Strategic Alignment
Q1: Have we clearly documented our winning aspiration, where-to-play, how-to-win, required capabilities, and management systems?
This assesses whether your current strategy is explicit enough to evaluate AI’s impact on it.
Select a score…
1 - Not documented; strategy exists mostly as tribal knowledge.
2 - Partially documented; key choices are implicit or inconsistent.
3 - Documented at a high level; gaps remain in capabilities/systems detail.
4 - Well documented; most leaders align on the choices and logic.
5 - Fully explicit and operational; choices are clear, current, and used to guide decisions.
Selected score: —
Q2: Have we identified where AI could threaten or amplify each of those strategic choices and captured the assumptions behind them?
This checks whether you’ve pressure-tested strategy choices against AI-driven changes.
Select a score…
1 - No structured review of how AI impacts our strategic choices.
2 - Some discussion occurred; assumptions are not captured or prioritized.
3 - Assumptions captured for a few choices; not complete or repeatable.
4 - Assumptions captured across most choices; prioritization is mostly clear.
5 - Comprehensive and repeatable; assumptions are explicit, prioritized, and reviewed regularly.
Selected score: —
Q3: Do we have a clear AI-driven strategic thesis (the “why now” and “how we win” with AI)?
A short statement connecting your ambition to AI-driven threats/opportunities.
Select a score…
1 - No thesis; AI is treated as tools and projects.
2 - A loose thesis exists; not specific enough to guide choices.
3 - Thesis exists; not consistently used to prioritize or communicate.
4 - Thesis is clear and used to guide priorities and trade-offs.
5 - Thesis is crisp, shared broadly, and drives portfolio, funding, and execution.
Selected score: —
Q4: Have we prioritized 1–2 AI pressures/opportunities with executive sponsorship and clear outcomes?
This checks focus: selecting the few AI bets that matter most.
Select a score…
1 - No priorities; many disconnected AI efforts.
2 - Priorities are implied; no clear sponsorship or outcomes.
3 - Some focus exists; outcomes and ownership are uneven.
4 - Clear priorities with sponsorship; outcomes mostly defined.
5 - Clear priorities with sponsorship, outcomes, and resourcing; execution is actively governed.
Selected score: —
Average Score for Strategic Alignment: 0.00
AI Fundamentals and Matching
Q5: Do leaders share a common, practical understanding of AI as prediction (data → model → predicted output)?
This reduces “AI magic thinking” and improves use-case selection.
Select a score…
1 - No shared understanding; confusion is common.
2 - Some understanding; not consistent across leadership.
3 - Baseline understanding exists; still uneven in practice.
4 - Strong shared understanding; informs decisions frequently.
5 - Deep, practical understanding; leaders consistently reason from first principles about AI feasibility and value.
Selected score: —
Q6: Do we distinguish AI capability types when selecting opportunities (perception, prediction, generative, automation)?
This prevents over-indexing on chatbots and improves solution fit.
Select a score…
1 - We treat most AI as the same (often “LLMs for everything”).
2 - We sometimes distinguish types; selection is inconsistent.
3 - We distinguish types for major initiatives; not yet standard practice.
4 - Capability matching is a standard step in our process.
5 - Capability matching is disciplined and measurable; we reliably choose the right AI approach for each strategic problem.
Selected score: —
Q7: Do we explicitly choose the autonomy level for AI use cases (augmentation vs automation vs autonomous) based on risk?
Autonomy is a governance decision; not all use cases should be automated.
Select a score…
1 - We do not define autonomy; it happens by default.
2 - We define it occasionally; not consistently tied to risk.
3 - We define autonomy for many use cases; enforcement is uneven.
4 - Autonomy decisions are explicit, risk-based, and documented.
5 - Autonomy decisions are explicit, audited, and continuously improved with monitoring and controls.
Selected score: —
Q8: Do we plan for AI limitations (hallucination, bias, edge cases, reliability) with controls and human oversight where needed?
This protects quality, safety, trust, and brand risk.
Select a score…
1 - We do not address limitations; failures would surprise us.
2 - We acknowledge limitations; controls are informal or ad hoc.
3 - Controls exist for some systems; not consistent enterprise-wide.
4 - Controls are defined and applied consistently; oversight is clear.
5 - Controls are robust and measured (monitoring, evals, escalation); governance is mature and adaptive.
Selected score: —
Average Score for AI Fundamentals & Matching: 0.00
Mapping AI Across Your Value-Creation Logic
Q9: Have we mapped AI opportunities across how we produce/deliver the core product or service (quality, maintenance, design, execution)?
This targets AI leverage points in production or service delivery.
Select a score…
1 - No mapping done; production/service delivery is not analyzed for AI.
2 - A few ideas exist; no structured mapping.
3 - Mapping exists for major stages; gaps remain.
4 - Mapping is comprehensive; clear hypotheses exist by stage.
5 - Mapping is comprehensive and prioritized; tied to measurable outcomes and execution plans.
Selected score: —
Q10: Have we mapped AI opportunities across core operations and workflows (document handling, forecasting, decision support, workflow automation)?
This targets the operating engine of the business and decision workflows.
Select a score…
1 - No mapping done; operations are not analyzed for AI leverage.
2 - A few isolated opportunities identified.
3 - Mapping exists for key workflows; prioritization is limited.
4 - Mapping is broad and structured; opportunities are prioritized.
5 - Mapping is complete, prioritized, and actively translated into a transformation roadmap.
Selected score: —
Q11: Have we mapped AI opportunities across strategic resources and assets (data, models, knowledge, infrastructure)?
This checks whether you’re building differentiated assets (especially data and knowledge).
Select a score…
1 - No asset mapping; data/knowledge are not treated as strategic AI assets.
2 - Some asset ideas exist; no strategic plan for them.
3 - Key assets are identified; investments are partial.
4 - Assets are mapped and funded; ownership and strategy are clear.
5 - Assets are a competitive moat; continuously improved with clear governance and measurement.
Selected score: —
Q12: Have we mapped AI opportunities across suppliers and partners (procurement, compliance, risk, collaboration)?
This targets value creation (and risk reduction) in the extended enterprise.
Select a score…
1 - No mapping; partner/supplier value chain is not analyzed for AI.
2 - A few opportunities identified; not structured.
3 - Mapping exists for key partner processes; limited prioritization.
4 - Broad mapping and prioritized opportunities; pilot plans exist.
5 - Mapping drives measurable improvements; integrated into procurement and partner operating model.
Selected score: —
Q13: Have we mapped AI opportunities across customer segments (new segments, micro-segmentation, personalization feasibility)?
This targets new growth and competitive differentiation.
Select a score…
1 - No segmentation mapping; AI is not linked to segment strategy.
2 - Some ideas exist; not quantified or tested.
3 - Mapping exists for key segments; early test planning.
4 - Mapping is robust; segment bets are prioritized and tested.
5 - Segment mapping drives measurable growth and differentiation; continuously refined with learning loops.
Selected score: —
Q14: Have we mapped AI opportunities across the offer/value proposition (personalized offers, new AI-enabled services, co-creation)?
This targets how you create and deliver customer value (not just efficiency).
Select a score…
1 - No mapping; AI is not connected to value proposition evolution.
2 - A few ideas exist; not prioritized or validated.
3 - Mapping exists for key offers; testing is partial.
4 - Mapping is comprehensive; prioritized hypotheses are in motion.
5 - Mapping drives new value creation and measurable differentiation; scaled across portfolio.
Selected score: —
Q15: Have we mapped AI opportunities across channels and customer experience (support, engagement, routing, interfaces)?
This targets how customers discover, buy, use, and get support.
Select a score…
1 - No mapping; AI is not connected to channel/experience strategy.
2 - Isolated experiments; no experience architecture.
3 - Mapping exists for key journeys; pilots are underway.
4 - Mapping is broad and prioritized; experience impact is measured.
5 - Mapping is executed at scale; customer experience improvements are continuous and differentiated.
Selected score: —
Q16: Have we mapped AI opportunities across revenue and cost logic (pricing, monetization models, automation-driven cost shifts)?
This targets profitability and new business model options.
Select a score…
1 - No mapping; AI is not connected to profit model design.
2 - Some ideas exist; ROI logic is weak or missing.
3 - Mapping exists for major levers; some quantification.
4 - Mapping is quantified and prioritized; pilots test ROI and unit economics.
5 - Mapping drives measurable margin expansion and/or new revenue models at scale.
Selected score: —
Average Score for Value-Creation Mapping: 0.00
Execution and Governance
Q17: Do we express priority AI opportunities as testable hypotheses with expected value, required data, and clear success metrics?
This ensures learning speed and prevents “pilot theater.”
Select a score…
1 - No hypothesis discipline; initiatives are vague or solution-led.
2 - Some hypotheses exist; metrics and data needs are unclear.
3 - Hypotheses are common; measurement is inconsistent.
4 - Hypotheses, metrics, and data needs are standard; learning loops exist.
5 - Hypothesis discipline is rigorous; decisions are driven by evidence, ROI logic, and systematic learning.
Selected score: —
Q18: Do we have governance to move from pilots to scale (risk review, monitoring, ownership, funding, operating model changes)?
This assesses whether AI becomes real capability, not isolated experiments.
Select a score…
1 - No governance; scaling is rare and chaotic.
2 - Governance exists in pockets; unclear ownership and controls.
3 - Governance defined; execution is uneven and slow.
4 - Governance is clear; scaling path is repeatable and supported.
5 - Governance is mature and adaptive; scale is routine, measured, and integrated into business strategy and operations.
Selected score: —
Average Score for Execution & Governance: 0.00
