Use AI to Test Your Business Plan—Not Just Write It

A business plan can look remarkably convincing before anyone checks whether the business can afford it. AI makes that part easier. The headings are tidy, the growth story hangs together, and the cash shortage has apparently decided not to attend.

We should use AI in planning. But not just to turn last year’s budget into this year’s confident prose. Its better use is to connect accurate financial records, meaningful operating measures and explicit assumptions, then help us challenge the plan before committing.

The tools have moved. The responsibility hasn’t.

On 29 October, Microsoft announced expanded finance capabilities across Copilot, including support for reconciliation, performance analysis and forecast-review preparation. There is an important availability distinction: its accounting capabilities are in Frontier, while finance skills in Excel and the Finance plugin in Cowork are generally available. Some integrations are still rolling out. [1]

Anthropic’s May announcement describes agents for financial modelling, ledger reconciliation and month-end close, alongside Excel sensitivity analysis. These are vendor descriptions of capabilities, not independent evidence that a particular SME will get a reliable plan or a return on its subscription. [2]

The more useful counterweight comes from the UK Financial Reporting Council’s July research. It found adoption concentrated in lower-risk, process-heavy activities, with judgement remaining human-led. Interviewees said efficiency gains were often being reinvested in reporting rather than simply becoming cost savings. That is corporate-reporting evidence, not an Australian SME planning survey, but it challenges the idea that faster automatically means cheaper—or better. [3]

Our opportunity is to spend less time assembling the plan and more time questioning it. We still have to do the questioning.

Give the plan a starting point we can defend

Before asking AI what next year could look like, establish what happened this year.

Use a reconciled profit and loss, balance sheet and bank position, supported by receivables, payables and relevant operating records. Mark the reporting period, entity, currency and GST treatment. Separate actuals from forecasts and identify unusual items. A missing supplier invoice is not a productivity improvement.

Definitions matter just as much. Does gross margin include subcontract labour? Are sales recorded when invoiced or when earned? Does “active customer” mean someone who bought this month or anyone still in the database? Don’t let AI blend unlike periods or silently choose a convenient definition.

This isn’t merely small-business housekeeping. In a September account of its own finance transformation, Microsoft describes consolidating fragmented workflows and creating one source of truth before layering intelligence into the process. The unglamorous preparation was part of the work, not something AI made unnecessary. [4]

We don’t need perfect data everywhere before starting. We need data good enough for the decision, with known gaps visible. Ask the tool to flag missing inputs and unresolved differences, not invent a number to keep the exercise moving.

Choose KPIs that can change the decision

Collecting more data isn’t always the answer. Sometimes it just gives us a more impressive report to ignore.

Start with the choice: hire, expand, reprice, buy stock or hold back. Then select measures that tell us whether that choice is working. Business Victoria’s guidance makes the basic point: KPIs should align with business goals and focus attention on what matters. It is enduring guidance, updated in 2022, not a new AI finding. [5]

Pair outcomes with the things that drive them. Revenue and margin tell us what happened. Qualified enquiries, quote conversion, delivery capacity or rework may help explain what happens next. A leading indicator is a hypothesis about future performance, not a promise.

For each KPI, agree its calculation, source, owner and review frequency. Name the threshold that would trigger a conversation or action. If no one can explain what they would do differently when it moves, ask why it belongs in the plan.

And keep cash beside profit. Sales can be growing while customers take longer to pay and stock absorbs the bank balance. ASIC’s guidance for companies in financial difficulty specifically directs attention to bank balances, overdue debts and upcoming obligations—not just the profit line. [6]

Test the plan before funding the story

Consider a hypothetical wholesaler deciding whether to expand its range. This is an illustration, not a customer case.

Its sales forecast looks encouraging. But the new stock must be paid for before customers settle their invoices. Extra revenue could improve reported profit while making the lowest cash point worse.

We could ask approved AI to help structure three cases: expected demand, slower sales with later collections, and stronger sales requiring more stock. Keep the assumptions visible. Where did the sales expectation come from? Which supplier terms are confirmed? What evidence supports the collection timing?

Check seasonality too: a busy quarter isn’t automatically the new normal. Ask the owner or adviser to challenge the commercial assumptions, and the accountant to validate accounting and tax inputs.

Use a spreadsheet or other controlled calculation model for the numbers. Have AI help organise inputs, inspect formulas and draft explanations; independently check opening balances, cash movements and closing balances. Change one driver at a time before combining stresses, so we can see what actually causes the problem.

Then ask a less comfortable question: what would have to be true for the expansion to fail? Perhaps the safer first step is a limited product trial, a deposit arrangement or a smaller purchase. AI earns its place if it helps us find a better test—not merely a more persuasive case for the original idea.

Make it a working plan

Start small this week: one decision, one agreed dataset and a short list of drivers. Use an approved tool and the minimum authorised data; check provider retention, training-use settings and access permissions. Test with dummy or de-identified records where necessary. Don’t upload confidential customer or employee information into an unapproved service.

Ask AI for a draft plan that separates evidence, assumptions, unknowns and proposed actions. Give each action an owner and deadline—for example, the purchasing manager checks supplier terms before next Friday’s decision. Agree when to review actual results, and what would make us stop, adjust or invest more. Use weekly checks where cash or demand moves quickly, with a fuller monthly review where appropriate.

Keep the source files, model version and human sign-off. At the next review, compare the prediction with what happened. Include checking time when judging whether the tool helped.

What is the next decision we could test before committing?

A useful AI-assisted business plan isn’t the one that sounds most certain. It is the one that makes uncertainty visible early enough for us to do something about it.

Sources and reading notes

All sources opened and checked on 8 November 2026 (Brisbane AEST). Product availability and claims should be refreshed before publication.

1. Microsoft: Microsoft Copilot for finance — 29 October 2026. Vendor announcement; availability varies by capability.
2. Anthropic: Agents for financial services — 5 May 2026. Vendor announcement; not independent effectiveness evidence.
3. FRC: The use of Artificial Intelligence Technologies in Corporate Reporting — released 8 July 2026, confirmed by the FRC release notice. UK corporate-reporting context; interviews and survey, not an SME planning trial.
4. Microsoft Inside Track: Prioritizing AI transformation opportunities in Microsoft Finance — 24 September 2026. First-party account of its own operations, not a controlled comparison.
5. Business Victoria: Measure for success — last updated 28 December 2022. Foundational KPI guidance, not current AI research.
6. ASIC: Experiencing financial difficulty — publication/update date not displayed in retrieved page. Current regulator guidance checked on the retrieval date; not personalised financial or legal advice.