How to use AI to write client report commentary reliably
- →AI is good at the report narrative; it is unreliable on messy data.
- →Feed it structured data plus context (KPI definitions, goals), not raw exports.
- →Use tested prompts for summary, anomalies, and recommendations - then review.
Writing the same commentary every week is one of the most repetitive parts of reporting - and one AI handles well, if you set it up right. The failure mode is feeding AI messy data and getting confident but wrong summaries. Here is how to avoid that.
Step 1 - Clean data first
AI reflects the quality of what it reads. Give it structured, consistent data with agreed KPI definitions - not raw platform exports. This is why the reporting system has to come before the AI layer.
Step 2 - Give it context, not just numbers
A prompt that includes what the metrics mean, the client's goals, and the time period produces commentary that is specific and useful. Numbers alone produce generic filler.
Step 3 - Use tested prompts for each job
| Job | What the prompt asks for |
|---|---|
| Summary | A plain-language recap of the week's performance vs goals |
| Anomalies | What changed materially and the likely driver |
| Recommendations | Concrete next actions based on the data |
Step 4 - Keep a human in the loop
AI drafts; you review. The point is to remove the blank-page work, not to send unreviewed output to clients. A quick check keeps quality and trust intact.
The Agency Reporting Automation Kit includes a tested AI insight prompt library built for marketing reporting - summaries, anomalies, and recommendations on your structured data.