Practical guide
AI fluency for strategy, consulting and corporate development
Design strategy exercises that separate evidence, assumptions and recommendations, with a fictional market-entry option review.
AI-enabled strategy work should make the reasoning behind a choice easier to inspect, not simply produce more confident presentations. This original guide helps teams choose exercises across problem definition, option development and decision communication. Its market-entry example is fictional and educational; it does not provide investment advice, market forecasts or a recommendation about an actual transaction.
Specify the decision horizon
A strategy assignment needs a bounded decision and a clear point at which analysis is useful.
In a fictional case, a company is considering entering an adjacent service market. The immediate question is whether to fund a discovery phase, not whether to commit to a full launch. A presentation claiming to resolve the entire expansion strategy would exceed the evidence and authority supplied in the exercise.
Write what can be decided now, what remains contingent and which stakeholder will use the output. Distinguish a problem-framing task from an investment case or integration plan. This prevents a broad strategy label from hiding several materially different types of work.
Build an evidence and assumption inventory
Keep supplied observations separate from estimates and propositions that still need testing.
The exercise pack might include customer interview notes, internal capacity estimates and a competitor's public service description. Interview notes can reveal the views expressed by those participants; they do not establish total market demand. A capacity estimate may be a planning assumption rather than an approved delivery commitment.
Ask AI to organize the inventory, then inspect how each item is described. Mark the source, date, scope and limitation. Avoid creating a precise market-size figure from an incomplete pack merely because the final slide appears to require one. An explicit research question is a legitimate output.
Generate options with different mechanisms
Useful alternatives differ in what the organization would actually do, not just in optimistic or cautious wording.
For the fictional company, compare a limited discovery project, a partner-led trial and no immediate entry. Explain what each option would reveal or preserve. Identify the resources, dependencies and reversibility of each approach. The exercise should not assume that expansion is necessary before the analysis begins.
AI can help identify omitted options or challenge the favored approach. Require the author to remove suggestions that depend on capabilities absent from the supplied context. A broad list of possibilities is only an intermediate output; the final comparison needs a reasoned scope.
Test the argument under a changed premise
A decision memo should show which assumptions matter enough to alter the recommendation.
Change the fictional delivery-capacity estimate or remove access to a proposed partner. Ask the worker to explain which option becomes less feasible and what evidence is now needed. This is more informative than asking them to defend the original recommendation regardless of the new information.
Keep scenario calculations visibly illustrative. Do not attach probabilities or expected returns without a basis in the exercise. A useful sensitivity note can describe the direction of change and the evidence threshold for revisiting a decision without pretending to forecast the market.
Deliver a decision brief, not a verdict on the future
The handoff should distinguish the recommended next commitment from the long-term hypothesis.
Finish with the decision requested, the evidence supporting it, the strongest alternative and the unresolved assumptions. Name the discovery work that would reduce the most consequential uncertainty. Keep the proposed commitment proportionate to what the pack can support, and identify who has authority to approve it.
A reviewer can examine framing, evidence use, option quality and responsiveness to the changed premise. Keep conclusions attached to these observed behaviors. Strategy, consulting and corporate-development teams can reuse the exercise structure, but specialized transaction, legal or financial work requires its own qualified review and should not be inferred from this practice result.
Sources and scope
NIST addresses AI risk management. Skills England describes workplace AI foundations.
These sources provide background, not endorsement of this exercise. The worked example and suggested review method are original illustrative guidance. They are not customer results, validated benchmarks or evidence of a particular product capability. Adapt the exercise to the task and use qualified review where consequences require it.
Sources: [1] [2]
Sources
- 1.AI RMF Core · NIST
- 2.AI foundation skills for work benchmark · Skills England