Manufacturers eye physical AI gains amid governance gaps

TCS has published a report on physical AI in manufacturing, finding strong expectations of operational change across warehouses and production environments.

The report is based on a survey of 300 manufacturing Chief Experience Officers and Vice Presidents in North America and Europe, spanning sectors including automotive, electronics, industrial machinery, process industries, and aerospace and defence.

According to the findings, 77% of respondents expect physical AI to have a significant or transformational effect on warehouse operations. Another 75% said the same for assembly and manufacturing operations, while 72% pointed to logistics and material movement.

The research suggests manufacturers view the technology as a long-term investment rather than a short-term trial. No organisation surveyed plans to cut spending on physical AI, while 26% expect to increase investment.

That spending outlook comes even though many companies are still at an early stage of adoption. TCS found that 68% of manufacturers remain in non-deployment or experimental stages, with legacy system integration, data infrastructure, and workforce skills identified as the main barriers to wider deployment.

Workforce focus

The report also highlights a strong emphasis on workforce support rather than replacement. It found that 42% of manufacturers expect significant workforce augmentation from physical AI, particularly through safety improvements and support for staff working in hazardous, repetitive, or complex settings.

This positions physical AI as a broader industrial operating model rather than a narrow automation project. Companies are shifting attention from standalone AI applications to larger systems spanning factories, warehouses, logistics networks, maintenance operations, and quality management.

Anupam Singhal, President, Manufacturing, TCS, commented on the findings.

"Physical AI is taking intelligence beyond the screen and onto the shop floor, where machines sense, adapt and act in real time. The manufacturers that scale it successfully will define the next era of manufacturing. TCS' 'infrastructure to intelligence' approach positions them to lead that transformation. With all the manufacturers in our study planning to either maintain or increase the investment, the direction is clear: towards more resilient, adaptive, and future-ready manufacturing enterprises," said Singhal.

Governance gaps

Alongside the investment case, the report highlights governance as a weak point for many manufacturers. It found that 44% of respondents reported unclear or no formal accountability structure for physical AI failures, while 40% said they were unprepared for emerging regulatory requirements.

Those figures suggest organisational structures may not yet be keeping pace with interest in deploying AI-linked systems in physical industrial settings. As companies move from experiments to wider deployment, questions around responsibility, oversight, and compliance are likely to become more pressing.

The study also links the findings to TCS's work with Google Cloud. That relationship includes the TCS Physical AI Gemini Experience Centre in Troy, Michigan, designed to help manufacturers test and scale use cases around safety, quality, and operational efficiency.

Kevin Ichhpurani, President, Global Partner Ecosystem, Google Cloud, described how the partnership is being positioned in manufacturing.

"Physical AI is moving manufacturing from digital insight to autonomous real-world action. Through our partnership with TCS, we are bringing Gemini's multimodal reasoning to the factory floor, enabling robots and systems to operate safely and intelligently in complex industrial environments," said Ichhpurani.

TCS's broader physical AI work also includes a blueprint combining robotics, sensing, edge intelligence, and cloud-based orchestration. It is intended to help manufacturers build physical AI systems with stronger operational oversight as adoption broadens.

The results point to a market that sees clear industrial use cases for physical AI but has yet to resolve many of the operational and governance questions involved in introducing autonomous and semi-autonomous systems into production and logistics environments. One of the clearest findings was that 68% of manufacturers remain in non-deployment or experimental stages.