From AI Hype to Practical Engineering

Getting useful results from AI requires engineering judgment about the task, the available context, and how much responsibility to give the tool. Drawing on my experience in enterprise architecture, platform modernization, and organizational AI enablement, I'll discuss how to move from everyday AI assistance toward more structured and agentic engineering workflows. We'll explore how code, documentation, architecture, and system relationships shape the results, using examples from development, quality engineering, and interim technical incident analysis. The session will also examine how engineers at different experience levels can use AI effectively while continuing to develop their own understanding and judgment. Throughout, we'll keep the discussion grounded in practical value, quality, cost, and human accountability. We'd love to hear about your interests and experience! Please take a moment to complete this brief survey so we can tailor the session to you: https://forms.gle/naUVrnbkajBRebbA9