Finance teams already work with structured data, recurring deadlines and clear standards for accuracy. Those strengths make finance a practical place to explore AI, provided every use case keeps its source material, review steps and decision owner visible.

Start with a task that produces a draft or an explanation for an expert to assess. A monthly variance summary, a first pass at categorizing questions or a formula explanation can be useful candidates. The accountant supplies the context, evaluates the output and confirms any statement that enters a report or affects a financial decision.

Begin with a small, repeatable finance task

Choose one workflow that takes time and follows a consistent pattern. Examples include preparing commentary on budget variances, organizing questions about invoices or summarizing an approved spreadsheet for a management review.

An illustrative variance workflow could use a training workbook with fictional figures. The accountant identifies the relevant period and comparison, then asks AI to group movements by category and draft explanations for review. The accountant checks the totals against the workbook, confirms the accounting context and writes the approved commentary in the reporting system.

Approved example workbook
    ↓
AI organizes figures and drafts explanations
    ↓
Accountant checks formulas, totals and context
    ↓
Reviewer confirms the financial interpretation
    ↓
Approved report commentary

This pattern separates preparation from professional approval. AI helps arrange information; the finance professional determines whether the interpretation matches the records, policy and business context.

ACCA’s 2025 Digital Pathways Playbook reports insights from 1,026 finance professionals across 100 countries. The report says 74% of SMEs in its survey already experiment with AI for day-to-day tasks such as data analysis and invoicing. That finding points to a practical opportunity for finance teams: shape everyday experimentation into a clear process with ownership, training and review standards. (ACCA, Digital Pathways Playbook)

Use AI for preparation and explanation

Spreadsheet analysis can help a finance team form a first view of a dataset. For example, AI can help create a summary of trends, suggest a chart or explain how a formula works. Microsoft’s official learning material includes a module on using Copilot to analyze and visualize data in Excel, showing one product-specific example of this workflow. Tool features and licensing depend on the company’s Microsoft 365 environment. (Microsoft Learn, Analyze and visualize data using Microsoft Copilot)

Other finance tasks can also begin with a well-bounded draft:

  • Draft an explanation of a variance from an approved sample workbook.
  • Group invoice questions by supplier, type or next owner.
  • Turn finance policy excerpts into a structured internal FAQ for review.
  • Summarize a set of confirmed management comments for a monthly report.

These examples work best when the prompt names the source, the required format and the intended reviewer. A strong request also asks the AI to separate confirmed information from points that need finance review. The accountant then checks the source rows and supporting documents before using the draft.

Keep the control points visible

Every finance workflow should make it easy to see which source supports a statement, who reviewed it and which system contains the final approved version. The control points vary by task: an analyst may check formulas and totals; a controller may check accounting policy; a budget owner may confirm operational context.

ACCA’s research on AI in accounting describes growing adoption alongside a need for strategic practices, employee training, data governance and human oversight. It highlights the role finance professionals can play in aligning AI initiatives with business goals and managing data quality. (ACCA, The Smart Alliance: Accounting Expertise Meets Machine Intelligence)

NIST’s Generative AI Profile provides a cross-sector risk-management lens for organizations. Its Govern, Map, Measure and Manage functions can help a finance team describe responsibilities, map a use case, set output checks and decide how the workflow will be monitored. (NIST, Generative AI Profile)

In daily practice, the rules can fit on a short checklist: use company-approved tools and materials, preserve source references, assign a qualified reviewer, confirm calculations and keep a traceable record of approved changes. The finance team can tailor each point to its own close, reporting and approval routines.

Measure usefulness against the current process

Before a pilot, record how the task works today: the inputs, review stages, time-consuming steps and quality expectations. After an AI-supported test, compare the new draft with the same standard. The team can assess completeness, correction effort, traceability and clarity for the next reader.

That assessment gives leaders a grounded basis for the next decision. A one-day lab maps one finance task, tests an AI step with approved example data and produces a review checklist. A one-week sprint documents a recurring process such as management commentary, including a named owner and a next-step decision. A four-week program gathers workflow cards from finance and adjacent functions, a shared review standard and a priority map for the next pilot.

To make the test useful for a finance leader, compare preparation time and correction effort across the same reporting task while keeping the source trail and approval checks visible. The result shows what changed in the workflow and gives the team evidence for its next decision.

Make finance expertise more visible

AI can help prepare summaries, organize questions and explore spreadsheet patterns. Finance professionals bring the knowledge that makes those materials meaningful: accounting policy, source quality, materiality, business context and responsibility for the final decision.