Most AI conversations stay at altitude — in the space of trends, predictions, and frameworks. My work begins when implementation does.
I was in the room.
I’ve spent a decade building AI products inside enterprise organizations, navigating the gap between what the technology promised and what implementation actually required. I’ve sat with executives who were sold a vision, watched teams chase it, and seen what happens when the demo doesn’t survive contact with reality.
That experience is what I bring to every talk.
Signature Keynote
You Were Promised Everything. Here’s What It Took.
The demo worked. The business case was compelling. The consultants were confident. The budget got approved.
And then something went wrong. Or didn’t go anywhere at all.
This isn’t a story about bad technology. The technology mostly did what it was supposed to do. This is a story about what happens when the incentives around AI adoption are misaligned from the start — when showing what’s possible matters more than explaining what’s hard.
The hardest part isn’t technical. It’s organizational. The decisions made before the first model is trained, around data, governance, ownership, and what success actually means, determine whether AI delivers value or just delivers activity.
This keynote draws from a decade of enterprise AI implementation to explain what separates the organizations that translated AI into measurable value from those still waiting. It’s a clear-eyed look at what went wrong, why it was predictable, and what changes when you stop chasing the demo and start building for real.
Additional Talks
The Million Dollar Promise: Why AI Business Cases Fall Apart in Production
Enterprise AI initiatives consistently enter with inflated projections and exit with diminished returns. Not because the technology failed, but because the assumptions were never tested. This session examines how AI value propositions get built, where they break down, and what rigorous business case development actually looks like before you commit the budget.
Chasing Fool’s Gold: Three Years of AI Hype and What It Actually Cost
Nearly three years after generative AI reshaped expectations across every industry, the pattern is consistent. Impressive demos, real investment, and returns that rarely match the original promise. This talk examines the structural reasons AI keeps underdelivering and what the organizations that got it right did differently from the start.
The Gap Between the Roadmap and the Room
What the strategy deck said, what implementation revealed, and how to close the distance before it costs you. This session examines the space between AI strategy and organizational reality, covering data readiness, governance, cross-functional alignment, and the questions worth asking before the work begins.
Systems Under Stress: What Enterprise AI Reveals About Leadership
When AI integrates into real workflows, it doesn’t just change processes. It exposes them. Fragility in data, decision structures, and incentives becomes visible quickly. This session focuses on what enterprise AI implementation reveals about organizational health and how leaders can use that signal to build more durable systems rather than paper over the gaps.
Past Engagements
AI Summit New York — 2022 Homegrown AI Framework & Business Impact
PHLAI — Philadelphia Artificial Intelligence Conference — 2019 Evening keynote on AI-driven customer experience, chatbot strategy, and building AI systems at scale.
Indiana IoT Lab, Fishers IN — 2020 Presented on AI, machine learning, Smart City Technology, 5G, and Edge computing. Featured in TechPoint.
Formats & Audiences
Available for keynote, moderated conversation, executive workshop, and panel discussion.
I speak to executive leadership teams, enterprise technology and innovation conferences, and MBA and graduate programs.
Particularly well-suited for organizations that have made significant AI investments and are asking hard questions about returns. Executive teams navigating the gap between AI strategy and implementation reality. Conferences looking for a perspective grounded in the work, not the hype.
About David
David Monnerat is a product leader who has spent a decade building AI systems inside enterprise organizations. He writes and speaks about what artificial intelligence actually changes inside companies, where the promises hold up, where they don’t, and what it takes to build something that lasts beyond the demo.
His perspective comes from inside real implementations, from the room where the hard decisions actually get made.
He also writes about caregiving and complexity at epilepsydad.com.
Let’s Talk
For speaking inquiries, reach out directly.