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Engineering friction compounds across your organization, costing you velocity, quality, and talent. This talk provides a data-driven framework for measuring, prioritizing, and scaling DevEx improvements that deliver measurable business impact.
Your engineers spend hours waiting on builds, navigating undocumented systems, and context-switching between 12 different tools. That’s not just frustrating—it’s expensive. Every friction point compounds across your organization, costing you velocity, quality, and your ability to retain top talent.
As AI transforms software development, this problem is about to get worse. AI-generated code increases deployment frequency and system complexity. If your baseline friction is already high, AI won’t speed you up—it will amplify every bottleneck and create new categories of problems you haven’t seen yet.
This talk presents a systematic framework for identifying and eliminating engineering friction at scale. You’ll learn how to measure what’s actually slowing your teams down, build data-driven business cases for DevEx investment, and prioritize changes that deliver measurable ROI. We’ll cover how to scale improvements from a single team to your entire engineering organization, and why the teams that fix their DevEx foundation now will outpace competitors in the AI era.
You’ll walk away with practical frameworks, measurement approaches, and stakeholder templates you can use immediately—whether you’re making the case for investment, leading an improvement initiative, or scaling best practices across teams.
Key takeaways
- A measurement framework for quantifying engineering friction and connecting it to business outcomes (velocity, quality, retention)
- Proven stakeholder templates for building data-driven business cases that secure DevEx investment
- A scalable playbook for rolling out DevEx improvements from single teams to entire engineering organizations
