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Module 3 · Stewardship: make it durableWed, Oct 1460 min
Value and evidence
Define the outcome the workflow should change, establish a baseline, and create a small evidence set for executive review.
Faculty: Prof. Suraj Srinivasan
Learning objectives
- ▸Define ROI for an AI portfolio
- ▸Handle baseline and attribution challenges
- ▸Build board reporting for AI value
Key concepts
AI ROI typesBaseline & attributionKPIs & value realizationBoard reporting
Memorize this
🧠 ROI: save, make, avoid, grow🧠 Attribution problem🧠 KPI dashboard
Your study flow
- 1. Read
Read the session guide above. Note the ideas that change how you see your organization's current AI question.
- 2. Test
Take the module quiz in Learn & Memorize to check understanding.
- 3. Drill
Run the spaced-repetition flashcards to lock in the terms from this module.
📝 Test this session
Question 1 of 5Value and evidence
What is the attribution problem?
Flashcards for this module
- Risk registerA living table of AI risks: category, likelihood × impact scoring, owner, and mitigation. The core of a governance framework that survives audit.
- EU AI Act risk tiersUnacceptable (banned), High (strict obligations), Limited (transparency), Minimal. Classification depends on the use case and sector. Timeline enforced in phases.
- NIST AI RMFU.S. AI Risk Management Framework: govern, map, measure, manage. The free anchor reference for responsible AI governance.
- Bias assessmentSystematically checking an AI system's training data, design, and outputs for unfair outcomes across groups: before and after deployment.
- Consent modelDefines how and when people agree to their data being used by AI: required for trust and, in regulated contexts, compliance.
- Attribution problemProving how much of an outcome is caused by AI vs other factors. The hardest part of AI ROI: requires a baseline and control/cohort thinking.
Build for Module 3: Governance Framework + Implementation Roadmap