Perspectives on AI adoption, technology leadership, and bridging the gap between what machines can do and what organizations are ready to use.
The conversation about AI is backwards. Everyone's obsessing over models, parameters, and benchmarks. But the real crisis isn't technological — it's human. The gap between what AI can do and what organizations are ready to adopt grows wider every quarter.
Read more →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. Here's what's real and what's hype at the intersection.
Read more →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.
Read more →What I've learned building production AI systems with enterprise security frameworks, human-in-the-loop guardrails, audit trails, and kill switches. Not demos — systems that are running today.
Read more →The cost of falling behind on AI isn't linear — it compounds. Customer expectations erode, market share shrinks, margins diminish. The cascade is predictable. The timing is not.
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