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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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?
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.
5 workflow automations to improve DevEx
Beyond measurement, the guide shows you the fastest path to ROI with 5 AI-powered automations that actually reduce dev toil:
- Code Experts
- AI Code Reviews
- AI Iteration Summaries
- AI-generated PR Descriptions
- Estimated Review Time Labels
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More resources
Guide
Migrating from Appfire Flow: a practical guide for engineering teams
Flow will cease operations in 2027. If your team built workflows, dashboards, and quarterly reviews around Flow, you have a real problem on a fixed deadline.
Workshop
Life beyond tokenmaxxing: AI efficiency for the long term
Watch a 45-minute session on how to measure AI's real impact across the SDLC and win the executive conversation about engineering efficiency.
Guide
Measuring Efficiency in the AI-Driven SDLC
Measuring efficiency in the AI-driven SDLC means tracking whether your whole software delivery system got faster and more reliable, not just whether developers...