Binding constraint
The main reason an AI idea was rejected at a given time, recorded so the decision can be reopened only when that specific constraint actually changes.
A binding constraint is the single most important reason an AI opportunity did not proceed at the time of the decision. It might be model quality, cost, latency, data access, regulation, integration effort, trust or organizational readiness.
Recording it turns a bare "no" into a conditional one. A backlog without rejection reasons loses information: when a new model arrives, the team either rescores everything or reopens nothing. With the constraint on file, the question becomes narrower and cheaper: which decisions that were correctly rejected six months ago are now wrong for a specific, identifiable reason?
How it is used
Each entry pairs the constraint with what it means now and the condition under which to reopen. A useful vocabulary distinguishes constraints that move with the technology from those that do not. Technology progress can unlock a latency or accuracy constraint; it will not by itself change a legal or ownership one.
Binding constraints are the key field of a rejection ledger, and they make reviewing past AI pilot decisions systematic rather than anecdotal.
Read more in Keep the AI ideas you rejected.
Related terms
Rejection ledger
A record kept beside the active AI portfolio of serious ideas that did not proceed, with the binding constraint, the evidence date and what would have to change to reopen them.
AI pilot
A limited trial of an AI use case to test whether it works in practice. A pilot proves one team can succeed; capability starts when others can reproduce the result.
Used in these essays
The wrong questions about AI right now
Many of the questions that helped us orient ourselves around generative AI are now too blunt to be useful. The harder work is no longer asking what AI is in the abstract, but specifying where it works, where it fails, what authority it should have, and what the whole system costs.
Keep the AI ideas you rejected
A new model release should not restart your AI roadmap. It should reopen only the ideas that were rejected for a constraint the release actually changed.