Abstract
AI tools are spreading rapidly across engineering teams, yet many organizations struggle to answer a basic question: is AI actually improving our engineering outcomes?
Most engineering organizations today are seeing higher adoption, but struggling to translate that usage into consistent improvements in productivity, quality, or delivery. Emerging research suggests the difference isn’t the tools themselves, but the capabilities organizations build around them. Without a clear framework for how AI adoption evolves, teams often invest heavily in tools while missing the organizational capabilities required to make them effective.
In this talk, we introduce a research-driven maturity model for AI adoption in software engineering, synthesizing the latest research on AI in engineering. The model explains the five stages organizations move through as AI becomes embedded in engineering workflows—and why many teams see productivity gains without corresponding improvements in delivery outcomes.
Attendees will leave with a clear model for diagnosing their organization’s AI maturity and identifying what must come next—before automation outpaces the systems needed to support it.
Speaker
Lizzie Matusov
Co-Founder & CEO @Quotient, Co-Author of "Research-Driven Engineering Leadership", Official Member Forbes Technology Council, Previously @Red Hat and @Invitae
Lizzie Matusov is the co-founder and CEO of Quotient, a platform that helps engineering leaders improve engineering outcomes in the age of AI. Quotient analyzes signals from developer workflows to identify and resolve bottlenecks affecting productivity, developer experience, and delivery.
Lizzie also co-authors Research-Driven Engineering Leadership, a newsletter that translates academic research into practical insights for engineering leaders. Before founding Quotient, she worked in engineering roles at Red Hat and Invitae. She holds an MS in Engineering Sciences and an MBA from Harvard and lives in SanFrancisco.
