AI in finance:
from variance to insight.

One useful starting point for AI in finance is a familiar task: explaining why actual performance differs from budget. The calculations may already exist in a spreadsheet, but turning them into a clear management narrative takes thought. What changed? What is supported by evidence? What should the business do next?

My perspective comes from audit, taxation and reporting. I would start with a small, reviewable workflow: let AI help organise the explanation, while the finance professional retains responsibility for the inputs, interpretation and final report. The approach below is a proposed workflow, not a claim about a system deployed at my employer.

Start with a question worth answering

“Analyse this spreadsheet” is too broad. A more useful question is: “Explain the revenue variance, distinguish confirmed drivers from possible causes, and identify the evidence needed before we recommend an action.”

Choose a bounded task with a known source and a named reviewer. Good candidates include drafting commentary from a checked variance table, organising management questions, or comparing a draft report with an approved checklist. Keep journal postings, payments and tax submissions outside this initial drafting workflow.

A worked example: more units, less revenue

Revenue is AED 16,000 below budget, a 3.2% shortfall, despite selling 10% more units. Using a bridge that values the volume change at the budget price and the price change at actual volume:

• Volume effect: (11,000 − 10,000) × AED 50 = AED 50,000 favourable.
• Price effect: (AED 44 − AED 50) × 11,000 = AED 66,000 adverse.
• Net effect: AED 50,000 − AED 66,000 = AED 16,000 adverse.

The bridge reconciles: AED 500,000 + AED 50,000 − AED 66,000 = AED 484,000. It tells us that the lower average price more than offset higher volume. It does not establish why the price fell. Discounts, contract changes or adjustments are questions to investigate, not facts to invent.

Illustrative figures only; no employer or client data. This example assumes one product, consistent units and comparable revenue definitions. A real multi-product business may also need mix, currency, returns and timing analysis.

Give AI a narrow brief and checked inputs

Calculate and reconcile the bridge using a spreadsheet, reporting system or other controlled calculation process before asking a language model to explain it. Give the model the approved table, definitions and relevant verified notes. The following prompt can be tested using the fictional figures above:

A suitable reviewed commentary would say: “Revenue was AED 16,000 (3.2%) below budget. Higher volume contributed AED 50,000, but a lower average selling price reduced revenue by AED 66,000. The reason for the price reduction is not yet established. Review invoice-level pricing, discounts and contract terms, then assess the effect on gross margin before recommending a pricing action.” This is an illustrative editorial example, not the result of a measured AI trial.

Review the explanation as carefully as the calculation

NIST’s Generative AI Profile identifies the risk that AI can confidently produce incorrect content, including unsupported reasoning or citations. Fluent wording is therefore insufficient evidence of accuracy. Source: NIST AI 600-1, section 2.2.

• Numbers: Recheck totals, signs, denominators and units against the approved workbook.
• Causes: Trace each explanation to evidence. Label remaining possibilities as hypotheses.
• Context: Confirm the period, scope and accounting definitions; review one-off items.
• Decisions: Ask whether the proposed action follows from the evidence. Assign an owner and review date.
• Record: Keep the source version, prompt, draft, reviewer changes and approved commentary under the organisation’s retention policy.

Protect the information before uploading it

Use only tools and accounts approved by your organisation for the data involved. Check access, retention and permitted uses before submitting a file. Removing names alone may not make a dataset safe to share: transaction details can still reveal a customer or commercial arrangement. For a first experiment, synthetic data such as the example above avoids using confidential records.

My taxation perspective adds another review point: an AI explanation is not authority for a tax position. Verify any tax or accounting conclusion against the applicable current source and have it reviewed by the responsible professional.

Measure whether the workflow actually helps

Compare several similar reporting tasks with and without AI. Track total time through final approval, factual corrections, unsupported claims and whether every final statement is traceable to evidence. A faster first draft is only useful if the review burden and quality of the final output improve too.

Start with one variance commentary and one reviewer. Expand only when the results justify it. The practical opportunity is to spend less time arranging words and more time investigating what the numbers mean for the next business decision.

Personal educational perspective, independent of my employer. This article describes a proposed approach; it does not announce a completed AI Finance Lab tool or claim measured productivity gains.