AI in Finance: How It Is Changing FP&A

AI in finance has moved from promise to a tool many teams already use day to day. In FP&A (financial planning and analysis) its value is concrete: it helps explain variances, flag anomalous data, draft reports and answer questions about the numbers without building a new pivot table every time. What it does not replace is the finance team’s judgement or its accountability for the figures. Here is what it can do, what to watch and how to start.

What AI in finance means for FP&A

AI in finance is not a system that decides for you. It is a set of models that read structured data (budgets, actuals, forecasts, headcount) and text (comments, policies, close notes) to produce analyses, summaries and proposals that a person reviews. If you need a refresher on the discipline itself, start with what FP&A is.

Two families of techniques are often lumped together:

• Statistical and machine learning models, which work on time series to project sales, costs or cash and to flag outliers.
• Language models, which understand plain-language questions, write explanations and summarise information, but do not calculate reliably on their own.

The best results usually come from combining them: a deterministic engine does the maths on verified data, and the language model explains, summarises and converses.

Practical use cases in day-to-day FP&A

Variance analysis explained in plain language

Comparing actuals with budget is easy; explaining the gaps takes time. An AI assistant can walk through the accounts with the largest variances, cross them with the model’s drivers (volume, price, headcount, exchange rate) and suggest a first explanation. For example, if payroll came in €20,000 over plan in a quarter (an illustrative figure), it might point out that two hires started early and operations logged overtime. The analyst validates the hypothesis and adds the context only they know.

Anomaly detection

Models can flag duplicate entries, amounts an order of magnitude off, accounts that flip sign or suppliers that suddenly appear. They do not replace reconciliation, but they help prioritise where to look before signing off the close.

Draft commentary for the board

Preparing the monthly or quarterly board pack means turning tables into messages. AI can draft a first version of the commentary: what beat or missed plan, which risks are visible in the coming months and which decisions are pending. The draft saves you from the blank page, but the finance lead decides the tone, the nuance and what gets said. To see how this kind of deliverable is structured, take a look at reporting and dashboards.

Questions about the data

Questions such as “which cost centres have grown most since January?” or “how is gross margin trending by product line?” can be answered in seconds if the assistant sits on a well-defined data model. That brings information closer to department heads who are not spreadsheet experts.

Assisted forecasting

AI can propose a baseline projection from history and seasonality, which the team then adjusts with information the data does not contain: a new contract, a price increase or a supplier change. The value is not in being right without human input, but in starting from a sensible baseline and spending the time on the assumptions.

Scenario preparation

Building a cautious, a base and an ambitious scenario means moving several drivers at once. An assistant can suggest which variables matter most, set up the variants and summarise the differences in cash and profit, so the discussion focuses on what to do in each case.

What AI does not replace

• Judgement. Whether a variance is a problem or good news depends on business context, strategy and conversations that live in no system.
• Accountability. The figures that reach the board or the bank are signed off by a person, not a model.
• Assumptions. How fast sales will grow, when to hire or what price to set are management decisions. AI can propose and challenge assumptions, but should not set them alone.

Risks of AI in finance and how to mitigate them

• Hallucinations. Language models can invent figures or causes with complete confidence. Mitigation: numbers always come from calculations on your data, and every statement shows which report or account it comes from.
• Data quality. If the chart of accounts is messy or actuals arrive incomplete, AI will amplify the problem. Mitigation: clean up account mapping and define a single source of data before automating.
• Privacy and governance. Financial and headcount data is sensitive. Mitigation: know where it is processed, what is shared with third parties, and apply the same permissions as everywhere else; someone who cannot see salaries in the system should not see them through the assistant either.
• Traceability. If nobody knows where an explanation came from, it cannot be audited. Mitigation: keep a history of questions, answers and sources, and require human review before anything is published.

The rule of thumb: keep a human in the loop. AI proposes; the team reviews and decides.

Generic chatbot versus specialised AI

A general-purpose chatbot can help you write an email or remember a formula, but it does not know your chart of accounts, your drivers or your close calendar.

Specialised AI works inside the planning platform, with the financial context already structured: the approved budget, the current forecast version and the actuals. That context is what lets it answer about the company's own figures instead of giving generic answers. OnePlan includes Aurora, the AI inside OnePlan, which understands the financial model within each user's permissions: it explains variances, drafts narratives and builds formulas that are applied only with approval.

When assessing any tool, ask:

• Does it work on my data inside the platform, or on copies I have to upload by hand?
• Does it respect each user’s permissions?
• Does it show where every figure comes from?
• Can I review and edit what it proposes before sharing it?

How to get started, step by step

1. Get the data in order. Review account mapping, cost centres and how actuals are loaded.
2. Pick one narrow use case. Monthly variance commentary is a good start: it recurs, it takes time and the output is easy to check.
3. Set review rules. Decide who validates each AI output and make sure nothing is published without that sign-off.
4. Measure simply. Over two or three closes, compare time spent on analysis and the quality of the explanations, before and after.
5. Expand gradually. Once the first case works, add draft commentary, data questions or scenarios.
6. Train the team. Teach people to ask good questions and, above all, to distrust answers that cite no source.

AI in finance cuts mechanical work and shortens the path from data to the question that matters. If you would like to see Aurora, OnePlan's AI, explaining budgets and actuals, you can request a demo and we will walk you through it.