AI has transformed digital systems, but most physical operations remain partially observed, sampled, and delayed. In logistics environments, decision-making is often based on declared system state rather than continuously verified physical reality. This roundtable will explore the rise of Physical AI - the application of continuous sensing, spatial intelligence, and closed-loop learning directly to real-world infrastructure - and what it means to turn warehouses and logistics networks into live, intelligent systems rather than static facilities supported by software.

For CTOs and CDOs, Physical AI represents a foundational shift. When physical state becomes continuously captured and reconciled, data integrity improves, latency between reality and system awareness collapses, and automation becomes materially more reliable. This unlocks new levels of operational trust, faster decision cycles, and the ability to move from reactive exception handling to proactive optimization. It also reframes logistics infrastructure as programmable and observable - much like modern cloud environments - enabling experimentation, simulation, and continuous improvement at the physical layer.

The session will convene technology and data leaders to discuss where their current blind spots lie, how data trust impacts AI initiatives, and what architectural changes are required to support truly intelligent operations. Rather than focusing on point solutions, the conversation will examine how Physical AI can become a strategic differentiator - shifting competitive advantage from running tighter operations to building smarter, self-aware infrastructure.