Use Claude and Codex to generate candidate project risks before launch, then have people check the evidence and decide what to do. Give both tools the same brief and failure date, collect their first-pass lists separately, and treat every output as a hypothesis—not a prediction. There is no published evidence here that the two tools together improve risk detection or project success.
What is a project premortem?
A premortem is a prospective risk exercise: before work begins, a team imagines the project has already failed and identifies plausible reasons. That framing can make it easier to raise concerns while there is still time to change the plan. Gary Klein described the technique in Harvard Business Review in 2007; the Agency for Healthcare Research and Quality also offers a premortem tool and worksheet for healthcare teams.
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The underlying idea, called prospective hindsight, is to explain a future event as if it had already happened. Mitchell, Russo, and Pennington examined this framing in a 1989 paper; it was not a study of AI tools. Klein’s HBR account attributes a 30% increase in people’s ability to identify reasons for future outcomes to that earlier research. This is not a promise that any given premortem will find 30% more risks, nor evidence of a benefit from using Claude or Codex.
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The sequence below adapts the documented premortem method to two AI tools. It is a practical workflow, not a validated Claude-and-Codex protocol.
#1 Best Overall
1. Give both tools the same project brief
Include the goal, success criteria, constraints, key assumptions, and a specific date by which you are imagining failure. Keep the brief concise enough to review. Do not include confidential information unless your organization permits sharing it under the relevant workspace and tool controls.
2. Ask each tool for an independent failure analysis
Run the same prompt with each tool before asking either to critique the other’s output. Separate first-pass generation makes it less likely that one list will anchor the other. For a solo exercise, write your own concerns before reading the AI responses; for a team, let people submit concerns independently before group discussion.
For a project with a repository, specify which files each tool may inspect. Ask it to connect claims to details in the brief or files, and to label assumptions and unknowns rather than inventing evidence. An example prompt is:
Imagine this project has failed by [date]. Using only the project brief and files you can inspect, list plausible causes of failure. For each cause, identify the supporting evidence or assumption, what remains unknown, an early warning signal, and one mitigation. Do not treat a possibility as a fact. Separate risks grounded in the supplied material from general possibilities. Finish with the three risks a human reviewer should investigate first and explain why.
Rank #3
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A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Seventh Edition and The Standard for Project Management (ENGLISH)
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- A Guide to the Project Management Body of Knowledge (PMBOK Guide) – Seventh Edition and The Standard for Project Management (ENGLISH)
3. Consolidate, challenge, and verify
Combine duplicate risks, but preserve genuinely different mechanisms. For each candidate, ask what evidence would confirm it or make it less likely. Check high-impact claims against the actual project context, and invite knowledgeable people to add, correct, or reject them. The AHRQ worksheet is useful for collecting concerns; Klein’s account emphasizes making space for dissent.
A generated risk is a hypothesis, not a forecast. If a tool gives a generic concern without project-specific support, label it accordingly rather than presenting it as a discovered fact.
Rank #4
- Harvard Business Review Project Management Handbook: How to Launch, Lead, and Sponsor Successful Projects
- Harvard Business Review Press
- BLANK BOOK
4. Turn credible risks into actions
For each risk the team accepts as worth managing, record its impact, likelihood or uncertainty, an early warning signal, a mitigation, an owner, and a decision date. These are practical planning fields, not a prescribed Claude or Codex feature. Choose a manageable set of actions to complete before launch, and revisit the list when important assumptions or project conditions change.
The Tool Desk
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Use the same brief, prompt, and review criteria rather than declaring a universal winner. Compare observable behavior on your project:
- Scope: Does the tool stay within the material it was asked to use?
- Specificity: Are the risks tied to this project, or could they describe almost any project?
- Evidence and uncertainty: Does it point to relevant details and distinguish evidence, assumptions, and unknowns?
- Actionability: Do mitigations include a useful signal, owner, and next step?
- Controls: Are repository access, permissions, and review of higher-impact actions appropriate for your work?
- Usability: Can the results be exported, compared, and revisited?
Product documentation describes different capabilities and controls, not premortem quality. Anthropic’s Claude Code CLI reference covers print mode, structured output, and permission modes. OpenAI’s Codex CLI getting-started guide and Codex plan documentation describe working with a local repository and user-selected controls. OpenAI’s account of running Codex safely at OpenAI describes its own bounded execution, network policies, and review of higher-risk actions; that account should not be read as a guarantee of identical behavior in every Codex setup. None of these sources establishes which tool produces better premortems.
What the exercise can—and cannot—tell you
A premortem helps surface plausible failure paths while a plan can still change. Claude and Codex can help draft and organize candidate risks, but their output does not establish that a risk will happen or that a proposed mitigation will work. The method’s prospective-hindsight framing has published research behind it; the effectiveness of this particular two-tool workflow has not been measured in the cited sources.
If you want a longer treatment of Klein’s method, the HBR Guide to Project Management chapter preview includes his chapter, “Performing a Project Premortem.”
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