Excel: audit AI-generated reports before sharing

Key insights

• Rebuild key report figures with PivotTables and compare totals against the AI-generated report.
• A matching grand total does not prove every category, region or month is represented correctly.
• COUNT-based checks can flag source rows where expected numeric fields or dates are stored incorrectly.
• Power Query's column quality, distribution and profile views can help identify unusual values.
• Excel catches recognized formula errors, but it cannot determine whether a successful formula uses the right logic.

Excel users can audit an AI-generated report by reconciling its figures against the source data and checking the data, formulas and assumptions behind them. This matters to analysts and report owners preparing spreadsheet results for managers or clients. Mynda Treacy (MyOnlineTrainingHub) [MVP] demonstrates an audit workflow using an AI-created sales report and Excel’s built-in analysis tools.

What it does

The workflow treats an AI-produced workbook as a draft to verify, not as a finished analysis. It tests whether headline metrics and breakdowns match the underlying records, then looks for source-data problems that might not be obvious from the dashboard. It also checks for formula errors and asks whether the business definitions used in the report are appropriate.

In the demonstration, Copilot creates a sales report with summary figures, chart data and a dashboard from orders and product data. Several headline values agree with an independent PivotTable, but comparisons by region and month reveal issues in the data. A trailing space makes one region value inconsistent, and an order date stored as text leads to an invalid month. Those examples show why a clean-looking dashboard and matching overall totals are not enough.

Who it is for and what you need

This process is for people who create, review or distribute Excel reports, including anyone responsible for checking AI-generated calculations before relying on them. The demonstrated workbook contains an Orders table and a product list, and the checks use PivotTables, worksheet formulas, filters, Power Query and Excel’s formula error-checking tools.

The material does not state a required Excel license, plan, tenant setting, region or rollout status. It does not establish that every step depends on Copilot: the audit methods shown are Excel analysis techniques applied to a workbook Copilot generated. The example uses 300 records, and Power Query’s profiling view is initially set to profile the top 1,000 rows by default; the transcript notes that profiling the entire dataset can take time with larger data.

If you work with AI-generated spreadsheets, the Excel topic hub may help you find related coverage. The core requirement is not a particular AI feature, but enough understanding of the report’s metrics to assess whether the calculations make sense.

How it works

1. Build an independent baseline from the source records. Insert a PivotTable and add the key measures, such as net sales, gross profit, order count and quantity. For average order value, summarize net sales by Average; calculate gross margin as total gross profit divided by total net sales.

2. Compare the report’s headline figures with the PivotTable. Then check individual breakdowns, such as salesperson, category, region and month, rather than relying only on the grand total. For values that are hard to compare by eye, subtract the PivotTable value from the report value and use ROUND to two decimal places for currency comparisons.

3. Inspect source columns for unexpected values. A temporary COUNT check can test whether fields expected to contain numbers—including dates, which Excel stores as numbers—are populated with numeric values. Filter for FALSE results and investigate the affected rows. For text values, a LEN check can expose extra spaces that ordinary filtering may not make obvious.

4. Profile the dataset in Power Query. Select the table, choose Data > Get Data from Table/Range, and check each column’s data type in Power Query Editor. On the View tab, enable Column quality, Column distribution and Column profile in Data Preview. Review valid, error and empty values, distinct and unique values, and the available column statistics.

5. Check for duplicate records and formula problems. Data > Remove Duplicates can remove rows, so work on a copy or be ready to undo; choose the columns that together define a duplicate record. Use Formulas > Error Checking for recognized errors, then inspect important formulas for correct ranges and business logic. Ctrl + ` displays formulas so you can check consistency down a column.

Before generating a report, the demonstrated prompting approach is to ask AI to flag source-data anomalies, list its assumptions and create a reconciliation sheet. These requests can make checks easier to review, but they do not replace independent verification.

Limitations and open questions

Excel’s error checker can identify formulas returning recognized errors such as #VALUE!, #DIV/0! or #REF!. It cannot tell whether a formula that calculates successfully references the wrong cells, uses the wrong range or applies the wrong calculation. Those cases require review against the intended metric and the structure of the data.

Reconciliation also has limits: matching totals can coexist with missing or misclassified records in a breakdown. And a formula can be technically correct while the business assumption is wrong. For example, counting rows as orders works only if each order appears once; if an order has multiple product lines, counting rows can overstate orders and affect average order value.

Remove Duplicates changes the source data, so use caution and investigate before permanently deleting a row. Power Query profiling can surface unusual values, but an unusual quantity, discount or price is not automatically an error. The transcript does not verify licensing or the availability of particular Copilot features, so those details should not be inferred from this audit example.

Sources

• Mynda Treacy’s Excel AI-report audit demonstration
• MyOnlineTrainingHub

Frequently asked questions

How do I check an AI-generated Excel report?

Create PivotTables from the source data and compare headline figures and each important breakdown with the report. Then inspect the data, formulas and assumptions behind any discrepancies.

Can Excel detect every formula mistake?

No. Error Checking can find recognized errors such as #VALUE!, but it cannot determine whether a formula that calculates successfully uses the right cells, range or business logic.

How can I find data quality issues in Excel?

Use worksheet checks such as COUNT for fields expected to be numeric and LEN to detect unexpected text lengths. Power Query’s Column quality, Column distribution and Column profile views can also help identify errors, blanks and unusual values.

What license do I need for these Excel audit checks?

The source material does not specify a required Excel license or plan. It demonstrates Excel tools including PivotTables, formulas, Power Query and Error Checking.