INDUSTRY // FIN. TECH

As CFOs head into budget season under pressure to fund AI and automation, the findings offer a reality check on where the market actually stands and where execution risk is most concentrated.

The CFO’ s AI Readiness Report asked respondents to rate their organisation’ s AI maturity on a 1 – 10 scale( low: 1 – 3, mid: 4 – 6, high: 7 – 10). The findings show:

• Around 50 % of organisations globally sit in mid-maturity( 4 – 6). They are adopting AI but not yet running it as a core finance capability

• Nearly one third self-identify as high maturity( 7 – 10), which makes the

‘ leader’ label common enough to warrant closer examination and broad enough that it can ' t be treated as a single operating reality

• The market is moving unevenly not sequentially, with a small group scaling, a large middle struggling to convert activity into operations and a tail that remains early

This uneven distribution matters more in finance than in most other business functions. Unlike experimentationheavy domains, finance AI must survive controls, audit, accountability and policy enforcement before it can scale into workflows that materially affect the business.

“ The real risk in finance AI isn’ t experimentation, it’ s getting stuck halfway,” said Hristo Borisov, CEO and Co-founder, Payhawk.

“ Many finance teams now have visible AI activity but lack the minimum structure needed to scale it safely under audit and control. The organisations that succeed won’ t be the loudest adopters, but the ones that make AI governable inside their finance operating model.”

AI maturity varies sharply by company context, where AI readiness in finance is strongly patterned by industry and company size. Tech organisations with more than 251 employees show the highest maturity levels globally, with over 70 % rating themselves as highly mature. Of smaller organisations in regulated and core-economy sectors( 50 – 250 employees), only 13.5 % report high maturity.

By contrast, large non-tech organisations overwhelmingly sit in the mid-maturity band, actively adopting AI but struggling to scale it into core finance operations.

A related structural signal helps explain this pattern. Higher AI maturity is more common in organisations with complex, multi-entity structures, where scale forces investment in standardisation, shared services and centralised controls.

But this does not guarantee AI readiness – without data consistency and data alignment, poor governance can create a lag.

A key implication of the research is that self-identified‘ AI leaders’ are not a uniform group. The headline maturity number masks wide variation in how finance teams deploy AI in practice. Some organisations have embedded AI into workflows with clear accountability. Others are moving fast without minimum guardrails or investing with intent but lacking the foundations to scale. �

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