The path to autonomous operations depends not only on technical capability, but also on operator trust, human judgment, competency, governance, and clear decision authority.

As AI costs decline, economic value may shift from model providers toward enterprises that embed inexpensive intelligence into high-value operating workflows.

The emerging logistics control layer connects fragmented execution systems to coordinate operating state, decisions, and actions across the logistics network.

Modern TMS platforms are extending beyond planning and execution to support connected operating states, faster decisions, and more dynamic transportation management.

Siemens’ Vivek Roy explains how data integration, industrial edge, contextualization, and zero trust can help manufacturers build scalable and secure foundations for industrial AI.

ABB’s Rajesh Ramachandran outlines how Industrial AI, combining contextualized data, analytical AI, generative AI, agentic AI, and digital twins is helping industries progress from automation toward increasingly autonomous operations.

Nova SMAR’s Octávio Paschoal explores how standardized interfaces, software-defined flow computing, and user-defined function blocks can extend O-PAS beyond traditional process-control applications.

Logistics leaders need to move beyond functional optimization and manage transportation, warehousing, technology, partners, and decisions as one interconnected system.

COPA-DATA’s Kishor Mandavia and Infrared Power’s Varun Garg explore how scalable OT platforms, AI, and emerging connectivity approaches can help renewable energy operators manage increasingly complex assets and move toward more intelligent operations.

SAP is broadening transportation management beyond core TMS functionality by connecting advanced planning, carrier collaboration, lighter logistics execution, warehouse integration, and emerging agentic AI capabilities into a more unified logistics execution architecture.