# AI impact: Measure what matters | LinearB

> GitHub Copilot here, Cursor there, Claude Code somewhere else. AI across tools is adopted, but is it actually working?

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# AI impact: Measure what matters

On-Demand

![Hero_Copilot_AI_Insights_and_Analytics_2400x1256_4125e6db8a](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Hero_Copilot_AI_Insights_and_Analytics_2400x1256_4125e6db8a?_a=BAVMn6ID0)

Watch On-Demand

## GitHub Copilot here, Cursor there, Claude Code somewhere else. AI across tools is adopted, but is it actually working?

### Speakers

![ben_lloyd_pearson_04_2025_83ee0533d9](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/ben_lloyd_pearson_04_2025_83ee0533d9?_a=BAVMn6ID0)

Ben Lloyd Pearson

Director, Developer Experience

LinearB

![ofer_headshot_3806717a39](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/ofer_headshot_3806717a39?_a=BAVMn6ID0)

Ofer Affias

Senior Director of Product

LinearB

### About the workshop

The real challenge isn’t adoption; it’s understanding what happens after.

In this session, you’ll learn how to connect AI activity to commits, PRs, and delivery outcomes, and walk away with an operating model to prove and scale impact.

Answer whether your AI investments are working:

* How much of your code is AI-assisted, and where is it contributing to code, reviews, and PRs?

* Which tools are making the most impact across your delivery pipeline?

* Which users, teams, and workflows are making the most AI impact?

* Is AI improving throughput, or simply shifting work from coding to review and rework?

## Your next read

[![Cover image for The engineering productivity gap: How elite AI teams are pulling away from the rest](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_2026_AI_Benchmarks_fd0e41d857?_a=BAVMn6ID0)](https://linearb.io/resources/engineering-productivity-gap)

Workshop

[The engineering productivity gap: How elite AI teams are pulling away from the rest](https://linearb.io/resources/engineering-productivity-gap)

Watch a workshop on the new benchmark data behind the engineering productivity gap, and the measurement play that puts your team on the right side of it.

[![Cover image for From measurement to action: moving off Appfire Flow without starting over](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/webinar_appfire_flow_migration_7387e46670?_a=BAVMn6ID0)](https://linearb.io/resources/flow-migration-workshop)

Workshop

[From measurement to action: moving off Appfire Flow without starting over](https://linearb.io/resources/flow-migration-workshop)

Join a live workshop on making the move off Appfire Flow without losing your history, your team structure, or your momentum.

[![Cover image for The AI engineering productivity gap: how elite teams pull ahead in 2026](https://assets.linearb.io/image/upload/c_limit,w_2560/f_auto/q_auto/v1/Blog_2026_AI_Benchmarks_fd0e41d857?_a=BAVMn6ID0)](https://linearb.io/resources/ai-engineering-productivity-gap)

Guide

[The AI engineering productivity gap: how elite teams pull ahead in 2026](https://linearb.io/resources/ai-engineering-productivity-gap)

Elite AI teams nearly doubled their merge rate in 2026 while developers using no AI stalled. See the engineering productivity gap in LinearB's 2.7M-PR data.

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