Writing on “enablement”
8 articles tagged enablement. See all posts.
Evals for delivery teams: trusting AI output without re-checking every line
Trustworthy AI output comes from evals built into the real pipeline, not a benchmark: evaluate what matters, gate at the right point, and keep review habits that scale.
Make yourself unnecessary: what good advisory leaves behind
Good advisory leaves a team stronger, not dependent. Capability and judgement should stay with the team, through co-delivery, explicit reasoning, and grown champions.
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.
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 gives everyone half-relevant material. Role-specific tracks outperform it.
Adoption 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.
Why 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.
Why 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.
What 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.