Insights
Scaling AI: cost, risk and value
A series for boards and operators who have rolled out chat assistants and now need AI that is cheaper to run, safer to govern and easier to measure. It starts with the case for a Jev-first strategy.
A Jev-first strategy: start with decisions, not chat
Part 1. Most organisations started AI with chat. Scaling it needs a different first question: which small decisions do we make thousands of times, and what would it take to make them on every item, with a record?
AI cost is the whole path, not the tokens
Part 2. Per-token prices make AI look cheap or expensive depending on who is selling. The number that matters is cost per correctly handled item, and most of it is not the model.
Risk is set by what an answer is allowed to do
Part 3. Model accuracy matters, but it is not the control. The control is the list of actions an answer can trigger, and the tests it has to pass first.
Explainable by design, without pretending the model explains itself
Part 4. Jev does not tell you why it answered. That is a feature. Explanations you can defend come from the structure around the model, not from the model narrating itself.
Measuring value: coverage, correctness and review minutes
Part 5. Hours saved per user is a survey result. Value you can defend is measured on items: how many were handled, how many correctly, and what that saved.
Compliance is evidence you can hand over
Part 6. No model makes an organisation compliant. What a Jev-first design does is produce the evidence compliance teams, auditors and regulators ask for, as a by-product of running.