Decision moment
What should an AI use case scoring matrix include?
Use Case Scoring Matrix
Decision assetAI Use Case Scoring Matrix
AI use case scoring matrix for impact, data readiness, governance risk, effort, owner and time-to-value before pilot or budget.
What should an AI use case scoring matrix include?
Short answer
An AI use case scoring matrix evaluates every idea by business impact, data readiness, governance risk, effort, ownership and time-to-value. The output is not an idea list. It is a decision: start, deepen, pause or stop.
Cluster
AI Use Case Prioritization
Recommended path
AI Kickstart
Tirion method
How this decision becomes workable
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
What leadership should score before action
Impact
Which measurable business problem gets smaller?
Data readiness
Are sources available, allowed and understandable enough?
Governance risk
Which privacy, security, liability or reputation questions appear?
Effort
Can the first step fit into a bounded sprint?
Owner
Who owns value, review and later operations?
Time-to-value
How quickly can a credible value signal appear?
Red flags
Signals that the page should lead to governance before build
- The scoring matrix measures value but not risk.
- Popular ideas receive special rules.
- Data readiness is checked only after budget approval.
- No stop criterion blocks political favorite projects.
Decision questions
Questions to answer before the next move
Which criteria apply equally to every use case?
Which scoring separates quick pilots from risky ideas?
Which idea looks attractive but should stop because data or ownership is weak?
Which three use cases belong in the next approval round?
Tirion artifacts
Outputs this work should create
Each page points toward concrete material leadership can review, not abstract advice.
A 1-5 view for impact, data, risk, effort, owner and time-to-value.
A completed example that makes start, deepen and stop logic visible.
A bridge from matrix to approval, pilot boundary and next owner decision.
Example pattern
A practical decision pattern
Many AI ideas are on the table, but teams evaluate value, risk and feasibility differently.
Tirion puts ideas into one scoring matrix with criteria, scoring example and stop criteria.
Only use cases with clear value, owner, data assumptions and acceptable risk move into pilot or approval.
Related pages in this cluster
AI Use Case Prioritization
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