Think Premortem

Premortem

A premortem stress-tests a plan by assuming it has already failed and reasoning backward to explain why, then converting each cause into a mitigation, a tripwire, and a kill criterion. The shift from "what might go wrong?" to "it went wrong, why?" is what does the work: it licenses dissent, surfaces more and more specific causes than ordinary risk review, and turns vague worry into pre-committed action while you can still change course. The output is a risk register, not a discussion.

When to Use

• Before a launch, hire, investment, migration, vendor selection, or any consequential, hard-to-reverse commitment.
• When a plan has optimistic momentum and you suspect concerns are going unspoken.
• When you want risks expressed as observable signals and pre-decided responses, not a feeling of caution.
• Often after options have been compared and one has been chosen, as the last gate before committing.

When NOT to Use

• After the outcome is known. That is a postmortem, a different tool.
• For trivial or fully reversible (two-way-door) decisions. The ceremony is not worth it.
• To generate options or to choose among them. Use an ideation skill or a decision-option review; premortem is a risk tool.
• As a ritual to bless a decision already made. If the mitigations will not be acted on, skip it; a premortem nobody acts on is theater.

Instructions

When asked to run a premortem, follow these steps:

1. Frame the decision and the horizon. State the plan and intended outcome in one or two sentences, and pick a concrete time horizon (for example, "six months after launch"). If the decision is trivial or already irreversible, say so and stop.
2. Declare the failure vividly. Assert it in the definite past: "It is [horizon]. This plan has failed badly." Make the failure concrete and specific, not "it underperformed." Write it into the template's "The failure, declared" section as a past-tense scene of observed fact - it is part of the artifact, and every cause in the register must explain it in the past tense.
3. Generate causes broadly, before judging. List the plausible reasons the failure happened. Aim for breadth and specificity; include uncomfortable, political, and second-order causes, not just technical ones. Do not filter yet.
4. Cluster and rank. Group related causes and rank them by likelihood and impact (High/Medium/Low each). Keep the vital few; do not pad.
5. Convert each top cause into action. For each high-priority cause, define a leading signal / tripwire (the early sign it is happening), a mitigation (what reduces the risk now), an owner, and a kill criterion (the pre-decided condition under which you stop or change course). This conversion step is mandatory; a list of risks without it is not a premortem.
6. Emit the risk register and a short summary. Produce the artifact in references/TEMPLATE.md: the declared failure in the definite past, then a one-paragraph "top risks and what we will do" summary above a ranked register table.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the filled risk register plus its summary, not a prose essay.

Quality Checklist

Before finalizing, verify:

• The failure was declared in the definite past, with a concrete horizon.
• Causes go beyond the obvious technical ones (include people, process, second-order, and external causes).
• Every high-priority cause has a tripwire, a mitigation, an owner, and a kill criterion (the conversion step is done).
• Risks are ranked, not an undifferentiated list.
• The output is the risk register artifact, not prose.
• No overclaiming: the skill does not promise a better outcome, only better-surfaced and better-handled risk (see evidence/dossier.md).

Evidence

Tier S/M (contested). Prospective hindsight reliably increases the number and specificity of causes surfaced and reduces overconfidence in a plan (Mitchell, Russo & Pennington 1989; Veinott, Klein & Wiggins 2010). It does not have strong evidence of improving final outcomes, and the often-quoted "30%" figure measures the number of reasons generated, not decision quality. The evidence is transferred from human studies and has not been validated for AI-augmented use. Full grading, sources, and caveats: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed premortem on a real decision.