AI for Formula Writing: How to Safely Use Generative Tools to Build Complex Excel Formulas

Stop wasting time fixing AI-made formulas: build them with generative tools — then prove they work

If you use Generative AI to draft Excel formulas, you already know the upside: lightning-fast drafts of complex logic and multi-step calculations. But you also know the downside — seemingly correct formulas that fail when confronted with real data or edge cases. In 2026, with Copilot-style assistants built into Excel and more teams relying on generative models for execution, the new skill is not how to ask AI for a formula — it’s how to verify and govern the results. This guide shows a practical, step-by-step way to use AI as a formula builder and then validate every outcome using a test harness and unit-test sheets inside Excel.

Why unit testing Excel formulas matters in 2026

Generative AI is now mainstream for productivity tasks across B2B businesses, and adoption accelerated in late 2025 and early 2026. Many teams treat AI as a reliable execution engine — and that’s useful — but major reports this year show that professionals still hesitate to trust AI with strategy or unsupervised decisions. When it comes to spreadsheets, that caution is justified: generated formulas can:

• Assume data shapes that don't exist in your files (e.g., contiguous ranges).
• Break on blank cells, error values, or text-instead-of-number inputs.
• Use deprecated or inefficient constructs that slow large workbooks.
• Leak sensitive data if prompts include real examples (privacy risk).

That’s why the practical pattern is: AI drafts → you verify → you automate and govern. Unit testing is the verification layer that buys you the productivity benefit of AI without the cleanup overhead.

What you’ll learn in this article

• How to ask AI to draft accurate formulas and capture provenance.
• How to build an Excel test harness (unit-test sheet) that validates outputs and edge cases.
• How to run automated or manual test runs and interpret failures.
• Best practices for governance, privacy and production deployment in 2026.

Step 1 — Use AI as a formula drafting assistant: prompts and provenance

Start by treating the AI like a junior developer who needs a precise specification. In 2026, Excel's built-in assistants (Copilot) and large-language-model tools are fast, but they need the right prompt. Below is a repeatable prompt pattern that gets consistent, testable formulas:

1. State the sheet layout: column names, sample rows, datatypes.
2. Describe the business rule in one sentence (expected result for a sample row).
3. Ask for a single-cell formula using Excel 365 functions (LET, LAMBDA, XLOOKUP, MAP where appropriate).
4. Ask the assistant to explain why the formula is robust and list expected failure modes.

Example prompt (use as a template):

"Sheet 'Sales' has columns: Date (dates), ProductID (text), Quantity (integer), Price (number), DiscountPct (number, may be blank). Calculate 'NetValue' as Quantity*Price*(1-DiscountPct) where blank DiscountPct defaults to 0. Return a single-cell formula for row 2 (assume headers in row 1), using Excel 365 functions. Include error handling for missing or non-numeric values and describe three edge cases that could break it. Provide a short explanation and the formula only in a single line."

Why capture this? Save the prompt and the AI's response in the workbook (see governance below). That provenance helps audits and troubleshooting when formulas change months later.

Sample AI-generated formula (expected)

From a good prompt you'll get something like:

=LET(q,VALUE(IFERROR(B2,0)), p, VALUE(IFERROR(C2,0)), d, IFERROR(VALUE(D2),0), IF(OR(q="",p=""),"#NODATA", ROUND(q*p*(1-d),2)))

This uses LET to name intermediate values and IFERROR/ VALUE to coerce numbers. It’s not perfect — that's why we test.

Step 2 — Build a test harness: the unit-test sheet

The test harness is a dedicated worksheet (call it _tests) where you define test cases, expected results and actual results that reference the formula under test. Keep tests human-readable and machine-evaluable so they can be automated.

Structure of the test sheet

• TestID — short unique ID (e.g., T001).
• Description — one-line summary of the case (e.g., "standard row").
• Inputs — columns matching the formula inputs (Date, ProductID, Quantity, Price, DiscountPct).
• Expected — the expected NetValue (calculated manually or from a canonical rule).
• Actual — a cell that computes the formula under test by referencing inputs.
• Pass/Fail — formula to compare Expected vs Actual and return PASS/FAIL.
• Notes — for commentary on failures.

Example columns (A:H): TestID, Description, Date, ProductID, Quantity, Price, DiscountPct, Expected, Actual, Result, Notes.

Make tests deterministic

• Avoid RAND() or volatile functions in expected values — they make comparisons flaky.
• Use explicit numbers for expected outputs or non-volatile formulas (e.g., ROUND with fixed numbers).
• If you must test variable scenarios, store the random seed or snapshot the expected results.

Step 3 — Write a PASS/FAIL formula

The simplest approach is an exact match, but you often need tolerance for floating-point rounding. Use this pattern in the Result column:

=IF(ABS(Actual - Expected) < 0.01, "PASS", "FAIL")

Or stricter if values must match exactly:

=IF(EXACT(TEXT(Actual,"0.00"),TEXT(Expected,"0.00")) ,"PASS","FAIL")

When results are text or error codes, compare strings or error types explicitly:

=IF(Actual=Expected, "PASS", "FAIL")

Step 4 — Define categories of tests (edge cases to include)

A well-rounded suite includes positive, negative and stress tests. Here are categories and example cases for the sample NetValue formula:

• Standard cases: Typical numeric rows — Quantity=5, Price=12.50, DiscountPct=0.1.
• Missing discounts: DiscountPct blank — expect discount=0.
• Zero and negative values: Quantity = 0, Price = 0, negative discounts (should clamp?), negative quantities (returns negative value).
• Non-numeric inputs: Text in Quantity or Price — expect #NODATA or error-captured output.
• Large numbers: Quantity and Price very large — test for overflow/performance and consider storage and calculation limits.
• Dates and boundaries: Date outside expected range if business logic depends on it (e.g., promo windows).
• Error propagation: Downstream formulas referencing this formula should receive consistent error codes.
• Array and spill behavior: If the formula uses MAP, BYROW or array logic, verify output shape matches expected rows/columns.

Step 5 — Automate test runs: lightweight scripts & macros

Manual review is fine for a few cases. For larger suites, automate test runs to quickly detect regressions after updates. Two options work well in modern Excel:

Office Scripts (Excel on web)

Office Scripts are TypeScript-based scripts that can open the workbook, run calculations and summarise test results. They run in the cloud and integrate nicely with Power Automate.

// Pseudocode Office Script to run tests
async function main(context) {
  const sheet = context.workbook.worksheets.getItem("_tests");
  // assume Result column is J, calculate used range
  const range = sheet.getUsedRange();
  range.calculate();
  // read pass/fail counts and write summary
}

Use Power Automate to schedule a nightly test run and send a failure summary to Slack or Teams.

VBA macro for desktop Excel

Sub RunTests()
  Dim ws As Worksheet
  Set ws = ThisWorkbook.Worksheets("_tests")
  Dim lastRow As Long
  lastRow = ws.Cells(ws.Rows.Count, "A").End(xlUp).Row
  ws.Calculate
  Dim passCount As Long
  Dim i As Long
  passCount = 0
  For i = 2 To lastRow
    If ws.Cells(i, "J").Value = "PASS" Then passCount = passCount + 1
  Next i
  MsgBox passCount & " / " & (lastRow - 1) & " tests passed"
End Sub

Tip: keep macros small and use workbook-level logging — write a timestamped test summary to a sheet called _test_log. Consider the long-term cost of storing logs and test artifacts when you design retention — see storage cost optimization.

Step 6 — Interpret failures and iterate

When a test fails, follow this workflow:

1. Read the test description and input row to reproduce the case.
2. Check the formula’s intermediate values — use TRACE or temporary columns, or wrap parts of the formula in named LET variables that you expose for debugging.
3. Decide whether the formula should change to handle the case or the case is invalid input that needs upstream validation.
4. Update the test (add expected behavior) and re-run.

Example debugging trick: if the AI-generated formula uses nested IFERROR which hides root problems, temporarily remove the outer IFERROR or replace it with a value that helps trace the error (e.g., return "ERR_Q" if Quantity is invalid). When deeper investigation is needed, consider running fuzz testing or bug bounty-style reviews of your calculation logic.

Best practices for formula robustness

• Use LET to name and re-use sub-expressions — improves readability and testability.
• Coerce types consciously (VALUE, DATEVALUE) and validate with ISNUMBER/ISDATE.
• Avoid volatile functions (NOW, RAND, INDIRECT) in core formulas — they make tests non-deterministic.
• Prefer explicit error codes ("#NODATA", "#INVALID") so tests can assert on string values rather than Excel error objects.
• Use LAMBDA for reusable logic and unit testing of subroutines — you can call a LAMBDA directly in the test sheet.
• Document assumptions inside the workbook: expected ranges, currency units, rounding rules.

Governance, privacy and provenance

Generative AI introduces new governance steps you must follow to keep teams safe and compliant.

• Provenance: Store the original AI prompt and the AI's response in a hidden sheet named _ai_provenance with a timestamp, user, and model version if available — this captures provenance for audits (and mirrors ideas from historical provenance discussions like art provenance).
• Privacy: Never paste real customer data into a public AI prompt. Use synthetic or redacted samples to avoid data leakage and GDPR risk. See the latest notes on URL privacy and API hygiene.
• Approval: Critical formulas should pass a peer review and test run before being marked production-ready.
• Version control: Use a change log (sheet or external) that records formula changes, reasons, and test results — and automate safe backups and versioning as part of deployment (see automating safe backups and versioning).

Advanced strategies: fuzz testing, property-based tests and CI

For mission-critical spreadsheets, level up your testing:

• Fuzz testing: Generate thousands of random inputs (within valid ranges) with Power Query or Office Scripts and compare outputs to a reference implementation or simple arithmetic oracle.
• Property-based testing: Verify invariant properties, e.g., "net value never exceeds quantity*price" or "discount never increases net value". These are expressed as logical checks rather than fixed expected values — tie these into your verification pipeline.
• Continuous Integration: Use Power Automate + Office Scripts to run test suites on checkpoint events (file save, scheduled run) and log results to a central dashboard.

Real-world example: commission calc with tiers (walkthrough)

Scenario: sales team commissions depend on month-to-date sales per rep. Tiers: up to 5k = 3%, 5k-20k = 5%, above 20k = 7%. Commission = tiered rate on incremental buckets. AI drafts a formula using LET and XMATCH. We then create tests to validate edge thresholds and negative sales adjustments.

Key tests to include:

• Sales exactly 5000 → commission = 150.
• Sales 5000.01 → commission uses higher tier on marginal amount.
• Sales 0 → commission 0.
• Large sales (100,000) → performance check and correct aggregated commission.

Automation: create an Office Script that loads 10,000 synthetic reps and runs the formula across them, comparing results to a reference Python calculation run in Power BI or a secure server. Failures log into _test_log with the failing inputs so you can reproduce locally. If you need to reproduce outside Excel, consider lightweight compute like deploying generative models on small hardware for local verification (see edge deployments).

2026 trends and future-proofing your approach

As of early 2026, two trends matter for spreadsheet teams:

• Embedded generative assistants (e.g., Excel Copilot) will be the default way teams draft formulas — so workflows that assume AI will produce correct output are risky. Unit test harnesses are now a core skill.
• Automation and governance tooling (Office Scripts, Power Automate, centralised test dashboards) are maturing — incorporate them to run scheduled validation and alert stakeholders automatically. If you’re designing a roadmap for organisational trust and scaling verification, see the interoperable verification layer work.

Recent analyses in late 2025 and January 2026 emphasise the same point: organisations gain productivity when AI handles execution, but they keep the gains only if humans enforce checks and governance. Use unit tests to make AI outputs trustworthy.

Checklist: ship formulas safely

1. Save the prompt and the AI response in _ai_provenance.
2. Build a _tests sheet with deterministic test cases covering edge cases.
3. Write PASS/FAIL logic with tolerances where needed.
4. Automate runs with Office Scripts/VBA and schedule via Power Automate.
5. Log results to _test_log and require peer sign-off before production use.
6. Redact sensitive examples from prompts; use synthetic data and keep safe backups and versioning (automate backups).

Closing: practical next steps (download and adopt)

AI can drastically speed up formula writing — but only when combined with disciplined testing. Start by building a minimal test harness for your next AI-generated formula: capture the prompt, add 10 targeted tests (including edge cases), and automate a nightly run. Within a week you’ll stop firefighting broken formulas and start trusting AI as a reliable formula builder.

Want a head start? We’ve prepared a ready-to-use Excel template for AI formula drafting and unit testing, plus a short video walkthrough that shows the exact VBA and Office Script examples in this article. Download it, paste your AI prompts into _ai_provenance, and run the tests. If you need a tailored workshop for your team (covering LAMBDA testing, Power Query validation and CI pipelines), our experts at excels.uk run hands-on sessions for UK businesses.

Call to action: Download the free AI Formula Unit-Test template from excels.uk, try it with one formula this week, and let us know how many hours you saved. Book a 30-minute audit and we’ll review your top 3 AI-generated formulas and provide a test-suite roadmap.

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