction will always be key."
As we succeed in computerizing cognitive
capabilities, computers will continue to augment
humans, as they have done for more than sixty
years. For enterprises, this means working
smarter and finding new ways to thrive.
Gil Press, a widely read columnist
(www.forbes.com/sites/gilpress) and blogger
(whatsthebigdata.com) on technology,
entrepreneurs, and innovation, is a Contributing
Writer for CIO Straight Talk.