AI is everywhere in DevOps—on roadmaps, in vendor demos, and in executive conversations.

But how much of that promise is actually showing up inside enterprise delivery pipelines? In this exclusive Techstrong PulseMeter webinar, we move beyond vendor narratives to measure where organizations actually stand. Based on a poll of the Techstrong community, we benchmark AI readiness across enterprise CI/CD environments—from legacy Jenkins-based toolchains to modern, managed platforms—and identify where reality is with respect to expectations.

Join Guy Currier, Analyst at The Futurum Group, and Loreli Cadapan at CloudBees for a conversation that highlights where AI is delivering real value, where adoption is stalling, and what it takes to move forward. You’ll leave with a clear understanding of how your pipeline compares to peers, with a grounded view of what AI adoption actually looks like in practice.

Key Takeaways:

1. More Code ≠ More Productivity

   AI is increasing code output, but not overall efficiency. Gains in generation speed are being offset by downstream friction—debugging, test failures, and CI reruns—resulting in flat or even declining net productivity.

2. Hidden Costs Are Becoming the Dominant Constraint

   The biggest impact of AI coding isn’t just speed—it’s cost and complexity. CI infrastructure strain, rising token consumption, and lack of cost visibility are introducing a new layer of operational overhead that most teams are not equipped to manage.

3. Organizations Are Not Aligned on the Problem

   Developers, managers, and executives are experiencing AI-driven development differently. While leadership focuses on velocity and investment, developers are dealing with trust issues, rework, and self-imposed usage limits—creating a growing gap in perception, ownership, and governance.