Half of all CIOs plan to use AI, but adoption is slow, says Gartner

Gartner says only 4% of CIOs have AI projects so far, but key lessons have been learned

Tags: Artifical intelligenceGartner Inc. (www.gartner.com/technology/home.jsp)
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Half of all CIOs plan to use AI, but adoption is slow, says Gartner AI efforts should start small, with projects that enable workers and include knowledge transfer, says Andrews.
By  Mark Sutton Published  March 3, 2018

Nearly half of all CIOs plan to deploy artificial intelligence, although only 4% have done so so far, according to a survey by Gartner.

The analyst company's 2018 CIO Agenda Survey of over 3,000 CIOs worldwide showed that 46% of CIOs plan to use AI solutions in future.

At present, AI is predominantly being used in fraud detection or to boost customer experience, Gartner said.

"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."

Gartner has identified four key lessons that have emerged from early AI projects.

The first is to aim low, with projects that are small in scope, and to use them as a learning experience rather than expect an impact on the bottom line

"Don't fall into the trap of primarily seeking hard outcomes, such as direct financial gains, with AI projects," said 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.

"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," he added.

AI projects should focus on augmenting people, not replacing them, which will avoid resistance from employees, and instead focus on how those employees can be enabled to pursue higher-value activities.

Andrews said "it will be far more productive to engage with workers on the front line, and to get them excited and engaged with the idea that AI-powered decision support can enhance and elevate the work they do every day".

Thirdly, organisations should be aware that they may require skills, particularly in data science, to execute on their AI plans, but they should ensure that knowledge transfer is part of any engagement, so that the organisation is able to develop its own skills for future projects.

Finally, organisations should choose transparent AI solutions that give the organisation a proper view into how and why outcomes are generated. 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 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, he added.

Gartner noted that in situations where decisions are subject to regulation and auditing, it may be a legal requirement to provide this kind of transparency.

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