What you can explore
1. What problem would this solve?
AI can describe a spreadsheet fluently while the underlying data or total is wrong. A neat management summary can hide a missing row, an old formula or a misunderstood reporting period. Check the source before drafting commentary.
2. What could become easier?
- Use AI to explain—not invent—numbers.
- Trace every important calculation.
- Keep a named reviewer in charge.
The aim is to make a clearer decision, with practical checks before relying on AI output.
3. What practical support could include
A possible starting point: An AI-assisted reporting process and reconciliation review. A scoped engagement might produce:
- Input-data and column-definition sheet
- Report draft versus verified calculation checklist
- Sample exception log
- Human sign-off and change record
- Workflow test on non-sensitive example data
Any practical support would be scoped to the task, available information and agreed responsibilities.
4. Why this approach?
This review separates field definitions, spreadsheet calculations, AI-written explanation and human sign-off into distinct checkpoints.
5. What evidence supports these recommendations?
A synthetic case-reconciliation example shows the difference between a valid arithmetic identity and a claim requiring more business context. Microsoft documents Excel error checks.
6. How might we work together?
Define fields; reconcile totals; draft explanatory text; trace claims back to cells; approve the report.
Practical guide: how to make this decision
Worked spreadsheet reconciliation — fictional example
A small sales report lists three lines of fictional units: 12, 8 and 5. The source total must therefore equal 25 units. If a generated summary reports 24 units, that is a calculation mismatch; if the underlying spreadsheet is missing a row, the summary must say the total is incomplete rather than invent a replacement.
| Input row |
Recorded units |
Check |
| Alpha |
12 |
Source row present |
| Beta |
8 |
Source row present |
| Gamma |
5 |
Source row present |
| Total |
25 |
12 + 8 + 5 = 25 |
A draft narrative may say, “The three recorded lines sum to 25 units.” It may not assert a monthly growth rate without comparison-period data. An optional workbook example should include one deliberately blank input, a formula check, a mismatch flag, and a field for the responsible reviewer. The engagement remains a proposed reporting review, not a claim that a business spreadsheet has been integrated.
Evidence and important limits
- NIST describes voluntary cross-sector risk-management guidance, not certification of any particular product. [S01]
Evidence references
Decide which parts of a report AI may assist
A reporting team may want help explaining a spreadsheet, drafting a summary or turning trends into questions. Those tasks differ from creating the underlying figures. Consider a weekly service report with counts of requests received, resolved and awaiting action. A system can propose a written explanation, but the source data, definitions and calculation method must remain visible. An attractive narrative is not evidence that the totals are correct. For this page, the proposed offer is a reporting workflow review, not a general spreadsheet course. A useful result is an agreed path from source file to verified calculation to reviewed explanation, with named ownership at each step. NIST's generative AI guidance supports context-specific evaluation and documentation when introducing AI into established work [NIST].
Record data definitions before writing prompts
Every report should have a data dictionary: what each field means, its permitted values, when records are included, how duplicates are treated and which source is authoritative. “Resolved this week” could mean cases closed during the week, cases opened and closed during the week, or cases closed within a target interval. A chatbot cannot infer the organization's intended definition from a column name alone. Before offering analysis, note the reporting period, timezone, record status rules, source owner, version and date of extraction. If definitions are inconsistent across departments, record the disagreement as a business decision requiring resolution. An AI summary must not silently choose whichever interpretation gives the most striking trend.
Worked example: check the numbers before the narrative
A hypothetical weekly dataset records 120 received cases, 75 closed cases and 45 cases still open at period end, assuming no opening backlog and consistent cut-off dates. The identity received = closed + still open holds for this simplified teaching example. If the report instead shows 75 closed and 60 open, it should not automatically claim 15 missing cases; a pre-existing backlog or different definitions could explain the difference. Record the reconciliation logic before describing results. A draft sentence might say “Most cases were closed.” A reviewer must first verify what “most” means and which denominator was used, then decide whether the statement is useful. No such figures should be presented as actual AICoach client performance.
| Check |
Example review question |
Responsible person |
| Source completeness |
Does the file cover the intended period? |
Data owner |
| Duplicate handling |
Could the same request appear twice? |
Process owner |
| Calculation |
Do totals follow the approved formula? |
Spreadsheet reviewer |
| Explanation |
Does each statement trace to a displayed metric? |
Report author |
| Exceptions |
Are backlogs or exclusions described? |
Business reviewer |
Use spreadsheet tools to confirm calculations
Excel supports formula inspection and error-checking features that can help identify formula mistakes. Microsoft explains that these tools do not guarantee a worksheet is free from errors [EXCEL]. A reporting review should compare displayed formulas and ranges against the intended calculation, inspect hidden rows or filters where relevant, and confirm that the workbook has recalculated in the expected mode. A text-generation model should not be the sole source of arithmetic truth. Where appropriate, have the spreadsheet itself calculate totals and let AI assist with explanation of approved results. Also watch for dates stored as text, inconsistent units and separate workbooks containing different versions of a source sheet.
Separate calculation review from AI interpretation
First verify the numeric outputs independently. Then provide the approved figures and definitions to an assistant, asking it to draft a neutral explanation and note uncertainties. Finally, trace each statement back to the verified table. For example, “Closed requests decreased compared with last week” must be supported by figures from both periods with the same definition. A sentence like “the team became less productive” requires additional evidence and may be unjustified even if closures fell. The review sheet should mark a statement as verified, needs qualification, unsupported or not appropriate for publication. This prevents a well-worded interpretation from becoming a decision-making fact without sufficient basis.
Build a report-claim trace table
The trace table identifies each material sentence, the relevant calculation or source, the reviewer and the conclusion. For a weekly service report, one row might connect “45 cases remained open” to a named summary cell and its underlying records. Another might connect “There was a significant increase” to a comparison with a prior period, a definition of significance and appropriate context; if no such definition exists, replace it with a precise description or remove the claim. An AI tool can propose the mapping, but the reviewer must validate it. A trace table is useful even without an AI system because it clarifies how important claims in management reports should be supported.
Test the workflow with awkward data, not only clean tables
Choose examples with blank cells, negative values where not expected, duplicate IDs, mixed currencies or units, inconsistent date formats, filters that hide records and formula references that changed after a column insert. Ensure any such sample is synthetic or specifically authorized. Ask the system to describe what it knows and to flag what it cannot establish, rather than forcing it to produce a confident management narrative. Evaluate whether the draft invents a comparison period, chooses the wrong denominator or treats a blank as zero without an approved rule. OpenAI's evaluation guidance favors task-specific examples and review against explicit criteria rather than a “looks reasonable” assessment [EVAL].
Preserve authorship, permissions and review authority
The original data owner remains responsible for source correctness; the business reviewer decides whether a narrative is appropriate for its audience. If reports include workforce, client or regulated data, check tool authorization before use. A report that will inform a financial, employment or other consequential decision needs qualified domain review. Do not let an assistant silently send or overwrite an approved report, and maintain version control between the verified workbook and generated prose. When a discrepancy occurs, correct the source or definition at the appropriate point rather than editing the summary to conceal the issue. The written handoff should identify who can amend figures and who approves the final message.
What the resulting workflow review should deliver
A useful pack contains the current reporting journey, a field-definition sheet, calculation reconciliation checklist, sample AI-writing instructions, claim-to-cell trace table, exception register and human sign-off step. It should state whether the objective is to assist drafting, help users ask questions about authorized data or redesign an end-to-end process. Those are different scopes. For general learning, see the related guide on AI spreadsheets. Buyers who need a controlled reporting workflow should leave with a concrete way to test the process, including the option to keep the current method when it proves more reliable. The example illustrates a review method and does not guarantee an outcome.
