Kill criteria
Pre-agreed, objective conditions under which a project is stopped regardless of political momentum (for example, thesis invalidated, timeline past three times the estimate, budget past twice the estimate without progress). The half of pipeline management that organizations find hardest.
Defined in 4 GAGE programs, which carry 11 distinct definitions of it. The wording above is taught in Certified AI Transformation Professional (CATP).
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.
Pre-agreed, objective conditions under which a project is stopped regardless of political momentum (for example, thesis invalidated, timeline past three times the estimate, budget past twice the estimate without progress). The half of pipeline management that organizations find hardest.
Pre-agreed, numeric conditions under which a project is paused or shut down, set in calm conditions rather than decided in a crisis. A stop rule written in advance is cheaper and steadier than one forced by a public failure.
The predefined, measurable conditions, decided before deployment, that trigger a move between offline states. A kill criterion written during an active incident is a pressured guess, not a control.
The specific, measurable evidence, set at the moment of funding, that would make an executive stop an AI initiative; what allows a portfolio to feed winners by stopping losers.
Pre-approved, objective conditions under which a pilot or the whole program should be stopped. Because they are signed off in advance, exercising them reads as discipline, not personal failure. Their absence produces the "pilot zoo."
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Strategic AI Decision-Making Frameworks · Bonus: AI Leadership Accelerator, Certified AI Practitioner: Workplace Foundations
- The Off Switch · Bounding by Design, Certified Agentic AI Governance Professional (CAAGP)
- The investment memo: one page that survives a CFO · The Money: Budgets, ROI, and Risk, Certified AI Governance Professional (CAIGP)
- The Operating Charter That Protects the Role · Own the AI Transformation Mandate, Certified AI Transformation Professional (CATP)
- AI Market Disruption and Competitive Intelligence · AI Literacy for Decision Makers, Certified AI Transformation Professional (CATP)
- Budget Strategy and Funding Models · The Economics and the Business Case, Certified AI Transformation Professional (CATP)
- The Governance Operating Model, Speed and Control · Governance and Responsible AI, Certified AI Transformation Professional (CATP)
- Program Management and Execution Discipline · Implementation from Pilot to Scale, Certified AI Transformation Professional (CATP)
- Innovation Management and R&D Strategy · Measure, Sustain and Evolve, Certified AI Transformation Professional (CATP)
- Capstone Phase 4: The Blueprint and the Mandate · Capstone: Your Transformation Blueprint, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where Kill criteria 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.
Builds an honest value model: baseline, measured change, cost, risk, and the projects that should be stopped.
4 lessons 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.
3 lessons on this page teach it as a primary outcome.
Designs decision rights, committees, intake, approval tiers and escalation so routine uses move and consequential uses get reviewed.
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.
- Chief AI Officerneeds it at depth
- Chief Information Officer, technology leadership editionneeds it at depth
- AI Adoption and Enablement Leadneeds working knowledge
- AI Program Managerneeds working knowledge · 11 open postings this week
- Operations Manager, AI-readyneeds working knowledge
- Chief Technology Officer, AI engineering editionneeds working knowledge
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: NIST AI RMF GOVERN; ISO/IEC 42001 Clause 5.
One interview question about it
How would you measure the return on an AI deployment honestly, including the costs people prefer to leave out?
Asked of candidates for roi and value measurement for 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 business, MBA or technology management degree carries it as a claim to back up: Value and return measurement are a course you passed.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.