Score one AI use case on 12 blocks in 4 pillars. Mark each block red, yellow or green, and the gaps show before you build.
Canonical reference: A Working Demo Is Not a Business Case: the AI Use Case Blueprint
Test the case before the budget. A 90-minute demo with a slick UI shows the model can run on chosen data. The Blueprint brings up what it leaves out (data foundations, defensibility, unit economics, governance) before you commit budget and reputation.
Score what is true today. Score each block on today's data quality, talent, governance and contract structure. Score the plan you hope for and every block turns green, which tells you nothing.
Does this use case solve a real, high-value problem with a viable data and technology approach?
Does this use case deliver provable business value with real market demand and defensibility?
Can you actually build, ship, and deliver this use case to real users?
Will it still pay, and still pass review, at scale and over time?
Circle one score per block. Transfer your scores from each section above.
What to do based on where your reds appear.
| Pillar with Red | Action Required |
|---|---|
| Foundation Blocks 1–3 |
STOP. The use case has a structural flaw. Return to problem discovery. Do not invest further until all Foundation blocks are Yellow or Green. |
| Value Engine Blocks 4–6 |
PAUSE. The technology may work, but the business case doesn't hold. Validate demand, sharpen the value proposition, or find defensibility before continuing. |
| Execution Blocks 7–9 |
PROCEED WITH CAUTION. Build mitigation plans for each red block. Assign owners, set timelines, and review weekly. These are solvable with effort. |
| Sustainability Blocks 10–12 |
PLAN BEFORE SCALE. You can pilot, but do not scale until these are addressed. Sustainability reds become fatal at production volume. |
| All Green Blocks 1–12 |
FULL SPEED AHEAD. This is rare. Build, and re-score at each stage, because a green block can turn red once real users and real data arrive. |
Definitions used throughout this worksheet. They match the article.
AI Use Case Blueprint. A 4-pillar / 12-block decision framework for evaluating any AI initiative before resource commitment. Pillars: Foundation (Strategic Intent), Value Engine (Business Value), Execution (Implementation), Sustainability (Long-Term Viability).
STOP / PAUSE / PROCEED WITH CAUTION / PLAN BEFORE SCALE. The four pillar-level verdicts. STOP: fix the problem, the data or the technical approach before any further spend. PAUSE: prove demand before building further. PROCEED WITH CAUTION: give each red an owner and a date. PLAN BEFORE SCALE: a pilot can run; scaling waits.
Cold Start Problem. The challenge of launching an AI use case with insufficient data to train or evaluate the model. Affects Block 2 (Data Strategy) scoring.
Data Flywheel. The compounding advantage when AI use creates more/better data, which improves the model, which drives more use. Affects Block 2 (Data Strategy) and Block 12 (Continuous Improvement) scoring.
Defensibility Moat. The mechanism by which an AI use case sustains competitive advantage over time. Most often a function of data, distribution, or proprietary workflow integration, and rarely the model itself. Affects Block 6 (Competitive Landscape) scoring.
Foundation Model. A large, general-purpose AI model (GPT-4, Claude, Gemini) that other applications are built on. Buying access to a foundation model is partly vendor evaluation, partly platform-risk acceptance.
Unit Economics. The cost-per-output and revenue-per-output of the AI use case at production scale. Often the least-tested part of a pilot. Affects Block 10 (Unit Economics) scoring.
Governance & Compliance Block (Block 11). The Blueprint block where regulatory exposure (EU AI Act, GDPR, HIPAA, sector rules) is scored alongside internal governance maturity. EU AI Act fine ceilings under Article 99, whichever is higher of the amount and the share of worldwide annual turnover: €35M or 7% for prohibited practices (Art. 99(3)); €15M or 3% for breaches of most operator duties, including the high-risk obligations and the Article 50 transparency duties (Art. 99(4)); €7.5M or 1% for supplying incorrect, incomplete or misleading information to authorities (Art. 99(5)). For SMEs, including start-ups, the lower of the two applies (Art. 99(6)); for small mid-caps (SMCs), the lower applies to the 99(4) and 99(5) tiers (Art. 99(6a), inserted by Regulation (EU) 2026/1744).
This worksheet goes with a published, sourced article. The method, the data behind it and the companion frameworks are linked below.
Canonical article: A Working Demo Is Not a Business Case: the AI Use Case Blueprint: the full method, the pillar logic, the scoring rules and a worked example.
Companion frameworks:
External sources: 451 Research, VotE: AI & Machine Learning (May 2025) · McKinsey, The state of AI in 2026 · Gartner on agentic AI projects (June 2025) · Regulation (EU) 2024/1689, Article 99, as amended by Regulation (EU) 2026/1744, Article 1(38)