AI productivity guide for engineering leaders

Discover how to track and drive AI productivity impact across your entire engineering organization. Inside you’ll find:

AI Measurement Framework: A structured approach to connecting AI measurement with action

How to measure AI adoption & impact: Tactical advice on combining dev surveys with AI acceptance metrics to build an ROI story

5 Workflow Automations to improve DevEx: Discover proven ways to reduce developer toil across the entire SDLC

AI productivity guide for engineering leaders

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Cover graphic for the 6 bottlenecks slowing AI-driven development.

The AI Measurement Framework

We created the LinearB AI Measurement Framework to help leaders cut through vanity metrics like hours saved or lines of AI-generated code. Instead, you’ll learn how to connect AI adoption to outcomes that matter:

Throughput: Are teams shipping features faster?
Quality: Are you reducing rework, defects, and technical debt?
Adoption + Impact: Is AI both widely used and creating measurable improvements?

AI Measurement Framework: Adoption and Impact.

How to measure AI adoption & impact

Inside, you’ll find practical steps to track adoption and impact across your org using:

Quantitative signals: AI Review Coverage, AI-generated suggestions, and AI-reviewed PRs.
Qualitative insights: Developer surveys that surface friction, sentiment, and workflow fit.
MCP-driven insights: Metrics comparison, cycle time analysis, and on-demand queries.