It is easy to talk about AI Agents. It is much harder to put them to work. In this session, we will share practical lessons from customers that have built and deployed Agents in real operational settings. One manufacturing firm used an Agent to monitor supply volatility and adjust procurement in near real-time. A retail customer applied Agent logic to automate promotional planning at product level, with measurable improvement to margin and stock levels.

These were not perfect projects. They faced internal resistance, unclear ownership, and data quality issues. What made them work was not the technology alone, but the discipline to keep the scope narrow, the logic transparent, and the outcomes visible to those on the front line.

You will leave with a clear understanding of what separates working Agents from stalled prototypes. This is not a vision pitch. It is a reflection on what actually happened, what needed to change, and how both companies and their partners adapted to get results.

It is easy to talk about AI Agents. It is much harder to put them to work. In this session, we will share practical lessons from customers that have built and deployed Agents in real operational settings. One manufacturing firm used an Agent to monitor supply volatility and adjust procurement in near real-time. A retail customer applied Agent logic to automate promotional planning at product level, with measurable improvement to margin and stock levels.

These were not perfect projects. They faced internal resistance, unclear ownership, and data quality issues. What made them work was not the technology alone, but the discipline to keep the scope narrow, the logic transparent, and the outcomes visible to those on the front line.

You will leave with a clear understanding of what separates working Agents from stalled prototypes. This is not a vision pitch. It is a reflection on what actually happened, what needed to change, and how both companies and their partners adapted to get results.