AI in the Finance Function: The UK CFO’s Action Plan for 2026

AI is delivering measurable gains in specific finance function use cases in 2026 — but only for CFOs who have moved beyond pilot fatigue to deploy governed, auditable automation across financial planning, close, and compliance processes.

The narrative has shifted sharply. Early 2023 AI transformation ambitions ran ahead of enterprise capability. By 2024, disillusionment had set in as pilots failed to scale. By 2025, a smaller set of clearly defined use cases — accounts payable automation, FP&A forecasting, financial close acceleration, and anomaly detection — had begun to deliver. In 2026, according to the CFO Connect State of AI in Finance report, the divide between finance functions that are capturing AI-driven efficiency and those still running manual reconciliation cycles has become a measurable competitive differentiator. The UK’s finance outsourcing sector alone is projected to be worth over £6.2 billion in 2026, driven in significant part by AI-enhanced service delivery. CFOs who have not yet built an AI governance framework for the finance function are now behind the curve.

Which Finance Processes Are Delivering Real AI Value in 2026?

Not all finance function use cases are equal. The clearest documented gains sit in four areas. First, accounts payable and receivable automation: AI-driven invoice matching, exception handling, and payment reconciliation are delivering cycle time reductions of 40–60% in well-governed deployments. Second, FP&A forecasting: machine learning models that incorporate external signals — pricing data, demand volatility, supply constraints, macro indicators — are producing rolling forecasts that adapt in near real-time rather than requiring quarterly manual refresh cycles. Third, financial close: AI-assisted reconciliation and variance explanation tools are compressing month-end close windows by material days in large finance functions. Fourth, fraud and anomaly detection: AI pattern recognition across transactional data is identifying payment fraud, duplicate invoices, and journal entry manipulation at a rate that manual controls cannot match.

According to the Infosys BPM AI in Finance report, FP&A is undergoing a decisive shift towards intelligence-led operations, with AI transforming financial planning into a living, continuously updated capability rather than a periodic projection exercise. This has direct implications for how CFOs should present financial outlooks to their boards — static annual forecasts are increasingly a lagging indicator of management quality rather than evidence of rigour.

• Executive Action: Map your five highest-volume finance processes against the four proven AI use case categories above. Prioritise automation where manual effort is highest and where errors carry regulatory or audit consequence.
• Brief the audit committee on how AI is being used in financial close and reporting processes — under the UK Corporate Governance Code 2024, AI-assisted controls must be disclosed where they are material to the integrity of financial statements.
• Establish a monthly metric on close cycle duration, forecast accuracy, and AP processing cost to track AI-driven improvement against baseline.

How Should CFOs Govern AI in the Finance Function?

AI governance in finance is not a technology question — it is a control framework question. Under the UK Corporate Governance Code 2024 and the Financial Reporting Council’s updated guidance on internal controls, CFOs are responsible for ensuring that automated processes that affect financial reporting are subject to the same rigour as manual controls. This means documented control objectives for each AI tool, defined human review thresholds where AI output is accepted without challenge, regular model validation against actual outcomes, and clear escalation paths when AI-generated outputs are outside expected variance ranges.

The ICO’s AI and Data Protection Code of Practice, published in 2026, also has direct finance function implications: where AI tools process personal data — including payroll, expense, or customer payment data — CFOs must ensure that data protection impact assessments have been completed and that processing agreements with AI vendors are compliant with UK GDPR. The CFO who delegates this to the CIO or DPO without maintaining finance-specific oversight is taking on personal accountability risk under the Senior Managers and Certification Regime if the organisation is FCA-regulated. Use the INFORMD AI governance assessment to evaluate your finance function’s current control maturity.

• Executive Action: Establish a Finance AI Control Register documenting every AI tool in use across the finance function, the control objective it supports, the human oversight mechanism, and the validation cadence.
• Require your external auditors to confirm how they are treating AI-assisted controls in the audit plan — this affects reliance decisions and the extent of substantive testing.
• Complete a UK GDPR data protection impact assessment for any AI tool processing personal financial data, and confirm vendor processing agreements are current.

How Is the CFO Role Itself Changing as AI Scales?

The CFO’s role in 2026 extends well beyond financial stewardship. As AI agents increasingly execute routine finance processes autonomously, the CFO becomes the orchestrator of human-AI collaboration: designing the workflows, setting the risk tolerance, monitoring the outputs, and translating AI-generated financial intelligence into board-level strategic decisions. This is a materially different skill set from traditional finance leadership, and organisations that have not invested in finance function AI literacy — at the team level, not just at the CFO level — are experiencing quality degradation rather than efficiency gains from their AI tools.

The practical implication is that CFOs must now treat AI capability as a core competency in finance function recruitment and development, not as a technology layer managed by IT. Finance analysts who cannot interrogate an AI-generated forecast, challenge an anomaly detection output, or identify when a model is producing results outside its reliable operating range are a governance liability. The FD Capital CFO AI guide notes that the finance function of 2026 is defined by its capacity to design and monitor AI-driven processes rather than to execute manual ones. Download the INFORMD capital approval and technology investment templates to embed AI governance into your finance function’s existing approval workflows. Further briefings on financial reporting, regulatory compliance, and AI governance are available in the INFORMD executive briefing library.

• Executive Action: Conduct a finance function AI literacy assessment across your team — identify who can interrogate and challenge AI outputs and where training gaps create control risk.
• Update finance function job descriptions and performance objectives to include AI governance competencies as a required capability, not an optional skill.
• Review the CFO’s board reporting pack — if it still presents quarterly static forecasts as the primary financial outlook, replace with rolling AI-assisted scenario ranges.

What Should CFOs Avoid When Deploying AI in Finance?

The most significant risk in finance function AI deployment is not technology failure — it is governance failure. Three patterns recur in organisations where AI has created rather than reduced financial control risk. First, shadow AI: finance teams adopting AI tools outside of approved procurement and data governance processes, creating unmonitored data flows and uncontrolled processing of sensitive financial data. Second, model drift: AI forecasting and anomaly detection tools that are not regularly validated against actual outcomes begin to produce outputs that reflect historical patterns rather than current business conditions, creating a false sense of forecast accuracy. Third, accountability diffusion: where AI output is accepted without documented human review, the audit trail for financial decisions becomes unclear, creating audit qualification risk and personal accountability exposure for the CFO under the Companies Act 2006.

• Executive Action: Conduct a shadow AI audit across the finance function — identify any AI tools in use that are not on the approved Finance AI Control Register and bring them into governed processes or decommission them.
• Establish a quarterly model validation review for all AI tools used in forecasting and anomaly detection, comparing predicted versus actual outcomes and documenting the review.
• Confirm with your company secretary and legal counsel that the CFO’s personal accountability position under the Companies Act 2006 is clearly defined in relation to AI-assisted financial reporting.

INFORMD provides intelligence briefings, tools and frameworks for senior business leaders across technology, finance, strategy and compliance. Based in Milton Keynes, UK, we help executives stay informed and act with confidence. Explore our full briefing library or access our free assessment tools.