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3 Ways To Raise the Success Rate of AI Projects

Many AI projects have yet to deliver satisfactory results, according to a report by Infosys Knowledge Institute. Learn about three areas of improvement.

Companies globally are spending billions of dollars in AI systems and yet their investments are not yielding the results they were looking for, according to a report by Infosys.

Worldwide spending on AI is expected to top $118 billion in 2022 and reach more than $300 billion by 2026, according to the inaugural report from Infosys Knowledge Institute titled, “Data + AI Radar.” It surveyed 2,500 AI practitioners from companies with over $500 million in annual revenue in the U.S., U.K., France, Germany, Australia and New Zealand.

According to the survey, 81% of respondents have deployed their first AI system in the past four years. But 85% of this group have not reached advanced AI capabilities with 63% of AI models "still driven by humans." Outcomes also are mediocre: Users say they are highly satisfied with their data and AI results only about a quarter of the time.

“Companies have achieved basic AI capabilities,” the survey said. However, “this is not what they want. Three out of four companies in our survey want to operate AI at enterprise scale.”

With most still using basic AI, users are dissatisfied with results: 85% of AI practitioners did not achieve top-tier capabilities such as predictive analytics.

The report said these AI projects are “failing to deliver” amid “heightened” expectations and recommended three areas of improvement: developing data practices that encourage sharing, binding explanations into advanced AI, and focusing AI teams on business.

1. Get your data right, and share it.

The report urged users to change their thinking about data as the new oil, connoting a hard-to-extract and exhaustible mineral. Instead, data should be seen as currency, which gains value as it circulates.

“Savvy businesses know that establishing a data-sharing ecosystem with partners and peers delivers greater benefits than a solitary data lake or warehouse,” the report said. For example, a company’s enterprise clients could choose to share their data to receive more customized AI models.

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