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Building A Simple Financial Model With AI Without Trusting It Blindly
By Nigel Guy · 3 min read
Asking an AI assistant to "build me a financial model" is an easy trap: it will happily produce something that looks like a proper model — labelled rows, a growth curve, a break-even point marked on a chart — regardless of whether the inputs underneath it are any good. A model that looks finished is not the same thing as a model that's right, and the polish is exactly what makes people stop checking.
The rule: build the model's structure with AI, but supply and verify every number that goes into it yourself — the assistant can build the scaffolding fast; it cannot know your actual costs, your actual conversion rate, or your actual runway.
The mechanism
- Decide what the model needs to answer, in one sentence, before you start — "when do I break even at current growth" or "what happens to margin if I raise prices 10%." A model built without a specific question turns into an exercise in producing numbers, not insight.
- Supply every input from your own records, not from the assistant's general assumptions about a business "like yours" — your actual cost per unit, your actual current customer count, your actual cash on hand. If you don't know a number, that's a genuine unknown to flag, not a gap to let the assistant fill with a plausible-sounding guess.
- Ask it to build the structure and formulas, not the numbers — the scaffolding of rows, the relationships between them, the chart. This is the part it's fast and reliable at, and the part that's tedious to build by hand.
- Check the arithmetic on at least one full path through the model by hand or in a spreadsheet you control. Not because AI is unusually bad at arithmetic, but because a model with one wrong formula early on propagates that error through every downstream number, and it's much easier to catch on one worked example than after the fact.
- Run the model at a pessimistic input, not just your expected one. The version of the model built on your hoped-for growth rate is the one you'll want to believe; the version built on a slower rate is the one that tells you whether the plan survives being wrong.
What to skip
Skip asking for "industry benchmark" numbers to fill gaps in your own data — a general figure for churn or margin in your sector is not a fact about your specific business, and importing it as one is the single most common way these models end up confidently wrong. And skip treating the finished model as the decision itself; it's an input to a decision you still have to make, weighing things the model can't see, like your own appetite for the specific risk involved.
Guardrails
- This produces a working model, not audited financial advice — anything feeding a funding round, a loan application, or a formal filing needs review by a qualified accountant regardless of how the model was built.
- Every number the assistant didn't get from you directly is a guess dressed as a figure — treat any input you can't trace back to your own records as unverified until you've checked it.
- Sensitivity matters more than precision here — a rough model tested against a pessimistic case tells you more than a precise-looking model tested only against the case you're hoping for.
- Revisit the model against real results regularly; a model's value comes from being corrected against reality, not from how convincing it looked the day you built it.
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