As CFOs enter the budget round and are under pressure to invest in AI and automation, the findings provide a reality check on where the market actually stands and where the execution risk is highest.
The middle ground is now the focal point of the market
The CFO AI Readiness Report asked respondents to assess their organization's AI maturity on a scale of 1-10 (low: 1-3, middle: 4-6, high: 7-10). The results show:
- 50% are in the AI middle ground (4-6): some adoption, but no integrated AI capability within finance.
- 32% see themselves as high maturity (7-10): a broad group that varies widely in actual implementation.
- The market is moving unevenly and non-linearly: a small group is scaling up, the middle ground struggles to convert to actions in operations, and the laggards are stuck.
In finance, AI must meet controls, audit requirements, accountability, and policy before it can be embedded in workflows that have a direct impact on the business. This makes the gap between experimenting and scaling larger than in other industries.
'Payhawk is exactly at the intersection where AI ambition meets financial reality,' says Hristo Borisov, CEO and co-founder of Payhawk. 'We are in the workflows where approvals turn into expenditures, payments are executed, exceptions pile up, and audit trails are tested. That is why we are convinced that the blockage is not in experimenting, but in running AI within controls without losing accountability.'
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AI maturity varies greatly by sector and company size
Tech companies with more than 251 employees are leading the way: over 70% assess themselves as high maturity. Smaller organizations in regulated and core economy sectors* (50-250 employees) lag behind, with only 13.5% reporting high maturity. Large non-tech companies are primarily in the AI middle ground. Complex multi-entity structures more often report higher maturity, but data quality and governance remain decisive for actual AI readiness.
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Leaders versus laggards: not a simple story
The research shows that self-proclaimed 'AI leaders' vary significantly in how they actually deploy AI. Some organizations have already embedded AI in controlled workflows, while others move quickly without minimal safeguards or the foundations to scale.
The limiting factor for AI maturity in finance is not the technology itself, but whether organizations can deploy AI stably, defensibly, and repeatedly within financial control environments.
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