Specialist review
Specialist review only. Use this page when the topic is concrete; the simple entry point remains the AI check.
Tirion AI Decision System
AI Decision System
A decision model for leadership teams that need to turn AI ideas into investable use cases, controlled pilots or deliberate stops.
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Framework overview
The AI Decision System separates AI ideas from investable initiatives. A use case is ready for budget or pilot only when business outcome, owner, data access, risk, evaluation, security and operations are jointly reliable.
The Decision Logic
The framework puts AI initiatives into a sequence leadership can actually govern: clarify value, limit risk, check data, define evaluation and plan operations before a tool debate takes over.
What measurable business value appears and why does this use case matter now?
Which risks, approvals and human oversight points must stand before a pilot?
Which data, metrics, owners and operating processes are needed after the first test?
Each dimension is scored from 0 to 3. A high score does not make the decision for leadership, but it shows whether the use case is investable, pilot-ready or needs to be rescoped.
Investable. The use case can move toward budget, pilot or implementation.
Pilot-ready, with gaps to close before production.
Rescope. Target, data or controls are still too unclear.
Stop or solve manually.
Readiness dimensions0-3
Business Outcome
0-3Is the desired business value measurable?
Responsibility
0-3Is there a business lead with decision authority?
Repeatability
0-3Is the task frequent or strategically important enough?
Data Access
0-3Are the needed sources allowed and reachable?
Data Quality
0-3Are freshness, completeness and gaps known?
Risk
0-3Is the use case classified by risk?
Human Oversight
0-3Are review, escalation and stop mechanisms defined?
Evaluation
0-3Are test set, metrics and negative tests available?
Security
0-3Are least privilege, logging and secrets clarified?
Operations
0-3Are owner, monitoring, feedback and incident process defined?
Hard stop criteria
- No accountable business owner.
- No legitimate or secured data access.
- R3/R4 risk without human oversight.
- External communication or mutating actions without approval process.
- No way to measure output quality.
Short checklist
- Problem described in one sentence.
- Target process and current effort known.
- Data sources, gaps and exclusions documented.
- Risk class and controls defined.
- Test set and success measurement available.
- Operating model and stop decision clarified.
Where to use this framework
Compare several AI ideas with the same criteria before teams commit budget.
Show which parts of a use case are viable for phase 1 and what stays deliberately out of scope.
Translate technical debate into value, risk, controls and the next approval point.
Executive FAQ
When is an AI use case investable?
When value, owner, data access, risk, evaluation, security and operations are at least sufficiently clarified. High potential alone is not enough.
What happens with a mid-range score?
The use case can often be piloted, but the gaps are made explicit and assigned to an owner before production.
Does the scorecard replace a workshop?
No. It creates decision structure. In AI Kickstart, it becomes a prioritized portfolio and 30/60/90 roadmap.
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Need this translated into a real decision?
Use the AI check to identify the strongest AI, cloud or approval lever before choosing a next step.