A third of CFOs have stopped expanding AI: what they're seeing that others aren't

Finance leaders are increasingly scrutinizing AI investments because they must balance promised productivity gains against implementation costs and uncertain returns, with more than a third of current AI users reporting no plans to expand usage this year. Key barriers include limited experience managing AI-enabled finance processes, difficulty proving value beyond short-term efficiency, fragmented financial data, and shortages of professionals who combine accounting expertise with data and AI skills. AI projects may also require longer payback periods than traditional technology investments, making returns difficult to isolate when deployments coincide with broader changes to systems, processes, and staffing. Rather than signaling abandonment, a pause in AI spending can enable organizations to standardize data, strengthen controls, reduce manual workflows, and train staff before scaling tools. Examples from ServiceNow, Shopify, and Levi Strauss suggest that measurable benefits are more likely when AI is embedded in specific operating processes and tied to clear business outcomes. The article argues that reliable data, consistent transaction coding, connected systems, and redesigned workflows may matter more to finance AI success than selecting the latest model.

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