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STAMFORD, CONN., FEBRUARY 13, 2018
GARTNER SAYS NEARLY HALF OF CIOS ARE PLANNING TO DEPLOY ARTIFICIAL INTELLIGENCE
ANALYSTS WILL FURTHER DISCUSS THE ADOPTION OF AI AT THE GARTNER DATA & ANALYTICS
SUMMIT, MARCH 19-21, 2018 IN LONDON, U.K.
Meaningful artificial intelligence (AI) deployments are just beginning to take
place, according to Gartner, Inc. Gartner’s 2018 CIO Agenda Survey shows
that four percent of CIOs have implemented AI, while a further 46 percent have
developed plans to do so.
"Despite huge levels of interest in AI technologies, current implementations
remain at quite low levels," said Whit Andrews, research vice president and
distinguished analyst at Gartner. "However, there is potential for strong growth
as CIOs begin piloting AI programs through a combination of buy, build and
outsource efforts."
As with most emerging or unfamiliar technologies, early adopters are facing
many obstacles to the progress of AI in their organizations. Gartner analysts
have identified the following four lessons that have emerged from these early AI
projects.
1. Aim Low at First
"Don’t fall into the trap of primarily seeking hard outcomes, such as direct
financial gains, with AI projects," said Mr. Andrews. "In general, it’s best to
start AI projects with a small scope and aim for 'soft' outcomes, such as
process improvements, customer satisfaction or financial benchmarking."
Expect AI projects to produce, at best, lessons that will help with subsequent,
larger experiments, pilots and implementations. In some organizations, a
financial target will be a requirement to start the project. "In this situation,
set the target as low as possible," said Mr. Andrews. "Think of targets in the
thousands or tens of thousands of dollars, understand what you’re trying to
accomplish on a small scale, and only then pursue more-dramatic benefits."
2. Focus on Augmenting People, Not Replacing Them
Big technological advances are often historically associated with a reduction in
staff head count. While reducing labor costs is attractive to business
executives, it is likely to create resistance from those whose jobs appear to be
at risk. In pursuing this way of thinking, organizations can miss out on real
opportunities to use the technology effectively. "We advise our clients that the
most transformational benefits of AI in the near term will arise from using it
to enable employees to pursue higher-value activities," added Mr. Andrews.
Gartner predicts that by 2020, 20 percent of organizations will dedicate workers
to monitoring and guiding neural networks.
"Leave behind notions of vast teams of infinitely duplicable 'smart agents' able
to execute tasks just like humans," said Mr. Andrews. "It will be far more
productive to engage with workers on the front line. Get them excited and
engaged with the idea that AI-powered decision support can enhance and elevate
the work they do every day."
3. Plan for Knowledge Transfer
Conversations with Gartner clients reveal that most organizations aren't
well-prepared for implementing AI. Specifically, they lack internal skills in
data science and plan to rely to a high degree on external providers to fill the
gap. Fifty-three percent of organizations in the CIO survey rated their own
ability to mine and exploit data as "limited" — the lowest level.
Gartner predicts that through 2022, 85 percent of AI projects will deliver
erroneous outcomes due to bias in data, algorithms or the teams responsible for
managing them.
"Data is the fuel for AI, so organizations need to prepare now to store and
manage even larger amounts of data for AI initiatives," said Jim Hare, research
vice president at Gartner. "Relying mostly on external suppliers for these
skills is not an ideal long-term solution. Therefore, ensure that early AI
projects help transfer knowledge from external experts to your employees, and
build up your organization’s in-house capabilities before moving on to
large-scale projects."
4. Choose Transparent AI Solutions
AI projects will often involve software or systems from external service
providers. It’s important that some insight into how decisions are reached is
built into any service agreement. "Whether an AI system produces the right
answer is not the only concern," said Mr. Andrews. "Executives need to
understand why it is effective, and offer insights into its reasoning when it’s
not."
Although it may not always be possible to explain all the details of an advanced
analytical model, such as a deep neural network, it’s important to at least
offer some kind of visualization of the potential choices. In fact, in
situations where decisions are subject to regulation and auditing, it may be a
legal requirement to provide this kind of transparency.
Gartner clients can read more in the report "Lessons From Early AI
Projects." This research is part of the Gartner Special Report "Craft an
Artificial Intelligence Strategy," a collection of research aimed at
helping CIOs incorporate AI into their strategic planning and evaluation
processes for business transformation.
Gartner Data & Analytics Summit
Gartner analysts will provide additional analysis on data and analytics
leadership trends at the Gartner Data & Analytics Summit 2018, taking place
in Sydney, Grapevine, Texas, London, Sao Paulo, Brazil, Mumbai, India and Tokyo.
Follow news and updates from the events on Twitter using #GartnerDA.
ABOUT GARTNER
Gartner, Inc. (NYSE: IT) delivers actionable, objective insight to executives
and their teams. Our expert guidance and tools enable faster, smarter decisions
and stronger performance on an organization’s mission critical priorities. To
learn more, visit gartner.com.
CONTACTS
Rob van der Meulen
Gartner
rob.vandermeulen@gartner.com
Thomas McCall
Gartner
thomas.mccall@gartner.com
--------------------------------------------------------------------------------
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