A fireside chat for founders and product leaders running an older codebase: what actually changes once AI gets into your SDLC, and what doesn't.
Model plus harness, and why the harness ends up being most of the work.
Team execution and product alignment, side by side, not just a slide with theory.
Submit a question at registration, or drop it in live as we go. Either way, it gets worked into the conversation, not saved for a rushed Q&A block at the end.
The most common prompts we're seeing on this exact problem, and what that tells you.
12 years scaling engineering teams, from QA engineer to Head of Resource Management. Now drives AI implementation on the delivery side: getting the right people and process in place so it actually ships.
12 years consulting on delivery for large organizations, much of it legacy modernization. Focuses on the delivery and product side of AIDLC: keeping roadmap, business goals, and what actually ships in sync.
"I keep hearing the same tension from founders: everyone says ship faster with AI, but nobody wants to be the team that broke production doing it. That's exactly the debate I want on stage this time, delivery versus product, speed versus what actually holds up."
Navigating whether to modernize now or wait, while the roadmap keeps slipping.
Save my seat →Leading a team that's already feeling the AI-native pressure from competitors.
Save my seat →Trying to figure out how much faster the roadmap could realistically move.
Save my seat →Scaling AI use past isolated experiments into everyday delivery.
Save my seat →No hard pitch. It's 60 minutes of real talk. If you want to go deeper afterward, there's one clear next step at the end, that's it.
No. This session is built for founders and business leaders, not engineers. We keep it in plain language, this isn't a deep-dive technical workshop.
No problem, register anyway. We'll record the session and send you the link afterward.
