Hand-check ten spreadsheet answers before the copilot sees a number you cannot verify

The eight named question types break in different places. The checklist supplies the list of places to look: the selected range, filters, hidden rows, date boundaries, and whether the formula copied a text value instead of calculating it ^[6]. Those five checks are the answer key for the date-range sum. The duplicate-count question specifies a stable ID because pointing the same count at a name column returns a different number ^[3].

The checklist leaves two of the ten slots open. The list names eight types ^[3] against an instruction to prepare ten questions ^[2], so those two are yours to write, for whatever is specific to your workbook ^[18].

Saving the formula, the cell range and the explanation is the step people skip. A demo opens on a clean table and a friendly request ^[22]. The rule dev.to states for the workbook layer is that the assistant writes a candidate formula and the user owns the number ^[6]. The piece says most spreadsheet AI failures are diagnosable if you preserve the intermediate artifact ^[17].

Two of the ten questions are definition tests. An ambiguous term such as "active customer" and an answer that lives in a footnote both test whether the assistant can tell a definition from a calculation ^[3][1]. The gap survives a better model. "Revenue" may mean invoiced revenue, paid revenue, or recognized revenue, and according to dev.to no model can infer a business definition reliably from a column named amount ^[13]. Power BI AI and Qlik Sense AI put natural-language questions on top of a semantic model. The piece calls that the right layer when many business users need consistent answers from governed data, and also the layer where missing governance becomes impossible to hide ^[14].

For the workbook products, the transfer condition is stated plainly: test the exact workbook, locale, formulas and sharing model the team uses ^[9]. Excel AI is not automatically better because it can call Python, and Sheets AI does not become a warehouse because it can summarize a range ^[9].

Below the spreadsheet, the products change shape. A shared customer list, inventory table or project ledger that has outgrown a sheet is often a data-shape problem ^[21]. Teable answers that with typed fields, views, permissions, APIs and AI-assisted table operations, self-hostable when records have to stay inside a controlled environment; it is not a full BI platform ^[10]. AI2SQL and SQLAI translate questions into SQL and can explain, optimize or repair the query, and their value rests on one prerequisite: the person running the query can read the SQL ^[11]. The advice at that layer is a complete schema with field comments, read-only credentials, and known-answer queries before anything else ^[12].