How a Financial Controller Uses AI in the Finance Function

Artificial intelligence has moved quickly from a topic finance professionals could ignore to one that is reshaping how the finance function operates day to day. For the Financial Controller — who owns the month-end close, the management accounts, the controls environment and the numbers the board relies on — it is a practical question: which parts of the role can be done faster and more accurately, and which must stay firmly under human control however capable the tools become.

This guide is written for working Financial Controllers and the senior finance professionals who report to them. It sets out, task by task, where AI genuinely helps, where it is dangerous, which tools are in practical use, and how to adopt them without compromising accuracy, control and accountability. If your question is the bigger one — whether AI will eventually do the Financial Controller’s job — our companion piece, Can AI replace a Financial Controller?, takes that on directly. This page is about what to do this month.

Why AI Matters to the Financial Controller Specifically

Much of the commentary on AI in finance is pitched at CFO and board level. The Financial Controller’s question is narrower and more immediate: of the dozens of discrete tasks that make up the controlling function each month, which can AI make faster or better right now, and which carry risks that make adoption a mistake?

The answer matters because the role is unusually exposed to the kind of work AI does well. A large share of the controlling function is structured, repetitive, rules-based work performed under time pressure on a monthly cycle: reconciling accounts, investigating variances, drafting commentary, preparing schedules, formatting reports, checking consistency between documents. These are precisely the tasks where a capable AI assistant can compress hours into minutes. At the same time, the Financial Controller is ultimately accountable for the accuracy of the output, so adopting AI in this role demands more care, not less, than in functions where errors are cheaper.

Our wider AI in finance library covers the full range of applications; this guide concentrates on the controller’s seat.

Where AI Genuinely Helps in the Finance Function

The most useful way to think about AI in the controlling function is task by task rather than as a single sweeping change. Below are the areas where the efficiency gains are real and the risks are manageable with sensible controls.

Drafting Variance Commentary and Management Reporting Narrative

Writing the management accounts narrative is one of the most time-consuming recurring tasks in the controlling function, and one of the most amenable to AI assistance. Each month someone must explain why revenue moved against budget, why a cost line came in higher than forecast, and what the variances mean for the full-year position. The underlying analysis — the actual numbers and the actual reasons — must come from the finance team, who understand the business. But turning that analysis into clear, consistent, well-structured prose is exactly the kind of task an AI assistant handles well.

A sensible workflow is to give the assistant the variance figures and a few short notes on the drivers, and ask it to draft the commentary in the house style. The output is a first draft that the Financial Controller reviews, corrects and approves — never a final document published without human review. Used this way, AI can cut the time spent on drafting substantially while improving consistency. Our guide to AI for variance commentary sets out prompts and review steps in more detail.

Reconciliations and Anomaly Detection

Reconciliation work consumes a significant share of the close timetable. AI tools, particularly those embedded in modern finance systems, can perform first-pass matching and surface only the exceptions that need human judgement.

Consider a balance sheet reconciliation on a control account with several hundred open items at month-end, of which the great majority match cleanly and a handful do not. The AI-assisted approach has the tool perform the matching, present the unmatched items with a plausible explanation for each (a timing difference here, a miscoding there, a duplicate posting somewhere else), and leave the finance professional to confirm or reject each one. The work falls from hours to minutes, attention is spent only where judgement is required, and — critically — a human still signs off every break before the reconciliation is closed. The control is preserved; only the drudgery is removed. This is the pattern that recurs throughout effective AI use in finance: the machine handles the volume, the human handles the judgement. See AI for reconciliations for a fuller treatment.

Accounts Payable and Accounts Receivable

The purchase and sales ledgers are where AI has been deployed longest and most widely. On the payables side, tools read supplier invoices, extract the key fields, match them to purchase orders and goods received, suggest the nominal coding and flag duplicates or unusual changes to supplier bank details. On the receivables side, they help prioritise the collections list, draft chasing emails in an appropriate tone, and match incoming receipts to open invoices where the remittance is incomplete.

For the Financial Controller the role here is design and oversight rather than processing. Someone has to decide the approval thresholds above which a human must review, who can release a payment the tool has prepared, and how a change of supplier bank details is verified outside the system — a control that matters more, not less, when the rest of the process is automated, because payment-diversion fraud targets exactly that step. Our guide to AI for AP and AR covers the workflow and the controls together.

Accelerating the Month-End Close

The month-end close is the defining rhythm of the controlling function, and almost every component of it has an AI-assisted version that is faster than the manual equivalent. Accruals and prepayments can be calculated with assistance, intercompany positions reconciled, and consistency checks between the trial balance, management accounts and board pack automated — compressing the timetable without removing the controller’s accountability.

For a fuller treatment of close design independent of AI, see our guide on optimising the month-end close, and for the AI-specific steps, AI for the month-end close. The AI dimension is best understood as an accelerant layered on top of a well-designed process — it speeds up a good close and exposes the weaknesses of a poor one.

Board Pack and Report Preparation

Preparing the board pack involves a great deal of assembly, formatting and consistency-checking that is necessary but not intellectually demanding. An AI assistant can check that narrative figures match the tables and that commentary is consistent with the prior month, flagging what a tired reviewer might miss late on a Friday. The judgement about what the board needs to see, and how to frame difficult messages, remains entirely human. Our guide on board pack preparation covers the judgement side; AI handles the assembly side.

Cash Flow Forecasting and Scenario Work

Building and maintaining a rolling cash flow forecast is a task where AI can both speed up the mechanical work and improve the quality of scenario analysis. An assistant can help construct the model structure, write the formulas, and rapidly generate sensitivity scenarios that would take much longer to build by hand. The Financial Controller retains responsibility for the assumptions — which are a matter of business judgement, not computation — and the model itself should live in a spreadsheet or planning tool where the arithmetic is deterministic. Our guide on building a 13-week rolling cash flow forecast sets out the underlying discipline, and AI for cash flow forecasting covers where the tools add most.

Reviewing Contracts, Leases and Documents for Financial Impact

Reading documents to extract their financial implications — a lease to assess for its accounting treatment, a customer contract that drives revenue recognition, a facility agreement whose covenants must be tracked — is an under-appreciated drain on time. AI assistants are well suited to a first-pass review: extracting key terms, summarising the financial implications and flagging clauses that need attention. The treatment decision remains a qualified human’s responsibility; only the laborious initial read is compressed.

Ad-Hoc Analysis and Answering the “Quick Question”

The CEO wants to understand a margin movement; the FD needs a view of a trend before a meeting. An AI assistant can help organise the data, suggest the right cut and draft a clear explanation. The numbers still come from the finance system and are still verified, but the turnaround on the “quick question” improves markedly.

Testing and Monitoring Controls

A less obvious but valuable use is in the controls environment itself. AI tools can review full journal populations rather than samples, flagging entries posted outside normal hours, round-sum amounts, unusual account combinations, or journals raised and approved by the same user. They can compare user access lists against role definitions, and summarise exceptions for the Financial Controller’s review. This does not replace a properly designed control framework, but it gives the controller a cheaper, broader view of whether the framework is working — and good evidence to show the auditors.

Where AI Is Dangerous in Finance

An honest guide must be as clear about the risks as about the benefits, because the Financial Controller will answer for an error regardless of whether a tool produced it. There are several areas where naive AI use is a genuine hazard.

Confidentiality and Data Security

The single most important risk is the inadvertent disclosure of confidential financial data. Putting unpublished results, commercially sensitive information, payroll data or customer data into a consumer AI tool that may retain inputs or use them to train its models is a serious breach of confidentiality and, where personal data is involved, potentially of UK GDPR and the Data Protection Act 2018. Payroll, expenses, debtor ledgers and supplier records almost always contain personal data, so this is not a theoretical concern for a finance team.

The Financial Controller must understand exactly how any AI tool handles the data submitted to it: whether inputs are retained, whether they are used for training, where they are processed, and what contractual and security assurances the provider gives. Enterprise tools with appropriate data-handling commitments are a different proposition from free consumer tools, and the distinction matters enormously in finance. Where a new use of personal data is likely to be high risk, a data protection impact assessment may be required, and the data protection officer (if the business has one) should be involved early. The Information Commissioner’s Office guidance on AI and data protection is the authoritative UK reference, and the National Cyber Security Centre publishes practical security guidance worth sharing with IT. Our guides to AI data security in finance and connecting AI to finance data safely go further.

Hallucination and the Verification Burden

AI language models can produce output that is fluent, confident and wrong. In finance, where a plausible but incorrect number can flow into the management accounts and the board pack, this is a fundamental risk. The discipline that addresses it is simple to state and essential to enforce: AI output is never trusted without verification. A figure produced or transformed by an AI tool must be checked against source, exactly as a junior team member’s work would be reviewed.

Calculation and Computation

Language models are not calculators, and using them to perform arithmetic directly is a category error. The correct pattern is to use AI to help structure a calculation, write a formula or build a model, and then perform the computation in a deterministic tool — a spreadsheet, the finance system, a purpose-built model — where the arithmetic is reliable and auditable. Asking a chat assistant to add up a column of figures and trusting the result is precisely the kind of misuse a Financial Controller cannot afford.

The Audit Trail

Every figure in the management accounts must be traceable to its source. AI-assisted work must preserve that trail, which means the Financial Controller needs to understand and document how AI tools have been used in producing the numbers — not to satisfy bureaucracy, but because the auditors will ask, and because the integrity of the accounts depends on it. In practice that means recording which tool was used for which task, keeping the inputs and outputs for anything that feeds a reported figure, and evidencing the human review in the normal way: a reviewer’s sign-off on the reconciliation, a tracked approval of the commentary. Auditors work to the FRC’s auditing standards, which expect them to understand the systems that produce the figures; our guide to what auditors ask about AI sets out the questions to prepare for.

Building a Sensible AI Approach as a Financial Controller

Adoption works best as a deliberate, controlled programme rather than an ad-hoc scramble.

Start with the low-risk, high-volume tasks. Commentary drafting, formatting, consistency-checking and first-pass reconciliation are the natural starting points: the efficiency gain is large, and an error is caught in the normal review process rather than flowing into the numbers undetected.

Establish clear rules about what data may and may not be put into which tools. This is the single most important control, and it should be written down and understood by everyone in the team, not held informally in the Financial Controller’s head. A short, explicit policy — these tools are approved, this data may never be submitted to an external tool, this is how we handle personal data — prevents the most serious risks. Our guide to an AI usage policy for finance teams provides a template to adapt.

Keep a human in the loop on everything that touches the numbers. The phrase is a cliché, but in the controlling function it is the whole game. Every output that affects the management accounts, the board pack or any external financial document is reviewed and approved by a qualified person before it goes anywhere. Our guide to human-in-the-loop AI controls shows how to build that review into the close rather than bolting it on.

Invest in the team’s AI literacy. The Financial Controllers who get the most from these tools help their teams understand both the capabilities and the limits — how to prompt effectively, how to spot a plausible-but-wrong output, where the confidentiality lines are. The professional bodies are increasingly treating this as a core skill; the ICAEW’s technology resources are a useful starting point, and our guide to prompt engineering for finance is written for finance teams specifically.

The Tools Financial Controllers Actually Use

The tools fall into three broad groups, and most finance teams end up using a mix.

• General-purpose assistants. Microsoft 365 Copilot, ChatGPT, Claude and similar assistants are used for drafting, summarising documents, explaining and writing spreadsheet formulas, and structuring analysis. The key question is which version the business licenses: an enterprise subscription with agreed data-handling terms is a different proposition from a personal free account. Our comparison of Claude, Copilot and ChatGPT for finance looks at the practical differences.
• AI features inside the accounting system. Cloud accounting platforms such as Xero and Sage have long suggested bank-feed matches and coding, and both vendors have been adding AI assistant features. Capabilities, and which plans include them, change frequently, so check the vendor’s current documentation rather than relying on marketing summaries. The same applies to the larger ERPs.
• Specialist finance tools. Dedicated products for invoice capture, reconciliation, close management and forecasting, often with machine-learning matching built in. These tend to deliver the most reliable gains on high-volume tasks because they are designed around the finance process, but they require integration and proper configuration.

Whatever the tool, the same tests apply: where does the data go, can the output be traced and evidenced, and who reviews it before it touches a reported figure? Our overview of the AI tool landscape for finance covers the categories in more depth.

Getting Started: What to Try This Month

For a Financial Controller who is convinced of the case but unsure where to begin, the most effective approach is to pick a single low-risk task and run it in parallel with the existing manual process for one cycle. The natural first candidate is variance commentary. In the next close, have a member of the team draft the narrative as they always would, and separately use an approved AI assistant to produce a draft from the same variance figures and driver notes. Compare the two. The AI draft will usually need correction, but it gets to a structured first version far faster than a blank page, and the comparison builds the team’s intuition for where the tool helps and where it falls short.

From there, extend deliberately. Once commentary drafting is working and trusted, add consistency-checking of the board pack, then first-pass reconciliation on a low-risk control account, then document summarisation. At each step the question is the same: is the efficiency gain real, and is an error — if one occurs — caught in the normal review process rather than flowing undetected into the numbers? If not, leave it for now.

What This Means for the Financial Controller Role

AI does not remove the need for a Financial Controller. It changes the balance of how the role spends its time. High-volume mechanical work is increasingly automated or assisted, freeing time for the analytical, advisory and judgement-based work that has always been the most valuable part of the role. Our companion piece, Can AI replace a Financial Controller? (Spoiler: not yet), sets out why accountability, judgement and challenge keep the role firmly human.

On balance this is a positive shift. The skill that matters is neither resistance nor uncritical enthusiasm, but the judgement to use the tools where they help, refuse them where they are dangerous, and keep the accountability the role has always carried.

Skills to Hire For in an AI-Capable Financial Controller

For employers, the question is how to recognise a Financial Controller who uses these tools well. The Financial Controller job description still starts with the fundamentals — qualification, close ownership, audit management, team leadership — but the strongest candidates now add a layer on top:

• A specific example of a task they have automated or AI-assisted, what it saved, and how the output was reviewed.
• A clear view of the limits — where they decided a human had to stay in the loop, and why. Candidates who describe both a use and a limit have usually implemented something.
• Data handling awareness: which tools they would approve, what data they would never submit, and how UK GDPR applies to finance data.
• Data capability: comfort querying the underlying data, building reliable models and reading system reports critically, rather than depending on others to extract numbers.
• Control design: experience writing or applying an AI usage policy, and evidencing review for the auditors.

Our article on how AI is changing what employers look for in an FC expands on this, and our Financial Controller recruitment service assesses these capabilities before you see a CV. If you need cover while you run a permanent search, an interim Financial Controller can usually be introduced within days; a permanent shortlist typically follows within one to two weeks of an agreed brief.

Frequently Asked Questions

What is the best first AI task for a Financial Controller?

Variance commentary. It is high-volume, low-risk and easy to review: run an AI draft alongside the normal manual draft for one close and compare. First-pass reconciliation on a low-risk control account is a sensible second step.

Can we put management accounts data into ChatGPT or Copilot?

Only through a tool the business has approved, with data-handling terms you have checked — typically an enterprise licence rather than a personal account. Unpublished results and anything containing personal data, such as payroll, should never go into an unapproved consumer tool. Write the rule down in an AI usage policy.

Will the auditors accept AI-assisted work?

Generally yes, provided the audit trail is intact: you can show what the tool did, what the inputs and outputs were, and who reviewed the result before it fed a reported figure. Expect to be asked how AI is used and controlled.

Do AI skills change what a Financial Controller is paid?

Pay is still driven mainly by scope, ownership context and location; our Financial Controller salary guide sets out typical ranges. AI capability tends to show up in who is shortlisted rather than as a separate premium.