Half of CIOs Are Planning to Deploy Artificial Intelligence
2018 — 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.
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.”
or unfamiliar technologies, early adopters are facing many obstacles to the progress of AIin
their organizations. Gartner analysts have identified the following four
lessons that have emerged from these early AI
projects.
1. Aim Low at First
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.”
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.
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.
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.”
AI Solutions
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.
From Early AI Projects.”
Summit
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 andTokyo. Follow news and updates from
the events on Twitter using #GartnerDA.
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