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.-
The Living Standard
The most dangerous state in codification as a practice is not absence. It is the standard that was accurate six months ago and hasn’t been touched since. Teams that have done the work of codification (written down their criteria, extracted judgment from the experts who carry it, given their AI something real to check itself…
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Judgment at Scale Is a Codification Problem
The most common codification mistake isn’t refusing to write things down. It is writing down the wrong thing. Teams ask their experienced people to document how they make decisions. Those people describe a process: the inputs they look for, the steps they follow, the signals they check. The documentation looks thorough. New people read it.…
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The Codification Advantage
Your competitors are using the same models you are. Probably the same tools too. If you are waiting for a better AI to give you an edge, you are waiting for something that will arrive for everyone simultaneously. The organisations that are pulling ahead with AI are not doing it because they chose a different…