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Use Claude and Codex to generate candidate project risks before launch, then verify and prioritize those risks yourself. Give both tools the same brief, ask each to imagine the project has failed by a specific date, and collect their answers separately before comparing them. Treat every generated cause as a hypothesis—not a prediction or proof that AI-assisted premortems improve project outcomes.
What a project premortem does
A premortem is a prospective risk exercise: before work begins, a team imagines the project has already failed and lists plausible reasons. That framing can surface concerns while there is still time to change the plan. Gary Klein described the project technique in Harvard Business Review in 2007; the Agency for Healthcare Research and Quality premortem tool applies a similar approach in healthcare.
The cognitive idea behind it is prospective hindsight: explain a future event as if it had already happened. Mitchell, Russo, and Pennington defined that framing in their 1989 paper, “Back to the future: Temporal perspective in the explanation of events”. That paper examines the framing, not Claude, Codex, or AI-generated risks.
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How to use Claude and Codex for a premortem
The sequence below adapts the established premortem method for two AI tools; it is not a separately validated protocol.
- Set a shared scope. Prepare one concise project brief covering the goal, success criteria, assumptions, constraints, and a specific date by which failure is imagined. Give each tool the same material. Do not include confidential information unless your workspace and tool controls permit it.
- Generate risks independently. Ask each tool to assume the project has failed by that date and identify plausible causes. Request concrete mechanisms tied to the brief or files it can inspect. Require it to label unknowns instead of filling gaps with speculation. Save both initial lists before asking either tool to critique the other response.
- Consolidate and challenge. Deduplicate overlapping risks, identify the assumptions behind each one, and ask what evidence would strengthen or weaken it. A person familiar with the project should check important claims against the actual plan, repository, or operating context.
- Prioritize and assign. For every credible risk, record its potential impact, likelihood or uncertainty, an early warning signal, a mitigation, a named owner, and a date for deciding what to do. These are useful planning fields, not special Claude or Codex features.
- Close the loop. Choose a small number of actions to complete before launch. Revisit the list when a key assumption, dependency, or project condition changes.
A reusable prompt
Use the same prompt and brief in both tools if you want a fair comparison:
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Generated answers are material for review, not evidence that the premortem method works or that a risk will occur.
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How to compare Claude and Codex on project risks
There is no established result in the cited sources showing that one tool produces better premortems, or that using both improves risk detection or project success. Compare their outputs on the same task and judge observable qualities rather than declaring a general winner.
- Scope: Does the tool stay within the supplied project context, or drift into generic risks?
- Specificity and support: Does it describe a plausible failure mechanism and point to relevant brief details or files?
- Uncertainty: Does it distinguish evidence from assumptions and say when information is missing?
- Actionability: Do mitigations have clear signals, owners, and next steps?
- Controls and review: Can you understand the tool’s permissions and repository access, and review higher-impact actions?
- Usability: Is the output straightforward to export, compare, and revisit?
Anthropic’s Claude Code CLI reference documents options including print mode, structured output, and permission modes. OpenAI’s Codex CLI getting-started documentation describes working with a local repository, while its plan documentation covers use with ChatGPT plans. For a deployment-specific account of execution boundaries, network policies, and review of higher-risk actions, OpenAI describes its own setup in “Running Codex safely at OpenAI”. These product descriptions do not establish which tool is more accurate for premortems; available controls and labels may vary by configuration.
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Protect dissent instead of outsourcing judgment
A premortem is more than asking software what could go wrong. People who understand the project need room to add, correct, or reject risks. Klein’s account emphasizes giving knowledgeable dissenters a way to speak up; AHRQ recommends encouraging everyone to share concerns and supplies an input worksheet.
For an individual exercise, write down your own risks before reviewing AI suggestions. In a group, have participants submit initial concerns independently, then discuss the combined list. This reduces the chance that the first confident answer—human or machine—sets the boundaries of the conversation.
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What the evidence does—and does not—show
The 1989 study and Klein’s later project guidance support the prospective-hindsight framing and the value of surfacing reasons for failure in advance. They do not test Claude, Codex, or the workflow described here. The cited sources provide no measurement of whether these tools improve premortem quality, identify more risks, or make projects more likely to succeed. Use them to broaden and organize questions, then rely on project evidence and accountable human judgment to decide what matters.
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