AI STRATEGY
Establish AI Quality Standards
Prioritize What Matters with a Scoring System
Not all metrics matter equally. Weighted scorecards let you focus on the quality dimensions most important to your business goals, producing a reliable overall performance score.
Why it's Important
Ensures efforts go to the most impactful areas
Balances tradeoffs between conflicting dimensions (e.g., creativity vs. accuracy)
Enables benchmarking and model comparison
Simplifies executive reporting
Supports responsible experimentation by measuring risk
How to Implement
Assign a weight (e.g., % out of 100) to each quality dimension
Use product objectives to prioritize (e.g., fairness = 40% in EdTech)
Create a scoring template
Add example outputs with overall quality calculations
Calibrate scoring via multiple reviewers
Pilot test across diverse user flows
Version your weights and document rationale
Available Workshops
Priority Ranking Workshop (team votes on importance)
Scoring Simulation Lab
Risk Impact Matrix (Quality vs. Impact)
Leadership Alignment Session
Quality KPI Mapping Exercise
Internal Scorecard Bake-off
Deliverables
Weighted scoring matrix
Scoring rubric and calculator template
Internal calibration documentation
Sample scored outputs for training
Version history log
How to Measure
Reviewer consistency across scoring sessions
Stakeholder alignment score (e.g., pre/post voting delta)
Time-to-score per output
Number of disagreements requiring arbitration
Performance delta between AI model versions
Scorecard coverage across scenarios
Uptake of scorecard in product reviews
Pro Tips
Automate calculations with conditional logic in your scorecard
Use traffic light visuals for quick insights
Include a “confidence” field per score
Let reviewers flag “ambiguous” cases for discussion
Use scores as part of sprint retrospectives
Get It Right
Keep weights simple at first (3–5 categories)
Revisit weights quarterly
Align scoring system with product strategy docs
Train at least 2 reviewers per team
Use outputs that reflect high-risk and high-volume cases
Don't Make These Mistakes
Overcomplicating scorecards with too many dimensions
Ignoring reviewer fatigue or bias
Skipping cross-functional input on weights
Forgetting to version or document scoring criteria
Assuming weights are “one-size-fits-all” across use cases