Lesson 3 — AI for Everyday Business Work

Duration: 120 minutes

Included delivery track: Follow LAB_SETUP.md for the supplied files and local simulation. Required activities use these materials; connected deployments are optional extensions. Instructor debrief: answer key. Real OAuth, cloud releases and payments are simulated or discussed through evidence packets. Deck: 03-ai-for-business-work
Lab: Operations Intelligence Sprint

Learners will be able to

turn a messy business question into a reviewable AI workflow;
use AI with spreadsheets, documents, web research, GitHub, and team chat;
preserve sources, formulas, decisions, and human approval; and
recognize when a task needs a connector/tool, a structured dataset, or a human rather than a generic chat prompt.

Instructor preparation

Use the five linked exports in Lab 3. Research sources are fabricated excerpts, not live pages. Recompute the budget instead of assuming CSV totals recalculate. Prepare a plain-text memo; connected-app work is an optional, separately approved extension.

Agenda

Time	Facilitation
0–10	Arrival: ask teams to list work they already do in Google Docs, Sheets, Discord, and GitHub. Mark where errors are costly.
10–25	Teach the business workflow canvas: source → transform → review → decision → record. Show why a pasted summary is not a system of record.
25–40	Demonstrate: clean a CSV without overwriting raw data, ask AI for anomalies, verify two calculations, and draft a cited one-page memo.
40–50	Mini-practice: turn a noisy chat thread into actions, decisions, assumptions, and unresolved questions.
50–60	Break.
60–95	Teams complete the operations sprint. Each member owns one surface: spreadsheet, docs/research, GitHub, or Discord.
95–110	Present decision memos. The audience asks, “Where did this number/claim come from?”
110–115	Discuss appropriate automation: drafts and triage can be automated; external sends, payments, publishing, and data changes need approval controls.
115–120	Exit ticket: name one work process AI can accelerate and the proof/approval it still needs.

Teaching notes

This is the course’s broad-business lesson. Keep it practical: researchers cite sources; spreadsheet users preserve raw data and verify formulas; communicators separate quoted decisions from AI-generated suggestions; developers connect issues, diffs, and tests. Learners should leave seeing AI as an operating layer across work, not just a chatbot.

Formative coaching

Teams submit a sourced decision memo, unchanged raw-data file, cleaned analysis, and a one-paragraph automation-boundary statement.

Product Studio connection — Make a business decision

Use up to five minutes of the existing closing/debrief time for a project next-step check; do not extend the 120-minute session. The build work happens as homework. Create a small synthetic dataset, checked CSV/spreadsheet analysis, source log and one-page decision memo for your product. Draft a concise mock team update; no live Google/Discord posting required.

Follow the milestone map and review checklist. Record support needs privately; practice lab quality criteria are coaching, not an exam or numeric completion gate.