Measurement · Evolved from OKRs
OKRs with Leading Model Metrics
Objectives and key results paired with both business outcomes and model performance indicators — eval scores, hallucination rate, deflection quality, human-override rate — measured alongside adoption and revenue.
Executive Guidance
How to hold this framework as a leader
OKRs remain the most widely understood mechanism for cross-org alignment on outcomes. The failure mode is not the framework but the drift toward output-shaped key results. In AI-affecting work, that drift is dangerous because business KRs move slowly while model behavior can regress overnight.
Every objective that touches an AI capability should carry at least one leading model metric alongside its lagging business metric. This is not a technical hygiene point; it is how leaders detect customer harm before it reaches revenue.
When to Apply
- Quarterly planning where AI features materially affect the outcome
- Aligning cross-functional teams on both business and model quality
- Any AI feature where user harm is possible from silent regression
AI-Era Notes
Every AI-affecting objective should carry at least one leading model metric alongside the lagging business metric. Regression in model metrics is a leading indicator of business regression.
Key Trade-off
Business outcomes without model metrics create silent regression risk; too many metrics dilute focus.
Operating Sequence
The order in which to install the framework
- 01Draft objectives from strategic outcomes, not team capacity.
- 02For each objective, define a small set of outcome-shaped key results.
- 03For AI-affecting objectives, add at least one leading model metric.
- 04Review the KRs monthly with the team responsible for movement.
- 05Reset only at end-of-cycle unless a leading metric signals regression.
Key Artifacts
The documents this framework produces
Objective Statement
Ambition, tied to a strategic outcome.
Owner: Product Leader
Key Result Set
3-5 measurable outcomes per objective.
Owner: Team
Model Metric Ledger
Leading model metrics tracked alongside business KRs.
Owner: Product + AI/ML
Operating Checklist
What "good" looks like when installed
Objective Quality
- Every objective ties to a stated strategic outcome.
- Objectives are inspirational and outcome-shaped, not activity lists.
Key Results
- Key results measure customer or business outcomes, not delivery.
- AI-affecting objectives carry at least one leading model metric.
- Each KR has a named owner and a monthly review point.
Common Antipatterns
- Treating eval scores as vanity metrics divorced from outcomes
- OKRs that measure only shipping activity, not customer or model health
Boardroom Questions
- Which of our objectives, if fully achieved, would meaningfully change the business?
- For our AI-affecting objectives, what model metric would signal regression before revenue does?
- Where are we measuring activity that should be measured as outcome?