A decision lens for adopting AI on regulated teams: sort tasks by what being wrong costs, then act differently in each zone. The finale of Still Accountable.
AI in regulated engineering makes code faster, but speed was never the constraint. Correctness, trust, and sign-off were. Don't optimise what wasn't slow.
A note for solo builders on isolation, calibration, and why the feeling of mediocrity often says more about silence than ability.
AI explainability for regulated teams: a model that can't explain why it produced an answer is a liability the moment a regulator asks you to justify the call.
AI produces senior-looking code and content with junior reliability, and never learns from mentoring. The review burden is the cost regulated teams underprice.
AI and data governance for Australian regulated teams: residency, data sovereignty, and the Privacy Act duties that decide where your data can go.
When an AI system gets it wrong, your team still answers for it. How regulated teams design for accountability that can't be delegated to the model.
An impressive AI demo and a system you can ship under audit are different projects. The gap between them is the work. First in a series for regulated teams.