I talk about what I've actually done — running a P&L, driving technology adoption inside resistant organizations, selling complex solutions to technical buyers, and building AI-powered applications as a non-traditional developer. No frameworks from a book. No theory. Operational experience delivered straight.
You bought the platform. Now nobody uses it. I've driven adoption programs at Microsoft (30% to 70%+ across 200+ partners) and in industrial automation environments where change moves slow. This talk covers what actually works when the technology is ready but the organization isn't.
Enterprise sales where the buyer knows more than you about their own systems. How to earn technical credibility, run discovery that surfaces real problems, build solution architectures that close deals, and navigate long sales cycles without losing momentum. From $2M+ ARR in enterprise workflow automation and $45M+ in career revenue.
What it actually looks like to own a P&L when you come from a technical background. Budgeting, reforecasting, margin discipline, hiring, client management, and making the numbers work when the team is small and the stakes are real. Lessons from managing a $2.8M industrial automation office.
I shipped 9 AI-powered web applications at dubltap.io with no formal software engineering background. Decades of industrial automation and enterprise operations taught me how to think about systems, users, and production reliability in ways that bootcamp graduates don't. This talk is for the experienced operator who wants to build.
Most organizations don't have greenfield environments. They have Siemens PLCs from 2008, Rockwell controllers running code nobody documented, and Schneider systems stitched together over decades. I've managed these environments and I build AI applications. This talk covers what's actually possible at the intersection — and what's hype.
The most valuable AI builders aren't the ones with the deepest ML expertise. They're the ones who understand operations, sales, delivery, and users — then apply AI to real problems. How a background in P&L management, enterprise sales, and industrial automation becomes a competitive advantage in building AI products.
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