The organisations getting value from AI are not the ones with the most ambitious pilots. They are the ones that picked a decision worth improving, defined what data the system may see, and named who remains accountable for the outcome.
Start from a decision, not a capability
A use case framed as a capability — summarise documents, answer questions — has no success condition. Framed as a decision, it does: which claim to review first, which supplier record needs correction, which ticket can be closed without a human reading it.
That framing also reveals the cost of being wrong, which determines how much oversight the workflow needs.
Data boundaries are the real design work
Most enterprise AI risk is data risk: what the system can retrieve, what leaves your control, what is retained and what appears in an output to someone who should not see it.
Deciding those boundaries per use case, and enforcing them in the retrieval layer rather than the prompt, is what makes deployment defensible to a security reviewer or a regulator.
Keep a human answer to 'who is responsible?'
Oversight is not a review step bolted on afterwards; it is a design property. Log what the system was asked, what it retrieved and what it produced, and keep a named role accountable for decisions taken on that basis.
Done this way, AI becomes an ordinary part of the control environment rather than an exception argued about at every audit.