The question finance professionals are most commonly asking about AI in 2026 is not whether to adopt it, but where to start. The tool landscape is complex, the use cases are numerous and the time available to experiment is limited. For finance teams looking to move from occasional AI experimentation to systematic adoption, the most productive starting point is role-specific: understanding specifically how AI changes the FC’s job, the management accountant’s job, the financial accountant’s job, and how to build capability across the team systematically.
This piece covers the role-specific AI landscape for the finance function and the practical steps for building AI literacy that translates into genuine productivity improvement.
How the Financial Controller Uses AI
The Financial Controller role is one of the most demanding in the finance function in terms of breadth — the FC owns the close, the management accounts, the audit relationship, the financial controls, the statutory accounts and, increasingly, the finance system. AI tools are being applied across most of these areas, with varying degrees of maturity.
The most productive AI applications for the FC in 2026 are in the close cycle: reconciliation assistance, management commentary drafting and exception identification. Beyond the close, FCs are using AI for technical accounting research (asking models to explain the application of specific standards to specific transactions, then verifying the output), for drafting internal control documentation and policies, and for reviewing and summarising lengthy regulatory guidance.
The FC who has integrated AI tools effectively into the monthly close is typically saving four to eight hours per month — the equivalent of a day’s capacity that can be redeployed into higher-value work such as commercial analysis, business partnering or finance team development. The FCs achieving the best results are those who have taken the time to build specific prompting routines for their most frequent tasks, rather than using AI tools ad hoc.
How a Financial Controller Uses AI →
The specific AI applications that are changing the FC role in 2026 — from the month-end close through to technical accounting research and financial controls — with the prompting approaches that work.
Using AI Inside Excel for Finance
Excel remains the primary tool of the finance function, and the integration of AI into Excel — principally through Microsoft Copilot for Excel — is one of the most immediately accessible AI applications for finance teams. Copilot can write formulas in plain English, analyse data and produce charts, explain what a formula does, identify errors in a model, and generate summary tables from large datasets.
The practical applications most valued by finance teams include: formula generation for complex calculations (Copilot writes the formula; the finance professional verifies it), data cleaning and restructuring (transforming a data export from a financial system into a usable format for analysis), and quick analysis of large datasets (asking Copilot to identify the top ten cost categories, the variance from budget by line, or the months with the highest revenue).
Copilot for Excel is not a replacement for Excel proficiency. Finance professionals who do not understand the formulas Copilot generates cannot verify that they are correct — and Copilot does generate incorrect formulas, particularly for complex nested functions or dynamic array formulas. The correct workflow is: Copilot generates the formula, the finance professional understands it well enough to verify it, then uses it. The productivity gain comes from eliminating the time spent looking up formula syntax, not from removing the need to understand what the formula does.
Beyond Copilot, AI tools are also being used to generate Excel templates, build financial model structures and produce data visualisations. The Microsoft Copilot for Excel documentation sets out the current capabilities and limitations.
Using AI Inside Excel for Finance →
The practical guide to Copilot for Excel — what it can and cannot do, the formula verification approach, and the specific finance tasks where it saves the most time.
Building AI Literacy Across the Finance Team
The most common AI adoption pattern in finance is one or two team members experimenting with AI tools independently, developing effective workflows, and then not sharing what they have learned. The productivity gains remain individual rather than becoming organisational. Building AI literacy systematically — across the whole finance team, in a structured way — requires a deliberate programme rather than hoping for organic spread.
The components of an effective finance team AI literacy programme are: a shared understanding of which AI tools are approved for use with which types of data (the data security policy); a shared prompt library covering the team’s most frequent use cases (reducing the learning curve for team members who have not yet experimented); structured sharing of new use cases and prompting approaches (a monthly team meeting item is sufficient); and individual practice with low-stakes tasks before AI is used on high-stakes outputs.
The CFO or FD who invests time in building this capability across their team is creating a structural advantage that compounds over time. The finance function with systematic AI literacy will consistently produce more analysis, more commentary and more insight per head than the one where AI adoption is patchy and individual. As AI tools improve through 2026 and 2027, the gap between teams that have built the capability and those that have not will widen.
Building AI Literacy Across a Finance Team →
The programme for building systematic AI capability across the finance function — from the data security policy through to the shared prompt library and the structured sharing approach.
What Auditors Are Now Asking About AI
Finance teams that have adopted AI tools should expect their external auditors to ask about it. The audit profession is developing its understanding of AI-assisted financial reporting, and auditors are increasingly asking specific questions about the AI tools used, the review processes applied to AI output, and how the finance function ensures that AI-generated work meets the required standards.
The Financial Reporting Council and the major audit firms have published guidance making clear that the auditor cannot simply accept AI-generated work without understanding the AI’s role in producing it and verifying the output. In practice, this means that finance teams should be prepared to explain: which AI tools were used, for which specific tasks, what data was provided to the AI, what review process was applied to the AI output before it was used in the financial statements, and how the finance team verified that the output was accurate and complete.
The finance teams that handle auditor AI questions most effectively are those that have documented their AI use in advance — as part of the AI governance framework — rather than having to reconstruct the answer when the auditor asks. A simple log of AI-assisted work, the review process applied and the name of the finance professional who took responsibility for the verified output is sufficient for most purposes.
What Auditors Ask About AI in Finance →
The specific questions auditors are now asking about AI-assisted financial reporting — and the documentation and governance framework that enables the finance team to answer them confidently.
AI Adoption: Where Finance Teams Should Focus in the Second Half of 2026
The pattern of AI adoption across the finance profession in the first half of 2026 shows a clear division: teams that have invested in building systematic capability are generating measurable productivity gains; teams that have experimented sporadically are not seeing consistent benefits. The second half of 2026 is the window in which the gap between these two groups will become visible in the quality and speed of financial reporting.
The priority areas for finance teams that have not yet built systematic AI capability are, in order: establishing the data security policy (which tools can be used with which data), building a shared prompt library for the three or four most time-consuming regular tasks, and ensuring the FC or FD has the technical understanding to verify AI output rather than accepting it without review. These three steps can be completed in a matter of weeks and create the foundation for progressively expanding AI use into more complex tasks.
For finance teams that are already using AI systematically, the priority is extending use into the higher-value analytical applications — FP&A forecasting support, technical accounting research, scenario modelling — where the productivity gains are larger but the human oversight requirements are also greater.
The Accountancy Capital Knowledge Centre contains guides covering every stage of this journey, from understanding what AI in finance is and is not through to role-specific guides for FCs, FDs, management accountants and financial accountants. See AI in Finance Hub to explore the full library.
