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World Congress 2026 Europe - Virtual Stage • Jul 1, 2026 • Session details

We Rolled Out GitHub Copilot… How Do We Prove It Helps?

Liuba Gonta , Yuliya Khadasevich

Stop guessing your AI return on investment. Learn how to connect GitHub Copilot telemetry to DORA and SPACE metrics to prove actual engineering value.

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#1 about 6 min

Understanding the AI coding assistant measurement challenge

Because enablement alone does not guarantee returns, organizations must identify if AI tools actually drive value.

#2 about 6 min

Navigating the four stages of AI adoption

Overcoming inconsistent tool usage requires evaluating true AI maturity rather than just tracking active seats.

#3 about 3 min

Leveraging existing frameworks for AI productivity measurement

Instead of inventing new metrics, teams can adapt established productivity frameworks to evaluate AI assistance.

#4 about 4 min

Evaluating AI delivery velocity and stability with DORA

Balancing deployment speed with reliability ensures AI-generated code does not accidentally accelerate shipping bugs.

#5 about 3 min

Measuring developer satisfaction and morale with SPACE

Standard performance metrics fail to capture burnout, making short surveys essential for tracking developer morale.

#6 about 3 min

Tracking cognitive load and deep focus using DevEx

Because AI can overload reviewers with generated code, tracking cognitive load provides a realistic view of daily productivity.

#7 about 5 min

Balancing speed and quality with the DX Core framework

Because improving speed often risks software quality, balanced telemetry systems prevent negative impacts on the developer experience.

#8 about 7 min

Navigating GitHub Copilot telemetry and usage dashboards

Identifying internal champions who can drive adoption requires analyzing built-in telemetry dashboards and exported usage data.

#9 about 3 min

Mapping tool telemetry to productivity frameworks and baselines

Connecting generalized AI assistance to actual performance gains requires establishing and correlating strict baseline metrics.

#10 about 6 min

Running controlled experiments to prove actual AI impact

Relying on anecdotal evidence obscures true causation, making controlled hypothesis testing essential for validating AI impact.

#11 about 2 min

Summarizing essential takeaways for measuring AI value

Sustaining long-term gains relies on continually verifying outcomes against controlled baselines instead of just implementing tools.

Matching moments

5:29 min

Driving organizational AI adoption through holistic developer joy

Alexander Birke Alexander Birke +3 · WWC 2025

1:23 min

Measuring developer productivity, efficiency metrics, and team happiness

Neel Sundaresan Neel Sundaresan +1 · WWC Europe 2026

3:16 min

Driving developer productivity with AI in automotive tech

Mike Butcher Mike Butcher +3 · WWC 2024

2:11 min

Understanding GitHub Copilot and core developer benefits

lgonta lgonta +1 · WWC 2024

4:12 min

Measuring generative AI impact on team productivity

Justin Reock Justin Reock · WWC Europe 2026

1:40 min

Adoption of integrated AI assistants in developer workflows

Julia Wilson Julia Wilson +1 · WWC 2025

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