AI-ready finance teams still lack rules & data base

Payhawk has published survey research suggesting that many finance teams that describe themselves as advanced AI users still lack the governance and data foundations needed to use AI more widely in core finance processes.

The study drew on a global survey of 1,520 finance and business leaders. It defined "AI leaders" as organisations that rated their AI maturity between seven and 10 out of 10, a group of 405 respondents.

Among these self-identified AI leaders, 74% lacked all the elements the research associated with scaling AI into day-to-day finance workflows such as close processes, financial controls, approvals, exception handling, audit trails and spend governance.

Governance gap

The findings suggest that what slows adoption inside finance teams may be shifting. Payhawk's data indicates that skills and tooling are more common than formal rules and usable data, raising questions about oversight in a function subject to regulatory and audit scrutiny.

The research identified five requirements it said determine whether AI moves from initial adoption to operational use in finance workflows: execution measures, minimum rules for AI use, skills and tools, committed budget, and data that is usable for AI analytics.

Only 26% of the AI leader group strongly agreed that all five were in place at the same time. The remainder lacked at least one foundation the study linked to scaling AI safely in high-accountability activities.

The results suggest that investment and experimentation may be moving faster than governance frameworks. In the leader cohort, 78% reported strong availability of AI skills and tools, 69% said budgets for AI had been committed, and 64% said they had execution measures in place.

Responses were weaker on minimum rules and data readiness. The research found that 32% of AI leaders had skills but lacked minimum rules for safer use, while 22% reported AI measures but still lacked minimum rules to scale consistently.

Data quality and availability also emerged as constraints. Two in five AI leaders (39%) did not strongly agree that their data can support AI-driven analytics effectively.

Rules and data

Payhawk described the imbalance as "rules debt" and "data debt", where AI activity expands without matching progress in governance rules or data foundations that finance teams trust for reporting and reconciliation.

The distinction matters because AI in finance often touches controls and approvals, affects audit trails and how decisions can be reviewed, and can shape spend controls across departments-areas that require consistent policy enforcement.

The survey also points to an uneven picture among organisations most confident in their AI maturity, suggesting high self-ratings do not always align with readiness for operational deployment across critical workflows.

According to the study, AI can be piloted with minimal infrastructure, but scaling requires an "operating stack", including clear rules and trusted data. It also links operational use to the ability to reconcile outputs against financial data.

On methodology, Payhawk said it worked with IResearch to interview senior professionals across eight countries, including C-suite leaders, vice-presidents, directors and senior individual contributors. The sample covered finance, accounting, sales, HR and procurement.

Industries represented included services, digital, manufacturing, healthcare, education, non-profit and business-to-consumer sectors. Responses were also split by company size, from 50 employees to 1,000 or more.

Payhawk is headquartered in London and sells spend management software covering bills, cards, expenses, travel and procurement.

A company statement positioned the issue as accountability in controlled workflows.

"In finance, AI only matters when you can delegate real work inside controlled workflows like approvals, reporting and audit trails," said Hristo Borisov, CEO and Co-Founder of Payhawk. "Our data shows the skills and experimentation are already there. What's missing is the operating stack, minimum rules and usable data that make AI accountable at scale."

Payhawk said the research is part of a wider CFO AI readiness report series and that it plans to publish further instalments.