Writing on “ai-adoption”
7 articles tagged ai-adoption. See all posts.
What predicts whether an AI cohort actually sticks
Across 700+ practitioners, durable AI adoption tracks three predictors: training on their own codebase, role-specific tracks, and internal champions seeded in the team.
Read articleSingapore Airlines: a website feature in five weeks, down from nine
Embedding GitHub Copilot across Singapore Airlines' delivery lifecycle cut a feature from nine weeks to five, with 95% of the work AI-generated and effort down 60%.
Read articleThe demo-to-production gap is an organisational problem
A better model does not close the demo-to-production gap. The skills that win a pilot do not generalise, so architecture, guardrails, and behaviour change close it.
Read articleAdoption is a behaviour change, not a tooling rollout
Tools do not change behaviour, practice does. A launch plus a generic workshop spikes then fades, while role-specific practice and champions make adoption durable.
Read articleWhy I optimise for the unremarkable workflow, not the wow demo
A hundred engineers using AI by default beats one jaw-dropping demo. Production compounds on the thousandth unremarkable run, so measure throughput, not wow.
Read articleWhy AI pilots stall before production
AI pilots stall because they optimise for a demo, not for adoption. Production requires architecture, guardrails, and a change in how teams work, not a better model.
Read articleWhat 700 trained engineers taught me about adoption
After training 700+ practitioners, the pattern is clear: adoption is a behaviour change, not a tooling rollout. Role-specific practice and internal champions are what make it stick.
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