Feed on “ai-adoption”
7 posts tagged ai-adoption. See the whole feed.
What predicts whether an AI crew actually sticks
Across 700+ practitioners, durable AI adoption tracks three tells, choom: training on their own codebase, role-specific tracks, and internal champions seeded in the crew.
Singapore Airlines: a website feature in five weeks, down from nine
Jacking GitHub Copilot across Singapore Airlines' delivery lifecycle cut a feature from nine weeks to five for the crew, with 95% of the work AI-generated and effort down 60%.
The demo-to-production gap is a crew problem, not a chrome problem
A better model does not close the demo-to-production gap, choom. The moves that win a pilot do not generalise, so architecture, guardrails, and behaviour change close it. It is a crew problem, not a chrome problem.
Adoption is a behaviour change, not a tooling drop
Tools do not change behaviour, reps do, choom. A launch plus a generic workshop spikes then fades, while role-specific reps and crew champions make adoption hold.
Why I chrome for the unremarkable grind, not the wow demo
A hundred engineers jacking into AI by default flatlines one jaw-dropping demo, choom. Production compounds on the thousandth unremarkable run, so measure throughput, not applause.
Why AI pilots flatline before they hit production
AI pilots flatline because they chrome up for a demo, not for adoption. Production needs architecture, ICE, and a crew that works different, not a fatter model.
What 700 chromed-up engineers taught me about adoption
After running 700+ crew through the training, the pattern's clear as neon, choom: adoption is a behaviour change, not a tooling drop. Role-specific reps and internal champions are what keep the rig running.