A proof of concept answers one question: can this work on our data? Production asks harder ones: will it keep working, can we tell when it does not, and does it fit how people already do their jobs?
Integration is most of the work
Useful AI reads from and writes to real systems such as ERP, document stores and ticketing tools. Designing those integrations, with the right permissions and error handling, usually takes more effort than the model itself.
Measure what matters
Agree on success measures before building: accuracy on representative cases, time saved, escalation rates. Without them, it is impossible to know whether optimization is working.
Design for people in the loop
The most dependable AI workflows make it easy for a person to review, correct and override. That is what earns trust and allows automation to expand over time.