planning-under-uncertainty

Planning Under Uncertainty

Scope

Covers

• Turning ambiguity into an executable plan via hypotheses, experiments, and decision triggers
• Diagnosing “what’s actually happening” before acting (especially in crisis / wartime situations)
• Using data as a compass (directional checks) rather than a GPS (false precision)
• Building buffers and contingencies so the plan survives chaos
• Setting a cadence for learning, decision-making, and stakeholder communication

When to use

• “We need a plan, but the requirements are unclear and the outcome is uncertain.”
• “Create a hypothesis-driven plan (experiments + decision rules) for this initiative.”
• “We’re in a crisis (drop in retention/revenue/reliability) and need a wartime diagnosis + action plan.”
• “Help us build contingencies, buffers, and pivot triggers before we commit.”

When NOT to use

• You don’t agree on the underlying problem/opportunity (use problem-definition).
• You need to choose what to do among many options (use prioritizing-roadmap).
• You already have a clear plan and only need dates/milestones and stakeholder cadence (use managing-timelines).
• You need a decision-ready PRD/spec for build execution (use writing-prds / writing-specs-designs).
• You’re weighing a specific binary or multi-option decision with known trade-offs (use evaluating-trade-offs).
• You need to map systemic interdependencies and feedback loops, not plan under ambiguity (use systems-thinking).
• You need to cut scope to hit a fixed timebox, not explore unknowns (use scoping-cutting).

Inputs

Minimum required

• The initiative context and desired outcome (“what are we trying to change?”)
• Time horizon and urgency (wartime vs peacetime)
• Constraints/guardrails (quality, compliance, brand, budget, “must not worsen” metrics)
• Stakeholders and decision rights (who decides pivot/stop/scale?)
• Top unknowns/assumptions (what would change the plan?)
• Current signals (what data exists; what feels true but unproven?)