Four percent of CIOs have implemented artificial intelligence (AI), while 46 percent have developed plans to deploy AI.
“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 analysts have identified the following four lessons that have emerged from these early AI projects.
Gartner suggests CIOs should not seek hard outcomes, such as direct financial gains, with AI deployment projects. CIOs should start AI projects with a small scope and aim for soft outcomes, such as process improvements, customer satisfaction or financial benchmarking. CIOs also should keep small financial targets from AI investment.
CIOs should focus on augmenting people and not replacing them when they opt for AI related investments in their organizations.
Gartner predicts that 20 percent of organizations will dedicate workers to monitoring and guiding neural networks by 2020.
Most organizations aren’t prepared for implementing AI. They lack internal skills in data science and plan to rely to a high degree on external providers to fill the gap. 53 percent of organizations in the CIO survey rated their own ability to mine and exploit data as limited — the lowest level.
Gartner predicts that 85 percent of AI projects will deliver erroneous outcomes due to bias in data, algorithms or the teams responsible for managing them through 2022.
CIOs should select transparent AI solutions. Though it may not be possible to explain all the details of an advanced analytical model, such as a deep neural network, it’s important to offer some kind of visualization of the potential choices.