AI projects rarely fail for the reasons we expect when we start them.

The prototype works. The model looks impressive. The architecture seems reasonable. Then real data, real users, real latency and real costs arrive.

This session is an honest look at the engineering lessons that emerge when AI moves from experimentation toward real use. We will examine the assumptions that looked reasonable at the beginning, the problems that appeared later, and the changes that made the system more useful and reliable.

Topics include data quality, retrieval, context, evaluation, latency, cost and the gap between a successful demonstration and a dependable application.

Rather than presenting a polished success story, the session focuses on the decisions that had to be revisited, the approaches that did not work as expected and the engineering principles that emerged from those failures.

Attendees will leave with a practical checklist for identifying risky assumptions earlier in their own AI projects and avoiding some of the expensive lessons that only become obvious after implementation.