No more worked examples. You've learned the moves — this chapter is where you prove they're yours.

## The Scenario

It's Sunday night. The CEO sends:

><b>Board meeting Thursday. I need a QBR pack — where we stand on Q2 priorities, what the data says about each, and one strategic question the board needs to weigh in on. You have until Wednesday EOD.

You don't have an analyst team. You have the agent you built in Lab 4.3.

## What the Board Actually Wants

A QBR that takes a position — not a status update:





Which are on track with evidence, which aren't with the gap

Vague summaries fail here. The board doesn't want "revenue is healthy." They want "we're hitting the MRR target but SMB is in product-specific trouble — we need to decide on the VP of SMB hire before May."

## What You're Proving

Three things this chapter tests — without telling you in advance which one will trip you up:

* Whether your agent's persona is sharp enough to produce a board-grade brief without you editing it;
* Whether you can spot when the agent skipped a step or hedged a finding;
* Whether you can iterate the prompt fast when the first output isn't there yet.

If the first brief is mushy, you don't blame Claude. You tighten the persona.

**Done when:** you have a QBR pack you'd send to the CEO unedited.

A hands-on course for non-technical managers and operators who need to turn raw business data into decisions, not summaries. You'll learn how to frame analytical questions sharply, read spreadsheets and internal docs through AI, combine internal numbers with market context, and build your own analyst agent that produces decision-ready briefs end-to-end.

Introduction to business analytics through the lens of AI — why raw data doesn't make decisions, the three layers of analytical questions, and where an AI assistant actually fits in the workflow.



Train the framing reflex. Turn vague business questions into sharp ones, pick the metric that actually matters, and lock down time windows, segments, and comparisons before touching any data.



Hands-on labs with the three sources every operator hits: sales spreadsheets, strategy docs in Notion, and market context from the web. Single-tool labs first, then a combined investigation.



Move from running labs to building your own. Write the persona, wire up the tools, and create an agent that delivers weekly KPI briefs and pre-meeting decision packs — finishing with a capstone Quarterly Business Review.



Capstone: The Quarterly Business Review

The Scenario

What the Board Actually Wants

What You're Proving