Decision moment
How should an AI use case be approved?
Use Case Approval
Problem pageAI use case approval workflow for value, data risk, effort, owner and governance readiness.

How should an AI use case be approved?
An AI use case should be approved based on value, data risk, effort, owner, review readiness and operating maturity. Otherwise the loudest ideas win.
How should an AI use case be approved?
AI Use Case Prioritization
AI Kickstart
Tirion method
The page is built as a decision surface, not as a generic article. The goal is to make scope, risk and next move visible.
Which criteria for impact, data, risk, effort and ownership apply to every idea.
Which use cases start, deepen, pause or stop.
Which governance questions must be answered before pilot, budget or build.
Scorecard
Which problem becomes measurably smaller?
Which data and decisions are involved?
Is there an owner, source and review path?
Red flags
Decision questions
Who owns the use case in the business?
Which data does the use case truly need?
What is a stop criterion before pilot?
Tirion artifacts
Each page points toward concrete material leadership can review, not abstract advice.
One page with risk, value, owner, non-goals and the next move.
A reviewable matrix for data, risk, effort, readiness and leadership control.
A 30/60/90 path with approvals, pilot boundary and accountable owners.
Example pattern
Many AI ideas compete, but budget, security and business teams evaluate them differently.
Tirion creates an approval workflow with scorecard, decision questions and stop criteria.
Only use cases with clear value, owner and defensible governance move into pilot or build.
Related pages in this cluster
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