I have led AI transformation end to end: in production, under a P&L mandate. I run every project personally, from the first day to the last, and when a build needs more capacity I bring it in from a trusted network of specialists.
I started as a developer, moved into project management, and then spent twenty years in solutions engineering, learning how companies actually buy, deploy, and live with technology. Global teams, presales methodologies, the full lifecycle of selling and delivering enterprise software across EMEA and LATAM, with clients in finance, legal, life sciences and retail. Building a global solutions-engineering function from zero, I nearly doubled win rates on complex enterprise deals.
Since 2022, AI has been at the heart of that work. Most recently, as Director of AI Transformation at a PE-backed software company, I rebuilt go-to-market and back-office operations around AI in production, cut a core quote-to-cash process from days to hours, and led the team through the part everyone underestimates: adoption. What works, what stalls, and why. The lens I bring is an investor’s: a cost-out programme books a one-time saving, while AI-native capability removes bottlenecks and compounds. I build the second.
The largest AI providers now embed engineers to do exactly this work, but only at the enterprise tier and locked to their own stack. Worklabs is the independent, platform-agnostic version, for companies standing up AI leadership for the first time.
What I learned in that seat is the foundation of Worklabs.
The skills that get you to a working demo are not the skills that get you to a system the team adopts. The distance between those two things is where most AI budgets get stuck.
Adoption metrics matter more than F1 scores. Once you’ve had to explain why a six-figure investment is sitting idle, you stop treating adoption as a phase 2 problem.
Deploying production systems at scale teaches you things that strategy alone can’t. You learn where the real friction lives, which edge cases actually matter, and why the last mile of adoption is the hardest part.
It’s the only way to ship fast without becoming the next compliance headline. Regulated, investor-backed environments teach you this. Move quickly through the gates, not around them.
Not the model. Not the infrastructure. The handoff between “working pilot” and “team adopts the new workflow as the only workflow.” Worklabs stays through that gap. It’s where the ROI lives.
Most consultant bio pages list only strengths. Here are the honest limits.
A 30-minute working call. You leave with a read on where AI would pay back first, whether or not we work together. I talk about your actual situation. If I can help, I’ll scope it. If I can’t, I’ll say so and point you at someone who can.
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