45% of finance leaders still lack minimum rules for AI use
New Payhawk research reveals ‘AI leaders’ are split into six operating postures – not a single maturity pathway
Sofia, 15th April 2026 — Almost half (45%) of organisations that consider themselves ‘AI leaders’ lack the baseline governance needed to safely scale AI in finance workflows, according to new research from Payhawk.
The research also challenges a common assumption that AI maturity progresses along a defined maturity ladder. Even within the ‘leader’ category, AI readiness is split into distinct adoption postures, each constrained by different readiness gaps. Data reveals that the real constraint on scaling is not AI capability, but governability: whether the organisation can defend, trace, and audit what AI does inside finance workflows.
These findings are based on a global survey of 1,520 finance and business leaders. ‘AI leaders’ (subset n=405) are defined as organisations that rated their AI maturity between 7 and 10 out of 10.
‘Rules’ and ‘data’ debt: why AI programs are stalling
Five operational requirements determine whether AI can move from ‘adopted’ to ‘operational’ inside finance workflows. They are: execution measures in place, minimum rules for AI use, skills and tools, a committed budget, and data usable for AI analytics. Only 26% of AI leaders have all five requirements in place.
The research segments leaders into six operating postures based on how they score across these five requirements:
- Scaled adopters (26.9%) — strong across all five requirements. These organisations have the full operating stack.
- Incremental improvers (17.5%) — AI readiness exists in pockets across the stack, but no single dimension is decisively strong.
- Execution-led implementers (16.0%) — strong on execution and skills, but minimum rules are absent. This is the clearest ‘rules debt’ posture.
- Agent-first, control-later (14.1%) — enthusiasm and experimentation outpace governance. Minimum rules are absent, and execution readiness is limited.
- Governance-forward scalers (13.8%) — strong rules and execution, but data readiness is weak (only 30% strongly agree). This is the clearest ‘data debt’ posture.
- Control-first planners (11.6%) — skills, budget, and data are relatively strong, but execution measures are not in place. Capability exists without deployment.
Figure 1: Leaders split into six operating postures, not a ladder
These two systemic gaps – ‘rules debt’ and ‘data debt’ – explain why scaling breaks down.
- Rules debt occurs when organisations deploy AI faster than they establish governance, leading to systems that cannot be audited, explained or safely embedded into workflows involving approvals, compliance or financial controls. Two postures — Execution-led implementers and Agent-first, control-later — carry this pattern, together accounting for roughly 30% of leaders.
- Data debt occurs when governance and execution are in place, but underlying data is inconsistent, incomplete or fragmented. In these cases, organisations can control AI usage – but cannot trust its outputs at scale. The clearest carrier is Governance-forward scalers.
The research highlights a clear imbalance. While 78% of self-reported ‘AI leaders’ report strong skills and tools, only 55% have minimum governance rules in place – the lowest-ranked readiness factor.
The rules debt explains why many organisations appear “advanced” in activity and still struggle to move beyond narrow assistive use cases. This often concentrates in smaller, faster-moving contexts. On the other hand, the data debt explains why some organisations seem disciplined and well-governed yet still fail to scale AI into core finance operations. The data debt concentrates in complex, governed contexts.
The research also identifies a common and costly mismatch: organisations investing in more AI capability when the real blocker is governance infrastructure, or building policy frameworks when the real blocker is data quality. In both cases, progress stalls because the operating constraint being addressed is not the one actually limiting scale.
“Finance AI scaling feels inconsistent because organisations are progressing unevenly across the capabilities that underpin scale”, said Hristo Borisov, CEO and Co-Founder of Payhawk. “Many organisations are investing in more AI when the real bottleneck lies elsewhere – in rules or data. Scaling AI in finance is fundamentally an orchestration challenge: coordinating rules, data, and accountability across workflows. Those that only address some readiness requirements will face inherent trade-offs and remain stuck in assistive use cases”.
You can read the third instalment of Payhawk’s CFO AI Readiness Report here.
Methodology
To gain insights into how CFOs are adapting to the changing landscape, Payhawk partnered with IResearch to interview 1,520 senior professionals globally. Using affirmative statements developed in close collaboration with finance and business leaders, IResearch conducted interviews across eight countries to reflect genuine operational realities and challenges. Coverage included:
- Regions: DACH, Spain, France, Benelux, UK & Ireland, United States*
- Seniority: C-suite, VPs, Directors, and senior individual contributors*
- Functions: Finance, Accounting, Sales, HR, Procurement*
- Industries: Services, Digital, Manufacturing, Healthcare, Education & Non-profit, B2C*
- Company size: 50–100 FTE, 101–250 FTE, 251–500 FTE, 501–1,000 FTE, and 1,000+ FTE*)