Financial planning and analysis is one of the finance function disciplines where AI is creating the most immediate and measurable impact. The FP&A team’s core activities — building forecasts, running scenario analysis, preparing board pack financial sections, modelling the impact of business decisions — involve the kind of structured, repetitive analytical work that AI tools are well-suited to accelerate. This is not future speculation. FP&A teams in the UK are already using AI to compress forecast cycles, improve scenario coverage and produce board pack financial commentary faster than was possible with manual processes.
This piece covers four specific AI applications that are changing FP&A and financial accounting practice in 2026, and the governance question that determines whether those applications are safe to rely on.
AI for FP&A Forecasting: What Is Actually Changing
The most significant AI application in FP&A is not replacing the financial model — the three-statement model, the rolling forecast, the driver-based planning model — but augmenting the work that happens around it. Specifically: generating the assumptions, analysing the variance between forecast and actual, and producing the management commentary that explains the financial performance to a non-financial audience.
AI tools are being used by FP&A teams to analyse historical data and surface patterns that inform forecast assumptions, to run scenario analysis across a wider range of variables than manual modelling typically permits, and to flag where the current forecast deviates significantly from the trend suggested by the underlying data. The output is a more considered, better-supported set of assumptions — not a forecast generated by AI, but a forecast generated by the FP&A team using AI to do the analytical groundwork faster.
The caveat is important: AI-assisted forecasting still depends entirely on the quality of the data and the quality of the judgment applied to the AI’s output. The finance teams getting the best results are those using AI as an analytical tool within a robust planning process, not as a replacement for that process. An AI tool cannot know that the sales team has just changed its go-to-market approach, or that a major customer is considering moving its contract. The FP&A professional who applies the AI output without that context will produce a more analytically sophisticated but commercially inaccurate forecast.
How FP&A teams are using AI to improve forecast assumptions, expand scenario coverage and accelerate the planning cycle — with honest assessments of where AI adds value and where human judgement remains irreplaceable.
AI for Board Pack Drafting
The board pack is one of the most time-consuming documents the finance function produces, and one of the most suitable for AI assistance. The financial sections of the board pack — the P&L commentary, the balance sheet review, the cash flow narrative, the KPI dashboard, the variance analysis — follow a consistent structure, reference structured financial data, and require a specific register: clear, precise and suitable for a non-finance-specialist board audience.
AI tools are being used to produce first drafts of board pack financial commentary from the underlying data. The workflow typically involves providing the AI with the prior period figures, the current period actuals, the budget and a brief context note on the key business developments, then asking it to produce structured commentary section by section. The output is reviewed and edited by the FP&A or FC to add the commercial context and management judgement that the AI cannot supply.
The boards that are reading AI-assisted board packs in 2026 are generally not aware that the commentary was AI-assisted — because the finance team is editing the output to the standard they would apply to any board pack. The FDs and FCs reporting the best results describe a process that saves two to four hours per board pack cycle without reducing the quality of the output. The key is treating the AI output as a draft, not as a finished product.
The financial section is appropriate for AI assistance. The strategic context and the management commentary on forward-looking items — what the business is doing differently, what the risks are, what the opportunities are — still require the finance team to write from first principles.
The specific workflow for using AI to produce board pack financial commentary — what to provide as input, how to structure the prompts, what to review and edit, and where AI should not be used.
AI for Financial Accountants: The Specific Use Cases
The financial accountant role — focused on statutory accounts, technical accounting, external audit management and regulatory reporting — is less frequently discussed in the context of AI than FP&A or management accounting, but there are specific applications that are already delivering value.
Technical accounting research is one of the most immediate. Financial accountants frequently need to research the application of accounting standards to specific transactions — the treatment of a complex lease under IFRS 16, the impairment testing requirements for goodwill under IAS 36, the revenue recognition implications of a new contract structure under IFRS 15. AI tools, when prompted correctly with the specific facts of the transaction, can produce useful first drafts of the technical accounting analysis that the financial accountant then verifies against the standard and applies to the specific facts.
Disclosure drafting is another productive application. The notes to the statutory accounts follow a structured format that is well-suited to AI assistance — once the financial accountant has determined the correct accounting treatment, the AI can produce a first draft of the disclosure that conforms to the required format and language. This is particularly useful for the more routine disclosures (going concern, related party transactions, operating lease commitments) where the structure is consistent but the drafting is time-consuming.
The financial accountant should approach AI assistance with the same scepticism they would apply to a junior team member’s first draft: verify the technical accuracy, check the specific standard references, and ensure the disclosure is complete and not misleading. The AI does not have access to the most recent standard amendments or interpretations, and technical accounting standards change.
AI for the Financial Accountant →
Where AI tools add genuine value in the financial accountant role — technical accounting research, disclosure drafting and audit preparation — and the verification approach that makes AI assistance safe to rely on.
Human-in-the-Loop AI Controls: The Governance Framework
As AI tools become embedded in finance function workflows, the governance question becomes more urgent: who is responsible for verifying AI output, what review processes are required, and how do you ensure that AI-assisted work meets the professional and regulatory standards that apply to financial reporting?
The human-in-the-loop principle — ensuring that a qualified professional reviews and takes responsibility for AI-generated output before it is used in financial reporting or management decision-making — is the foundation of responsible AI adoption in finance. It is also, increasingly, what auditors and regulators expect.
The Financial Reporting Council has published guidance on the use of AI in audit that emphasises the auditor’s responsibility to understand and verify AI-assisted work. The same principle applies to the finance function: the FC, FD or financial accountant who relies on AI output without applying professional judgement to verify it remains professionally responsible for the output, regardless of whether it was AI-generated.
The practical governance framework for AI use in finance includes: defining which tasks are appropriate for AI assistance (and which are not), establishing review requirements for AI output before it is used in financial reporting, maintaining an audit trail that identifies AI-assisted work, and ensuring that the finance team understands the limitations of the specific AI tools they are using.
Human-in-the-Loop AI Controls →
The governance framework for AI adoption in the finance function — what review processes are required, what the FRC expects, and how to build an AI control environment that meets professional standards.
Connecting AI to Finance Data: The Infrastructure Question
One of the most significant barriers to productive AI adoption in finance is the data connection problem: the most useful AI applications for finance require access to the financial data in the systems the finance team uses — the ERP, the FP&A platform, the data warehouse — and most general-purpose AI tools do not have that access by default.
The approaches finance teams are using to bridge this gap include: exporting data from financial systems and providing it in structured formats to AI tools (the most common approach, appropriate for non-real-time analysis), using AI tools with API connections to financial data sources (more powerful but requiring technical implementation), and deploying AI features built into the financial systems themselves (Copilot in Excel, AI features in Workday, Anaplan AI).
The data connection question intersects directly with the data security question. Connecting AI tools to financial systems requires careful consideration of what data the AI can access, how it processes and stores that data, and what the access controls are. Finance teams that are investing in AI data connections should involve their IT security and legal teams in the implementation design, not as an afterthought.
Connecting AI to Finance Data Safely →
How to connect AI tools to financial systems — the technical approaches, the data security requirements, and the governance framework for AI that has access to real financial data.
See the Accountancy Capital Knowledge Centre for the full library of AI guides across every finance function role. The AI in Finance Hub is the starting point for teams building their AI strategy, with links to all task-specific, tool-specific and governance guides. For the FC or FD hiring AI-capable finance professionals, see AI Finance Recruitment.
