How to turn checked data into an AI-assisted report

Turning checked data into an AI-assisted report requires separating evidence, interpretation and proposals. The model can organize the narrative but should not create numbers or decisions to make the text more persuasive.

Our exercise writes a summary about a fictional support operation. Inputs are already reviewed; the focus is communicating what happened, which uncertainty remains and what needs a decision.

Prepare a small evidence package

Gather the final table, metric definitions and analysis limitations. Include the period, filters and origin of every value that may appear in the report.

Do not submit several versions of the same table without identifying the approved one. If material contains a discrepancy, resolve it or explicitly mark it before requesting a draft.

The W3C’s metadata and provenance guidance helps preserve the connection between results and their origin. In a report, that connection explains where a conclusion came from without reconstructing the entire analysis.

Define the reader’s decision

A routine monitoring report may differ from a report seeking approval for change. State who will read it, what they need to understand and which decisions are genuinely open.

Do not ask AI to recommend the best strategy without providing criteria. A proposal is useful only when it identifies its evidence and remaining uncertainty.

Fictional element	Authorized content
Evidence	Total interactions increased from 80 to 100
Context	Hours worked increased from 40 to 50
Interpretation	Volume grew; hourly productivity stayed equal
Next investigation	Understand demand distribution

The numbers are illustrative and can be checked with simple division. This example avoids concluding that efficiency improved merely because total output increased.

Request a structure with clear distinctions

Provide identified evidence and request a short draft. Specify that suggestions must appear as proposals rather than being mixed with observed facts.

Write an executive report from this reviewed evidence package.
Use: context, three findings, limitations and pending decisions.
For every number, preserve its metric, period and table identifier.
Separate evidence, interpretation and proposal.
Do not create statistics, causes or targets that were not supplied.
Flag any conclusion without support.

Three findings is a choice for this exercise, not a rule for every report. It forces prioritization instead of repeating every table row.

Keep enough context for a reader who did not perform the analysis. A compact report can still explain what was counted and why a comparison matters.

Check each sentence against the input

Mark every number and locate it in the source. Check signs, units, period comparisons and the difference between percentages and percentage points where relevant.

Then review nonnumerical claims such as “the main reason” or “the team preferred”. They also need evidence, even when they sound like natural connections between paragraphs.

If AI supplies an uninvestigated cause, label it as a hypothesis or remove it. A chronological sequence alone does not establish that one event caused another.

Make proposals fit the actual decision

A useful next step explains what to investigate, who needs to decide and which evidence would help. Do not add deadlines or owners the organization has not defined.

When alternatives exist, explain how to compare them. This makes the recommendation reviewable rather than dependent on the apparent authority of its writing.

Separate an approved commitment from a suggested experiment. A reader should not have to infer which actions the organization has already agreed to perform.

Define the completion criterion

The report is ready when another reader can distinguish measurement, interpretation and pending decisions. Every important claim needs a verifiable origin or a visible indication of uncertainty.

Common errors include omitting limitations, changing metrics during drafting and converting suggestions into assumed commitments. Store the approved version beside its evidence package.

Use the AI-assisted chart guide for a supporting figure and the calculation checking guide for the final numerical review.

Frequently asked questions

What should AI receive before drafting the report?

Provide approved tables, metric definitions, period, filters, sources and relevant limitations.

Can AI complete an uninvestigated cause?

It should not present it as fact. Identify the hypothesis or remove the unsupported claim.

When is the report ready?

When numbers and conclusions have verifiable origins and readers can distinguish evidence, interpretation and proposal.