Speaker
Abstract
Giving every engineer access to coding agents is only the beginning of adoption. One person may discover a useful way of working while teammates struggle to reproduce it, lack time to experiment, or find that it does not fit their tasks. How does a team turn individual discoveries into practices that others can understand, use, and improve?
Drawing on my experience building OpenAI Ads, I’ll explore how engineering teams can develop that shared capability. The focus is on the people and routines that support adoption: making room for experimentation, helping engineers with different levels of experience, and deciding which discoveries deserve wider use.
I’ll examine the organizational and cultural challenges of adopting AI across a team, including differences in experience, confidence, and ways of working. The discussion will focus on how teams develop shared judgment about where agents are useful and create the conditions for engineers to learn from one another.
I’ll also explore how teams sustain adoption as tools and expectations change. That means considering how knowledge spreads, how responsibilities evolve, and how leaders assess whether new ways of working are helping the team. The focus is on making experimentation contribute to lasting improvements in everyday engineering.
Attendees will leave with a framework for guiding AI adoption across an engineering team, connecting individual experimentation to shared capability and meaningful improvements in how the team works. The lessons apply across coding agents and engineering environments.
Main Takeaways:
- Understand the human side of AI adoption. Recognize how differences in experience and confidence affect the way engineers learn to work with agents.
- Develop shared team capability. Explore how individual learning becomes knowledge and practices that others can use and improve.
- Sustain adoption as tools evolve. Consider how leadership, shared responsibility, and evaluation help teams turn experimentation into lasting improvements.
Interview
Interview:
What is the focus of your work these days?
My work currently focuses on building AI-native advertising and monetization systems at OpenAI. I’m also interested in how engineering teams develop shared expertise with coding agents and make useful practices accessible beyond the individuals who discover them.
What was the motivation behind your talk?
An engineer discovering a useful AI workflow does not automatically make it a team capability. Other people need to understand when it applies, reproduce the result in their own work, and know where to turn when it breaks or stops helping.
My experience building OpenAI Ads has motivated me to think about the team practices that make adoption sustainable. I want to examine how engineers get time and support to experiment, how discoveries become reusable tools and procedures, and how responsibility for learning can be shared across the team.
My goal is to give attendees practical ways to help their teams learn together, beginning with one recurring problem and a workflow that another engineer can successfully use.
Who is your session for?
Engineering managers, technical leads, staff engineers, and practitioners helping their teams adopt coding agents.