Who Really Rules AI? Discover Why CFOs Are Leading the Artificial Intelligence Success Story

According to Laks Srinivasan, co-founder and CEO of the Return on AI Institute, the responsibility for AI typically rests with Chief Data and Analytics Officers or Chief AI Officers. However, when CFOs oversee AI projects and are responsible for scoring outcomes, companies tend to extract more value. Srinivasan, an AI strategy expert, co-authored the study “Economic Maturity for Artificial Intelligence” with Thomas H. Davenport, a Babson College professor and co-founder of the Return on AI Institute, highlighting the importance of this shift. The study is based on a survey of 1,006 C-suite executives across 11 countries and 32 industries, in addition to interviews with technology, data, and AI leaders.

The findings reveal a clear difference in the success of AI depending on the involvement of the CFO. Only 2% of respondents mentioned that CFOs were in charge of obtaining value from AI. However, when CFOs are responsible, 76% achieved a great deal of value, significantly higher than other roles. Srinivasan explains that CFOs can develop the methodology and scale it enterprise-wide, bringing institutional credibility to the numbers.

In several companies surveyed, CFOs and finance teams partnered with technology executives to certify the value of AI. A prominent example is DBS Bank in Singapore, where unit CFOs are responsible for vetting the AI value numbers before they are rolled up into the enterprise. DBS Bank has generated approximately 1 billion Singapore dollars in economic value from its data analytics and AI initiatives, thanks to the involvement of CFOs. The Return on AI Institute, which launched about five years ago, collaborates with Scaled Agile, Inc., on thought leadership and AI upskilling.

Another key finding of the study is that generative AI is the most difficult type to establish value from, with 44% of respondents citing it, likely due to the challenges of measuring productivity in “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, 35% of companies that have adopted agentic AI report high value.

Srinivasan notes that, while we all see value in personal productivity, the challenge lies in translating that into business value. His advice is to involve finance and aggregate different metrics. “It may not be a science, maybe there’s a little bit of art involved, but you have to do it,” he stated. Another important piece of advice is AI training for everyone. There is a 23-point advantage in achieving high value when both employees and leaders are trained; however, 58% of organizations have not trained employees in basic AI use.

Regarding workforce impact, only 2% of the organizations surveyed have made significant AI-driven headcount cuts, but nearly 90% have reduced or frozen hiring in anticipation. Srinivasan comments that headcount reductions and hiring freezes are well ahead of the evidence. Implementing AI also requires significant organizational change.

Srinivasan recommends a “narrow and deep AI” approach, reimagining specific processes for the AI era. Instead of layering AI onto existing workflows, the key question is: what gets automated, and what still requires human judgment? “You can actually make a solid, logical case to say, ‘This is really the headcount we need,’ after doing all the hard work,” Srinivasan explained.

Srinivasan's research and recommendations offer valuable guidance for companies seeking to maximize the value of AI, emphasizing the importance of CFO involvement, AI training, and a strategic approach to implementation.