I'm a senior data analyst at a mid-size e-commerce company. Every month I build a 15-page marketing performance report. Every month my CMO reads the first page and ignores the rest.

Loaded this agent into Claude, pasted in my raw metrics, and asked for an executive summary. It restructured everything — headline insight first, traffic light status, three actions. My CMO read the whole thing in a meeting and asked follow-up questions based on the anomaly section. That has never happened.

The "so what?" framework alone changed how I think about presenting data. I now refuse to show a number without context.

I ran an A/B test on our checkout flow. Results were statistically significant (p=0.02, 8% lift in conversion). I knew it was a big deal. But explaining "p-value of 0.02" to our CEO was going to be painful.

Fed the results to this agent and asked it to translate for a non-technical audience. It produced: "We tested the new checkout flow against the original with 12,000 users. The new version converted 8% more customers. We're 98% confident this is a real improvement, not random chance. At current traffic, this translates to roughly [X] additional conversions per month."

That's exactly what the CEO needed. Decision approved in the meeting. No glazed-over eyes. No "can you explain what a p-value is."

I pasted in a quarterly report draft and asked it to narrate the executive summary. Instead of blindly writing it up, it flagged that my revenue breakdown by segment didn't add up to the total revenue figure I'd given — there was a $180K discrepancy.

I'd been staring at that data for two days and hadn't caught it. Turned out I'd double-counted a revenue reclass. That error would have gone to the board.

An AI agent that refuses to narrate inconsistent data is worth infinitely more than one that just writes whatever you tell it to.

I needed to present the same quarterly results to our CEO, our analytics team, our client, and our board. Four different audiences, four different levels of depth. Used to take me a full day.

Fed the data to the agent once. Asked for four variants. Got an exec summary (1 page), an analyst deep-dive (with methodology notes), a client-facing version (outcome-focused), and a board version (risk-framed). Ten minutes. All four were usable with minor edits.