Almost every product and engineering organisation has adopted AI tools. Very few have changed how they work because of them — and that gap is where most AI initiatives quietly fail.
Where AI adoption actually succeeds
Adoption isn’t about picking a model. It’s about the organisational changes that make AI genuinely useful: codifying the judgment your best people carry, building governance that doesn’t just block progress, and understanding why individual productivity gains don’t automatically become team-level gains. I write about the practical side of AI adoption — drawn from running it inside Wall Street English’s product and technology organisation, not from vendor demos. That means the sequencing problem (why you have to codify before you automate), the coordination cost of AI acceleration, and what governance looks like when it isn’t just a slide in a board deck. If any of this is a live problem for your organisation, get in touch.-
AI Isn’t Free Anymore
Cloud CPU spot pricing has vanished, not because of GPU training but because agents run everything on CPU. The free-AI story needs an update, not a retraction.
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Your AI Governance Policy Is Written for the Wrong Unit
Many organisations start their AI governance policy with a list of approved models. That list would not have stopped the July attack on Hugging Face, which came from people running software irresponsibly.
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The AI Acceleration Trap: When Output Outruns Absorption
Your teams can now produce more than your organisation can take in, and the numbers leadership watches reward them for it. That is the AI acceleration trap: the signal that says faster is the same one that hides the constraint. In February I wrote about the acceleration trap, borrowing the phrase from Mike Fisher’s When…