Pre-mortem
A preparation technique in which you assume the budget was rejected and work backward to the most likely causes, so you can fix them before the meeting rather than diagnose them after it. Distinct from a post-mortem, which happens after the failure. Once you have listed the possible causes, do not treat them as equally urgent; rank them by how likely each is and how much damage it would do if it happened, and spend your remaining preparation time on the top of that list, not spread evenly across every cause you can imagine.
Defined in 6 GAGE programs, which carry 16 distinct definitions of it. The wording above is taught in Certified AI Governance Professional (CAIGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
A preparation technique in which you assume the budget was rejected and work backward to the most likely causes, so you can fix them before the meeting rather than diagnose them after it. Distinct from a post-mortem, which happens after the failure. Once you have listed the possible causes, do not treat them as equally urgent; rank them by how likely each is and how much damage it would do if it happened, and spend your remaining preparation time on the top of that list, not spread evenly across every cause you can imagine.
An exercise, borrowed from decision-science practice, in which an engineer assumes a decision has already failed at some point in the future and works backward to generate the most plausible reasons why, in order to surface real weaknesses before a live review does. Used in this topic as a structured method for anticipating objections and guarding against confirmation bias.
A decision-science technique in which, before committing to a high-stakes call, a team imagines the decision has already failed a year later and works backward to name why. It surfaces doubts that a confident room otherwise suppresses, turning them into candidate failure conditions and guardrails; it is the practical antidote to the Zillow failure mode.
The general practice, applied throughout Section 3I's worked walkthrough, of deliberately imagining a failure has already happened and working backward through what each kind of reviewer would ask about it, rather than waiting for a real incident or a real examiner to ask those questions for the first time.
A decision technique in which one assumes a plan has already failed and writes the story of why, to surface the assumptions and risks that optimism hides; pairs naturally with a predictive model's blind spots.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Crafting Prompts for Business Scenarios · Practical AI Workflow Design and Prompt Engineering, Certified AI Practitioner: Workplace Foundations
- AI for Innovation: Predictive Analytics Applications · Advanced AI Literacy, Certified AI Practitioner: Workplace Foundations
- Know Your Attacker · The Capstone: Deploy and Defend, Certified Agentic AI Governance Professional (CAAGP)
- Scoping the deployment: what your organization actually needs versus what the demo showed · Shipping AI and Surviving the Incident, Certified AI Governance Professional (CAIGP)
- The hostile board: defending your AI budget to directors who interrupt · The Money: Budgets, ROI, and Risk, Certified AI Governance Professional (CAIGP)
- Building Personal Authority and Credibility Without a Technical Background · Own the AI Transformation Mandate, Certified AI Transformation Professional (CATP)
- Capstone Phase 1: The Organizational Assessment · Capstone: Your Transformation Blueprint, Certified AI Transformation Professional (CATP)
- The technique toolkit · Risk Identification, Scenarios, and Assessment, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
- What the judgment layer is and why it runs alongside the technical programs · Your Engineering Judgment Command Center, Engineering Judgment and Professional Formation
- Reasoning from first principles instead of by analogy · Engineering Thinking and Elegant Minimalism, Engineering Judgment and Professional Formation
Where it shows up at work
A definition is where Pre-mortem starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Briefs a model like a good manager: context, constraints, examples, verification, and knows when to stop delegating.
One lesson on this page teach it as a primary outcome.
Decides where AI should be used, where it should not, and which use cases create measurable value, tied to the organization's priorities.
One lesson on this page teach it as a primary outcome.
Uses generative AI tools daily, knows what a model can and cannot do, and can say where an output should not be trusted.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Literacy Leadneeds it at depth
- AI Adoption and Enablement Leadneeds it at depth
- Chief AI Officerneeds it at depth
- Chief Information Officer, technology leadership editionneeds it at depth
- Chief Technology Officer, AI engineering editionneeds working knowledge
- Operations Manager, AI-readyneeds it at depth
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Article 4; DOL AI Literacy Framework.
One interview question about it
Describe a workflow you redesigned around an AI tool. What did the prompt have to contain for the result to be reliable?
Asked of candidates for prompting and workflow design with ai. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- A computer science or software engineering degree carries it as work you can show: Prompting and tool workflows are already in your daily work.
- A business, MBA or technology management degree carries it as a claim to back up: Strategy and opportunity triage are the core of the degree.
- Recent graduate in law, policy, business or data carries it as a claim to back up: Most graduates already use the tools daily.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.