Feed on “enablement”
8 posts tagged enablement. See the whole feed.
Evals for delivery crews: trusting AI output without re-scanning every line
Trustworthy AI output comes from evals wired into the real pipeline like ICE, not a benchmark: eval what matters, gate at the right spot, and keep review habits that scale for the whole crew.
Make yourself unnecessary: what good advisory leaves behind
Preem advisory leaves a crew stronger, not hooked on you like a bad ripperdoc. Capability and judgement should stay chipped into the crew, through co-delivery, explicit reasoning, and grown champions.
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.
AI training needs a track per role, not one shared workshop
Engineers, leads, POs, QA, designers, and managers use AI for different work, so one shared workshop hands the whole crew half-relevant material. Role-specific tracks flatline it, choom.
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.