AI STRATEGY
Create Offline Datasets for Quality Evaluation
Know Where You Stand in the Market
Benchmarking your AI against publicly available models provides external validation of quality. It also highlights areas where your model is leading—or lagging—versus the competition.
Why it's Important
Enables relative performance evaluation
Helps justify model updates or retraining efforts
Builds investor and stakeholder confidence
Highlights unique model advantages
Encourages best practice adoption from peers
How to Implement
Run evaluations on external models and your own
Normalize scores for fair comparison
Document how your product context differs from benchmark assumptions
Share comparative reports with product, sales, and leadership
Available Workshops
Competitive Output Comparison Lab
What Are We Better At? Roundtable
Investor Readiness Report Sprint
Performance Gap Analysis Jam
Deliverables
Model benchmark report
Comparative scorecard (you vs. GPT vs. Claude, etc.)
Market positioning slide for stakeholders
Risk caveats and context notes
Public benchmark test script
How to Measure
Model scores on each scorecard dimension
Gaps vs. top-performing public models
Internal improvement delta from last cycle
Team alignment on performance goals
External validation use in pitch decks or blogs
% of tasks with competitive parity or advantage
Pro Tips
Build benchmark scenarios into OKRs
Revisit results every major release
Create internal leaderboards for friendly competition
Share standout results publicly (when safe and accurate)
Get It Right
Use internal Scorecard to evaluate models
Don’t chase external benchmarks at the expense of UX
Use external benchmarks as input, not the sole metric
Be transparent about gaps and plans to improve
Don't Make These Mistakes
Cherry-picking evaluations that make you look good
Ignoring benchmarks outside your comfort zone
Over-promising based on narrow success cases
Using irrelevant academic tasks to prove user value
Keeping evaluation results private from decision-makers