This lab takes ~60 minutes of focused work. You will generate tangible outputs — not just understanding. The "Generate Report" button at the bottom compiles everything into one document you'll add to your Phase 2 application.
4 quizzes covering Chapter 1 key concepts — verify your literacy foundation.
5 exercises mapping AI to your real work, with HBR category tagging.
Generate your Chapter 1 Lab Report — first artifact for your Phase 2 Capstone folder.
A system on Line 3 sends an SMS to maintenance every Friday at 5 PM if the daily preventive-maintenance checklist isn't completed. This is:
MPV builds an internal HR helpdesk chatbot that answers Vietnamese-language policy questions ("how many leave days do I have?", "how do I claim overtime?"). Which HBR category does this fall into?
You ask ChatGPT to "summarise ISO 13485 Clause 8.5.2 requirements for medical syringe CAPA timing". The response includes a confident-sounding "30-day mandatory closure" — but this specific deadline does not exist in the standard. What just happened, and what do you do?
MPV leadership asks you to propose the company's first AI project. Based on HBR's research (Davenport & Ronanki, 2018), which approach is most likely to succeed?
Tick the boxes you genuinely already feel comfortable with. Then write your single biggest improvement area in one sentence.
My single biggest improvement area:
Choose one specific repeated task from your department this past month. Describe it concretely (no abstractions), tag it with its HBR category, and explain how AI could help.
Specific task (with frequency):
Pain point + cost (time/quality/risk):
🏷️ HBR category — tag with ① / ② / ③ and explain why:
How AI would help (use one of: summarize, classify, draft, recommend, translate, analyse, check):
Which MPV pillar(s) this fits (Quality / Predictive Maintenance / Supply Chain / Compliance):
Pick one number or dashboard metric from your area where you regularly stare at the screen wondering "why did this change?". This is your Category ② Cognitive Insight candidate.
The metric / data point (with current value):
The question I would ask AI about this data:
Expected output shape:
How I would verify the AI's answer before acting:
Now go run a real AI prompt — ideally the one from Exercise 2 or 3. Paste the AI output into your notes, then score it on the rubric. This is the same scoring discipline you'll apply to every AI output for the rest of your career.
| Rule | Score 1 | Score 2 | Score 3 | Score 4 |
|---|---|---|---|---|
| Relevance | Off-topic | Partly relevant | Mostly relevant | Fully answers the task |
| Accuracy | Wrong / risky | Needs major fact-check | Minor fact-check needed | Reasonable after spot verification |
| Actionability | Cannot use | Needs heavy rewriting | Useful draft | Work-ready after one review pass |
| Ethics / Safety | Unsafe (PII, regulated) | Privacy / bias concern | Mostly safe | Safe and responsible |
Task you gave to the AI:
AI output (short version or your summary):
Your scores (Relevance / Accuracy / Actionability / Ethics, each 1–4):
What you would change about your prompt next time:
You've just generated one real opportunity. Lock it in. Write what you'll actually do — and what you'll not let AI do without review.
I will use AI for these tasks this month (be specific):
I will NOT let AI do these without human review (be specific):
Feedback for the Quanskill team:
Click below after completing the quizzes and exercises. The generated text is your first Phase 2 Capstone folder artifact — save it, you'll combine it with Chapters 2–4 reports in the final lab.
You've built the foundation: AI demystified, types named, HBR framework owned, MPV pillars mapped, first opportunity tagged and scored. Chapter 2 turns this into skill with the CRAFT prompting framework.
📖 Chapter 2 Theory →