IL 201 | Industry AI Proof-of-Value Co-creation Lab
Work with an industry sponsor and cross-domain advisors to turn a real industry problem into a testable AI solution, and use the prototype, evaluation, and business evidence to decide whether it is ready for a pilot. Proof of Value, not just proof of concept.
Your next step
IL 201 · Module 1: Problem and Hypothesis
Curriculum
- Module 1Problem and HypothesisLock down the problem and baseline, write a falsifiable solution hypothesis, and get an initial usage-scope approval.
- Module 2Evaluation DesignDesign a human/non-AI/AI three-way comparison, split a dev set from a held-out acceptance set, and define measurement and stop criteria.
- Module 3Prototype BuildBuild a minimal-scope, reproducible prototype, and record its version, run configuration, and human-handoff path.
- Module 4Testing and Failure AnalysisTest on the acceptance set with development kept separate from acceptance, break down errors and rework by category, and write a failure log.
- Module 5Cost and ValueAccount for tooling, review, operations, exception-handling, and data-preparation cost, and complete a cost/value sheet.
- Module 6Evidence ReviewCite evidence, state limitations, and write a decision brief recommending Proceed, Iterate, Hold, or No-Go.
Progress is stored in this browser and does not identify you. It is uploaded only if you join progress sync below.
Practice and assessment
- PracticeWith an industry sponsor and cross-domain advisors, build the prototype, run the evaluation, and analyze the results.
- AssessScored against the rubric weights, with an Evidence Gate decision and a sponsor review.
- ApplyDepending on the Evidence Gate decision, carry the project into IL 202 readiness review or IL 301's pilot.
About this course
Outcomes
- Maintain fidelity between the solution and the original problem statement
- Design a testable solution hypothesis
- Build a prototype good enough to evaluate
- Design a rigorous evaluation plan and evaluation dataset
- Produce evidence that supports business value
- Identify responsible-AI and risk issues
- Maintain appropriate human oversight
- Complete a sponsor review and produce a final recommendation
Prerequisites
- IL 101, or an approved equivalent project charter, is required
- Project-specific AI/DS courses may additionally be required
Who it is for
- Project teams with an approved project charter who need to validate a solution's value
- AI/data leads and technical owners
- Industry sponsors and process owners who will join the sponsor review
Certificate
Earns a capability certificate (L2).
Requirements to earn it
- Submit a project artifact and pass review
Why this course
IL 201 answers one question: is this AI solution valuable and feasible enough to justify further investment? Success is not building a demo: it is producing enough evidence to make a better investment decision. A well-supported No-Go is as successful a learning outcome as a Scale decision.
Learning and project time planning
| Formal guided hours | 18 hours |
| Estimated independent/applied project time | Not yet determined |
| Suggested real-world project span | 3-4 weeks |
Proof-of-Value deliverables
- Approved project charter
- Solution hypothesis
- Prototype
- Evaluation plan
- Evaluation cases/dataset
- Results
- Failure analysis
- Risk notes
- Sponsor review
- Final recommendation
Minimum conditions before a project is admitted to IL 201
- A confirmed executive sponsor
- A confirmed business/process owner
- An approved problem statement
- A defined baseline
- A defined target outcome
- A clearly defined evaluation metric
- A feasible evidence/data plan
- An acceptable privacy/security scope
"No Sponsor, No Project": the industry sponsor commits to joining kickoff, validating scope, naming a process owner, enabling access to data, confirming the baseline/KPI, and joining milestone and final reviews (or delegating).
Formal guided time is 18 hours, recommended over 3-4 weeks, following Sprint 0 (kickoff), Sprint 1 (solution hypothesis), Sprint 2 (build), Sprint 3 (test), Sprint 4 (evaluate), and Sprint 5 (decision).
Assessment rubric weights
| Problem fidelity | 10% |
| Solution design | 15% |
| Prototype quality | 15% |
| Evaluation rigor | 20% |
| Business-value evidence | 15% |
| Responsible AI/risk | 10% |
| Human oversight | 5% |
| Sponsor review | 10% |
Completing IL 201 moves the project to Gate 2 (the Evidence Gate): PROCEED TO READINESS, ITERATE, HOLD, or NO-GO, decided jointly with the sponsor review.
Certificate
Industry AI Proof-of-Value Certificate. Requires: the project was admitted, the required sprints were completed, the prototype was delivered, the evaluation was completed, the sponsor review was completed, and the academic rubric was passed: attendance alone does not earn the certificate.
Roles
- Industry sponsor: validates the problem, confirms the target and KPIs, and joins the sponsor review
- Business/process owner: owns the affected workflow
- Cross-domain advisor: helps with framing and evidence quality
- AI/data advisor: technical feasibility, architecture, data, and evaluation
- Project team: 3-5 learners
Data and privacy requirements
The prototype and evaluation data may include process and data summaries provided by the enterprise and learner submissions. Treat them as sensitive: store them only in managed locations, share them only with authorized people, and follow the project data-retention policy. Synthetic data, de-identified data, or controlled sandbox data is acceptable; unrestricted production data is not required.
Related courses and next step
Requires an approved IL 101 project charter; specific AI/DS courses may be required as technical prerequisites depending on the project.
Depending on the Evidence Gate decision, carry the project into IL 202's readiness review, or plan directly for IL 301's pilot.
Course feedback
It takes 2 to 3 minutes and is used to improve the course.