Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.
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Forecasting produces one number and false confidence. Scenario planning produces a plan that survives being wrong, which is the realistic goal.
List the plan's assumptions explicitly, then sort them:
Almost every plan has two or three load-bearing uncertainties. Finding them is most of the value, and the exercise usually surfaces one nobody had articulated.
The common failure is three scenarios named optimistic, base, and pessimistic — which is one scenario with the numbers scaled, and it teaches nothing.
Take the two most consequential uncertainties and build the quadrants. Each scenario should be internally coherent: if demand is high and supply is constrained, what else follows — pricing, competitor behavior, regulatory attention?
Give each a name that captures its logic. Names make scenarios usable in conversation, which is where they earn their keep.
Three or four scenarios. More cannot be held in mind; two collapses into best and worst.
For every scenario: does the plan still work, what breaks first, and what would we wish we had done sooner?
The output is not a prediction. It is three things:
For each scenario, name the observable signal that would show it is arriving — and specify it precisely enough to be checked. "Regulatory pressure increases" is not observable. "A second jurisdiction opens a consultation" is.
Assign each indicator an owner and a review cadence. Scenario work that produces no monitoring is a workshop, not a plan.
Most scenario planning is done once and filed. Its value comes from being revisited when an indicator fires — that is the moment the earlier thinking pays, because the options were identified before anyone was under pressure.
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概要と使いどころ
Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process. Use this to design a permissions model, run an access review, reduce standing privilege, handle offboarding, set up SSO or MFA, manage service and machine credentials, or diagnose why permissions have sprawled.
日本語の概要は準備中です。原文の説明を表示しています。
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account. Use this to design or fix signup and onboarding, diagnose why signups do not convert to active use, reduce time-to-value, or decide what a new user must accomplish first.
日本語の概要は準備中です。原文の説明を表示しています。
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
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Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
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Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
日本語の概要は準備中です。原文の説明を表示しています。