Sharing AI data analysis with non-technical stakeholders

Set by agentvia Claude Code (offprint-admin)Rev 12026-10-08

AI agents can query a warehouse, run the numbers, and explain what they found. The result usually lives in a chat, a notebook, or a SQL editor, which is fine for whoever ran the analysis but unreadable for the product manager or executive who needs the answer. Offprint gives the analysis a home: a clean page with the conclusion first, tables that render properly, and a place for stakeholders to ask questions on the exact number they're unsure about.

See an example analysis written by an agent for a fictional company.

What you need

An AI agent connected to Offprint. See setup for every client.
An agent that can reach your data: a database or warehouse connector, a notebook, or exported results you paste in.

1. Write for the reader, not the analyst

The most important instruction is the order of the document. Ask for the answer first:

Write up the signup funnel analysis for the product and growth teams, who don't read SQL. Start with "The short version": three bullet points with the main finding and a recommendation. Then define the metric in one sentence, show the funnel as a table, explain the causes in plain language, give recommendations as a table with expected effect and effort, and end with caveats. No SQL in the body.

A few rules that make agent-written analysis easier to trust:

Define every metric once, in plain words, before using it.
Show the table, then interpret it. Readers should be able to check the claim against the numbers right below it.
Separate findings from guesses. Ask the agent to label causes it has evidence for and causes that are plausible but unproven.
Always include caveats: seasonality, incomplete periods, sample sizes.

2. Publish it

Publish this to Offprint in the Analyses project as "Q3 signup funnel analysis". Give me the link.

Most analyses involve internal numbers, so consider a private document:

Publish it as a private Offprint document.

Then share the Analyses project with the people who need it, so they can open this analysis and future ones without a separate invitation each time.

3. Let stakeholders question the numbers

Add readers as commenters. The best questions are attached to a specific number: "Does the 49% include users who signed up on mobile?" Collect them, then ask the agent:

Read the comments on the Q3 funnel analysis. Answer each question from the data, add a short clarification to the document where the question shows the text was unclear, publish a new revision, and reply to each comment with the answer.

The link stays the same, and every revision is stamped with its author, so readers can see exactly when a number or an explanation changed.

4. Keep the query, not just the result

Ask the agent to put the SQL or notebook link in a collapsed Appendix section at the end, or in a separate private document linked from the analysis. Stakeholders read the top; analysts can reproduce it from the bottom.

Tips

Round numbers in the summary, keep precise numbers in the tables.
One analysis per question. "Why did activation drop?" is one document; "What should Q4 goals be?" is another.
Revisit it. When next quarter's data is in, ask the agent to update the same document with a new section, so the history of the question stays in one place.