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Give outcome labels time to mature Recent records may contain unresolved outcomes rather than negative results. Define the target window and reporting allowance before using conversion labels for retraining or investment decisions. Build reassessment into AI procurement An AI supplier can change the model, data handling or workflow after contract award. Define material-change triggers, evidence rights and review authority so continued use remains an explicit buyer decision. Put a decision gate on live AI testing A live AI trial needs a defined customer outcome, exposure boundary and executable stop decision. Use the proof of concept to design that trial, then use the trial to decide whether the service should expand. Constrain the inventory policy before training the agent An inventory agent acts on cash, stock and service levels. Define its permitted actions, comparison policy and fallback before asking it to optimise replenishment. Time saved at a desk is not capacity released A shorter individual task does not establish extra organisational capacity. Follow the work through queues, reviews and bottlenecks, and value working-pattern improvements separately from increased output. Evaluate the system where its decisions are made Evaluate an AI workflow against the decisions it will support. Include retrieval, permissions, tools and human review, then agree how outcomes and guardrails will determine release. An erasure request must reach the derived copy An erasure request may affect training extracts, retrieval indexes and stored AI outputs as well as the source record. Map the derived copies, assess their status and record the decision for each relevant location. Shared responsibility starts at the AI service boundary Approve supplier handovers as part of the AI service design. Name who controls data access, supplies incident evidence and restores service, then test that those parties can perform the agreed work. A pricing model needs a learning budget before it needs more features Demand learning can require controlled price variation with an economic cost. Agree the exposure, margin floor and stopping rule before asking a pricing model to optimise revenue. Do not ask a quality model for a score. Ask for an intervention. A quality model can help engineers choose a process intervention. Its commercial value depends on a safe comparison that connects the change to saleable output, stability and cost. Personalise the offer, then prove the margin Personalisation can change which offer reaches an eligible customer. Test that policy with a randomised holdout and measure incremental conversion, margin and later customer outcomes. Automate the close, not the sign-off AI-assisted classification may release accounting capacity, but review and correction work determine the result. Test routine handling against the existing close process while preserving evidence and accountable sign-off.
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