The task
It’s 9:08 on Monday. The KPI report is due at 10:00, the leadership thread is already moving, and your BI tool just spat out a 14-row table that nobody is going to read. You need a 200-word narrative that explains the wins, names the misses honestly, ties hypotheses to other metrics that actually moved, and points at one thing to watch next week, without sounding like a generic templated update or hiding the one number you wish were different.
Where AI helps — and where it does not
AI is excellent at structure, week-over-week framing, and replacing “MAU was up” with “MAU climbed 4.2% WoW, driven by retention rather than new signups.” It can also keep the report under 200 words without you having to count. What AI cannot do: invent the causal hypothesis. If you do not paste adjacent context — a campaign that launched, a holiday weekend, an outage, a competitor move — the model will pattern-match to generic causes (“possibly due to seasonality”) that calibrate nothing and signal nothing.
The named failure mode: the airport-novel hypothesis. AI fills the “why” slot with a fluent-but-empty sentence (“conversion rose due to improved product-market fit”). Always require it to ground hypotheses in another metric that moved in the same direction.
What to feed the AI
- Compact KPI table (this week, last week, target), 5-10 rows max
- 1-2 sentences of context (campaigns live, holidays, outages, competitor news)
- The 2 metrics leadership cares most about this quarter, so AI weights the narrative
- Last week’s report (so phrasing rotates, not repeats)
- Any known data caveats (BI delay, attribution change, instrumentation bug)
- The audience (your manager, the leadership channel, the all-hands)
- Tone constraint: factual, no hedging adverbs (“somewhat”, “slightly”)
- The one thing you want the reader to remember by Tuesday morning
Copy-ready prompt
You are writing this week's 200-word KPI report.
Audience: {leadership Slack / weekly all-hands / manager 1:1}.
Priority metrics this quarter: {top 2 metrics}.
Context: {campaigns, holidays, outages, attribution changes}.
Last week's report (for phrasing variety, not content): {paste}.
KPI table:
{paste table with this-week / last-week / target columns}
Write in this structure:
1) One-line summary the reader could quote in Slack.
2) Two wins — each names the metric, the WoW delta, and one adjacent metric that confirms it.
3) Two misses — each names the gap, the WoW delta, and a hypothesis grounded in another metric that moved.
4) One thing to watch next week — specific enough that a different number next Monday would change my action.
Rules: no hedging adverbs. No "due to seasonality" unless I gave you a seasonality input. If you cannot ground a hypothesis in another metric I gave you, say "hypothesis pending — need {what data}."Shorter variant — leadership Slack post only
Convert this KPI table into a 60-word leadership Slack post.
1 line summary, 1 line top win with number, 1 line top miss with number, 1 question for the room.
Table: {paste}. Context: {one sentence}.Sample output
A useful narrative paragraph: “Week 19 was a retention story. DAU/MAU climbed to 0.42 (vs 0.39 last week, target 0.40), driven by Day-7 retention on the May 4 cohort (38% vs 31% prior cohort) — not by new signups, which dipped 6% WoW. The miss: paid conversion fell from 3.1% to 2.6%; the drop concentrates in the iOS cohort, which also saw a 22% spike in checkout error rate after the 1.42 release. Hypothesis: client-side checkout bug, not pricing. Watching iOS paid conversion daily until checkout error rate normalizes.”
A useful Slack-post variant: “Week 19: retention up, iOS paid conversion down. DAU/MAU 0.42 (+0.03). Paid conversion 2.6% (-0.5pp), tracking iOS checkout error spike. Question for the room: hold the May pricing test or pause until iOS is clean?”
How to refine
- Ground every hypothesis: “For each miss, name the other metric that moved in the same direction. If none exists in the data I gave you, say so explicitly.”
- Rotate phrasing, not structure: “Rewrite this report using last week’s structure but no repeated transitional phrases. Readers tune out identical openings.”
- Punch the headline: “Replace the summary line with one a leader could quote in Slack — verb, number, direction. No ‘overall, things are tracking well.’”
- Surface the buried miss: “If any KPI is worse than target and worse than last week, it belongs in the misses section even if I forgot to flag it.”
- Trim: “Cut to 180 words. Whatever survives is the signal.”
Common mistakes
- Letting AI invent hypotheses without grounding. “Due to seasonality” with no seasonality input is a hallucination, not analysis
- Same opening sentence every week. Readers train themselves to skim past it
- Hiding misses in the middle of a wins paragraph. Calibration will surface them later, more painfully
- Pasting the entire BI dump (50 rows). The model dilutes the narrative across noise
- No “thing to watch”. Readers don’t know what to expect different next Monday
- Omitting outages or attribution changes from context. AI then writes confident hypotheses for what is actually a data artifact
- Letting AI hedge (“conversion may have slightly dropped”). Every hedge is a sentence that does no work
- Forgetting to include the target column. Wins-vs-last-week without wins-vs-target hides the real story
FAQ
- Should the structure change weekly?: Slightly. Same skeleton (summary, wins, misses, watch), fresh phrasing. Readers tune out identical reports faster than you think.
- What if numbers were affected by an outage?: Flag the outage in context. AI will then label the affected line “data caveat” instead of inventing a business hypothesis.
- Should I include charts?: Reference them (“see iOS conversion chart”) but do not try to embed in the narrative. The report is text; the dashboard is the source of truth.
- How do I handle a metric I’m not sure how to interpret?: Tell the AI: “For metric X, I’m unsure whether the move is meaningful — list the two adjacent metrics I should check before claiming a hypothesis.” Then check them.
- Can I reuse one prompt forever?: Yes for structure, no for context. The structure stabilizes; the context (campaigns, outages, priorities) is what makes the narrative useful.
Related
- Weekly planning — the planning side of the weekly cadence
- Financial trend analysis — multi-quarter trends instead of WoW
- Budget narrative AI — annual budget storytelling
- Funnel analysis readout AI — funnel-specific version
- Business data analysis AI — adjacent analysis prompts
- Manager update email AI — narrative for the one-up