Principles
- Define a baseline period and use comparable scope.
- Measure both throughput and quality signals.
- Prefer metrics tied to delivery outcomes, not model usage counts.
Delivery leadership / AI-enabled execution
Yevgeniy Gavrilov
Enterprise delivery, modernization, and practical AI enablement for distributed teams.
AI developer productivity metrics
A measurement approach for AI-assisted development that avoids vanity metrics and makes results credible in enterprise delivery.
Core metrics
- Throughput: stories delivered and story points delivered.
- Efficiency: average hours per story and hours per story point.
- Flow proxy: time in state from Active to Done when tracked consistently.
- PR characteristics: LOC delta, files changed, and comments count.
- Quality signals: parity failures, escaped defects, rollback rate, and test flakiness.
How to baseline
- Pick a stable pre-AI window, usually two to three sprints.
- Normalize by story type such as endpoint, service, tests, or support.
- Track team composition changes including onboarding and role shifts.
Interpreting results safely
- More story points can mislead if PRs become artificially thin. Compare PR size and review comments.
- Fewer comments are not automatically better. Check defect rate and parity failures too.
- Speed gains must be paired with guardrails and validation evidence.
Search topics
- AI developer productivity
- GenAI metrics
- SDLC measurement
- throughput
- cycle time
- PR size
- code review
- quality signals