Measuring AI impact, assessing readiness, and new data trends

In this episode of Engineering Enablement, Jesse Adametz joins Abi Noda, this time to host. Together, they explore how AI is showing up across the SDLC, not just in code generation, and how it is shifting bottlenecks across the development process. They unpack what “AI readiness” actually means in practice, and why it often comes down to developer experience fundamentals like documentation, environments, and feedback loops.They also discuss why enablement matters more than tool choice, how teams are thinking about measuring ROI, and what changes as background agents become more common. Finally, they explore how the role of the engineer may evolve, the open questions teams are still grappling with, and the challenges of non-engineers contributing to codebases.Where to find Jesse Adametz: • LinkedIn: https://www.linkedin.com/in/jesseadametz • X: https://x.com/jesseadametz • Website: https://www.jesseadametz.com/Where to find Abi Noda:• LinkedIn: https://www.linkedin.com/in/abinoda In this episode, we cover:(00:00) Intro(02:12) Where AI is showing up across the SDLC(05:53) AI readiness and its link to developer experience(08:23) Why enablement, education, and experimentation matter more than tool choice(13:05) The case for a dedicated enablement team(14:50) Measuring AI ROI: challenges and tradeoffs(19:46) Background agents and token spend(24:12) Measuring agent output with PR throughput(26:58) How the engineer role might change(31:01) Specs and documentation in the age of AI(33:11) Non-engineers writing code(35:30) What’s changing in the SDLC and open questionsReferenced:• Measuring AI code assistants and agents• Lessons from Twilio’s multi-year platform consolidation• The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win• How Claude remembers your project - Claude Code Docs• specIsJustCode : r/ProgrammerHumor