Autonomous AI agents are racing into the enterprise, but oversight is lagging. Overall, the current level of at least moderate agentic AI adoption in the ER&I industry (20%) is lower than the broader pattern across industries and is slightly below the cross-industry average (23%). Over the next two years, adoption rates are expected to grow significantly with shared momentum across industries—aside from technology, media, and telecom, which projects a notably higher rate.

Our report shows that 92% of ER&I companies expect to customize agents to fit the unique needs of their business.

Physical AI is the class of AI systems that perceive the real world, make decisions, and drive physical actions through machines or control systems.

Physical AI integration is already expanding, with 72% of companies in the ER&I industry reporting at least limited use of physical AI.

A key factor in early adoption is environmental control. Physical AI use cases that take place in controlled domains such as factories and warehouses tend to progress much faster than use cases in open, real-world environments, where the challenges and risks are far more complex and unpredictable.

In our survey, cost was cited most often as a key barrier to physical AI deployment. When evaluating business cases for physical AI, decision makers in ER&I organizations should account for total cost of ownership and not just initial equipment costs. These costs can significantly exceed the initial investment in AI models and software. Companies that underestimate these costs risk project delays or abandonment partway through implementation.