Most AI failures in production are not model quality problems: they are ownership, observability, and rollback problems. Teams ship a demo then discover that prompts drift, latency spikes, and nobody knows which version of the system answered a customer last Tuesday.
A practical baseline is to treat AI features like any other critical path: define success metrics, add structured logging, version prompts and configs, and wire explicit fallbacks when confidence is low or the model times out. Users should never see a silent failure.
On the product side, resist hiding the AI behind a magic black box when transparency helps trust. Clear states like draft, needs review, or could not complete reduce support load and make your compliance stakeholders much happier.
Finally, plan for change. Requirements will shift and models will be swapped. If your UI and business rules are entangled with one provider's quirks, you will pay that tax on every migration. Keep interfaces boring and your orchestration layer explicit.