Most AI projects fail before they start. Not because the technology is wrong — but because nobody asked the right question first.
I’m Matt Sheehan. My focus is the gap at the heart of modern AI: the difference between systems that perceive the world and systems that reason causally about it. More data. Better models. Faster dashboards. And the same decisions still made by humans who can’t keep up with what the machine is showing them.
That bottleneck isn’t a data problem. It isn’t a compute problem. It’s architectural — and it shows up the same way whether you’re running insurance underwriting, supply chain operations, or crisis response.
Twenty-five years building at the intersection of physical data and decision-making taught me something most AI strategy misses: the hardest problem was never collecting the data. It was connecting observation to cause, and cause to the right decision. That discipline — grounded in the physical world, not just the written record of it — is what most AI strategy is missing.
I work with organisations to locate exactly where their AI capability is stalling — and what it actually takes to move from pattern-matching to genuine causal reasoning.
