Why CFOs—not chief AI officers—are the secret to getting real value from AI

Good morning. Typically, value accountability for AI falls on the chief data and analytics officers or chief AI officers, Laks Srinivasan, co-founder and CEO of the Return on AI Institute, told me. But when CFOs oversee AI projects and are responsible for scoring outcomes, companies tend to extract more value, he said.

Srinivasan, an AI strategy expert, co-authored the study, “Economic Maturity for Artificial Intelligence,” with Thomas H. Davenport, a Babson College professor, MIT fellow, and co-founder of the Return on AI Institute. The findings are based on a survey of 1,006 C-suite executives across 11 countries and 32 industries, plus interviews with technology, data and AI leaders.

Only 2% of respondents said CFOs are charged with achieving value from AI. However, when CFOs are responsible, 76% achieved a great deal of value, substantially higher than for other roles. It’s not that CFOs necessarily know more about AI than a chief AI officer or other C-suite leaders, Srinivasan said. Finance chiefs can develop the methodology and scale it enterprise-wide. “When finance gets involved, it brings institutional credibility behind numbers,” he said.

In several companies surveyed, CFOs and finance teams partnered with technology executives to certify AI value. “For example, at DBS Bank in Singapore, the unit CFOs are responsible for vetting the AI value numbers before they are rolled up into the enterprise,” Srinivasan said. “And DBS Bank says it has generated about 1 billion Singapore dollars in economic value from its data analytics and AI initiatives; that’s because CFOs get involved,” he said.

The Return on AI Institute launched about five years ago and partners with Scaled Agile, Inc., on thought leadership and AI upskilling. Another key finding: generative AI is the most difficult type to establish value from, with 44% of respondents citing it, likely due to challenges measuring productivity for “broad and shallow” use cases.

Agentic AI ranks second at 24%, followed by analytical AI at 16%, while rule-based AI is the least difficult. Despite this, the 35% of companies that have adopted........

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