AI for Finance Teams: Where It Helps in Close, Audit and Reporting

Every finance leader is being asked the same question: what is our AI strategy? Having spent years in audit and in PwC's National and Technical Accounting Research Office before running companies, my answer is practical. Start where the work is repetitive, high-volume and checkable, and keep judgment where judgment belongs.

Where AI already earns its keep

Reconciliations. Matching bank lines, intercompany balances and subledgers is the classic job for machine learning. Models learn the patterns behind matches that rules miss, and humans review the exceptions.

Accounts payable and receivable. Invoice capture, coding suggestions, duplicate detection and cash application are now mature uses that shorten the close and reduce errors.

Anomaly detection. Scanning every journal entry, rather than a sample, for unusual timing, amounts, users or accounts is a step change for internal audit and fraud prevention.

Drafting. Generative AI is useful for first drafts of variance commentary, policy summaries and board pack narratives, provided someone who understands the numbers edits the result.

Where judgment still decides

Revenue recognition, lease and financial instrument classification, impairment, going concern and complex transactions all require applying standards to facts. AI can retrieve the relevant guidance and summarise a contract, but the conclusion must be owned by a qualified professional who can explain it to an auditor and a regulator. Treating a model's answer as an accounting position is a control failure.

Controls for an AI-enabled finance function

• Document which processes use AI, what data they see and who reviews the output.
• Keep audit trails: inputs, outputs, reviewer and approval.
• Test models periodically against known answers, the same way you test any key control.
• Keep confidential financial data out of tools that do not meet your data protection obligations.

A 90-day plan for a finance team

Days 1 to 30: choose and measure. List the ten most time-consuming tasks in the monthly close. Pick two that are repetitive and rule-based, such as bank reconciliations or accounts payable coding. Measure the current time and error rate.

Days 31 to 60: pilot with controls. Run the AI tool in parallel with the existing process. Review every output. Document how the tool works, who approves its results and how exceptions are handled.

Days 61 to 90: decide and scale. Compare time saved and error rates. If the pilot succeeds, move it into production with a review step and update the process documentation. Then choose the next two tasks.

Skills finance professionals need now

• Data literacy: understanding where data comes from and how to test its completeness and accuracy.
• Prompting and review: asking AI tools precise questions and checking answers against source documents.
• Process design: redesigning workflows so people focus on analysis and judgment.
• Control thinking: knowing what could go wrong and how to detect it.

What auditors will look for

If AI tools affect financial reporting, external auditors will want to understand them. Expect questions about access controls, change management, how outputs are reviewed and how the tool's accuracy was tested. Teams that document these points from the start make audits faster and avoid control deficiencies.

A leader's view: redesign roles, not only tasks

When AI saves hours in the close, leaders should decide deliberately how that time is used: deeper analysis for business partners, stronger controls, faster forecasting or development for staff. Without that decision, saved time disappears into other routine work. Finance leaders who redesign roles around judgment and insight turn AI productivity into better decisions for the whole business.

Start small, measure, scale

The best programmes pick one painful process, measure hours and error rates before and after, and expand only when the numbers prove it. Finance teams that adopt AI this way gain capacity for the work that matters most: analysis, partnership with the business and better decisions.

Common questions

Can AI close the books?

AI can automate many close tasks such as reconciliations, matching and draft commentary, but accountants still need to review results, make estimates and approve entries.

Is it safe to use AI with financial data?

Yes, with approved enterprise tools, access controls, data protection agreements and a clear policy on what data may be shared.