Block 1
Strategic Intent
What do we want to achieve with AI?
Ambition
What is the 1 dominant AI strategic priority?
Bottleneck | Business case | Workflow | Business Unit
Metrics That Truly Matter
Primary metric (revenue, EBITDA, NPS, time-to-market...)
Leading indicator that predicts success
Time Horizon
6–12 months: what specific win are we targeting?
12–36 months: what capability are we building?
AI Posture
Fast follower | Co-creator | AI-native
Block 2
Value at Stake
Where is the money?
Value Map
Where is value created today? (top 3 processes)
Where is value destroyed today? (costly inefficiencies)
Where is value dormant or under-captured?
Quantification
$ at stake if we solve the bottleneck
Benchmark vs. competitors or best-in-class
To-Be Archetype
Which archetype are we moving toward with AI?
Degree of value chain control: low → high
Block 3
Decision Advantage
Which decision, if improved, wins the game?
Decision Taxonomy
Strategic (annual) → capital allocation, M&A, portfolio
Tactical (monthly) → pricing, capacity, product
Operational (daily) → fulfillment, service, risk
Prioritization
Which decision has the highest economic impact?
Which has the highest frequency and volume?
Is the bottleneck human, data-driven, or process-driven?
Solution Design
Human-in-the-loop | AI-assisted | Fully automated
What signal (data) do we need that we don't have today?
Block 4
Digital Capabilities Stack
What must we build vs. buy?
Data — The Foundation
Do we have proprietary data that is hard to replicate?
Data quality, accessibility, governance: what is the gap?
Models & Software
External foundation model (OpenAI, Anthropic, Google...)
Fine-tuning / RAG with proprietary data → differential edge
Build vs. buy vs. partner?
Infrastructure
Cloud / on-prem / hybrid → what does regulation dictate?
Technology interdependency: low → high
Build capabilities, not vendor dependencies.
Block 5
Strategic Bets & Roadmap
What do we bet on — and what do we leave out?
The 1–2 Bets
Bet 1: [name] → value hypothesis → success metric
Bet 2 (optional): same structure
Next 90 Days — Sprint Zero
Week 1–2: diagnosis and data audit
Week 3–6: prototype / MVP
Week 7–12: pilot, measure, decide
What We Will NOT Do
Projects we consciously deprioritize
Vendors / technologies we are not adopting now
Is this bet coherent with our AI posture and strategic archetype?
Block 6
Risk & Responsibility Matrix
What can go wrong — and how do we manage it?
People Risk
Which roles change or disappear? Reskilling plan?
Is there cultural resistance? How do we address it?
Ethics & Bias
What biases could the model amplify?
Who audits AI decisions?
Regulation
EU AI Act, GDPR, local regulation — what applies?
Do we need explainability (XAI) for compliance?
Dependency
What happens if the vendor raises prices or shuts down?
Contingency plan and exit strategy