Physical AI: The Next Big Growth Frontier for Tech Service Providers

For years, the idea of intelligent machines belonged to fiction and future forecasts. Today, it sits at the center of boardroom discussions and investment decisions. Across industries, machines that can think, decide, and act are reshaping enterprise priorities. We are seeing real-world examples of this shift.

SoftBank’s recent move to acquire ABB’s robotics division for USD 5 Bn Skild AI’s launch of a general-purpose robotic “brain” backed by leading investors, and SAP’s partnership with NEURA Robotics and NVIDIA to connect enterprise systems with physical agents all signal one thing: intelligence is leaving the cloud and entering the physical world. This is Physical AI.

Physical AI: From Automation to Intelligence in Motion

Physical AI enables machines to sense, reason, act, and learn. These systems do more than follow programmed instructions; they interpret their surroundings, decide what to do next, and improve with every interaction.

A Physical AI system sees through cameras, LiDAR, and tactile sensors, reasons about its environment, acts on those insights, and refines its performance over time. Unlike traditional automation, which executes predefined steps, Physical AI adapts as conditions change, allowing operations to evolve dynamically.

This shift is redefining how enterprises think about work. As intelligence moves into the physical world, the way work is designed, executed, and improved is being reimagined for adaptability and speed.

Enterprise Operations Are Evolving: From Linear to Learning Systems

For decades, industrial value chains followed a predictable pattern: Design → Build → Operate → Service. Each stage optimized its own goals and passed the baton to the next. The result was efficiency, but not adaptability.

Physical AI changes this model. By embedding intelligence into machines and systems, enterprises can now connect these once-separate stages into a single learning cycle.

• Design happens within digital twins, where teams can detect bottlenecks and test scenarios before a single part is produced.
• Build relies on vision-led robotic cells that use cameras and LiDAR to adapt force, sequence, and motion in real time.
• Operate is orchestrated by predictive systems and autonomous movers instead of manual oversight and static schedules.
• Service feeds live telemetry back into design and production, ensuring that every new cycle launches smarter than the last.

Together, these feedback loops transform operations from fixed processes into self-improving systems that learn from every movement and interaction.

Automotive: Where the Line Begins to Learn

No industry illustrates this shift more vividly than automotive, the birthplace of industrial automation and now the testbed for Physical AI.

At BMW’s Spartanburg plant, humanoid robots from Figure AI are being trained to handle last-meter assembly tasks that change too often for fixed automation. Tesla’s Optimus prototypes are exploring how humanoids can take on repetitive, precision tasks alongside human workers. Stellantis is using AI-driven vision for real-time inspection, while Siemens and FlexQube are reinventing intralogistics with self-coordinating fleets of automated guided vehicles.

Even on the road, Aurora’s autonomous freight operations feed real-world driving data back into virtual testbeds, improving models with every run. The car trains the factory, and the factory trains the fleet.

This continuous feedback loop defines how modern enterprises learn and scale. What began on the automotive line is now visible across sectors: turbines that train their digital twins, warehouses that teach autonomous robots, and similar learning systems emerging across manufacturing, services, and mobility industries.

As enterprises move toward this intelligent, connected model, the complexity of integration grows. Turning these learning systems into enterprise-scale operations requires deep expertise across data, software, and hardware. This is where Tech Service Providers are stepping in, building the digital-physical backbone that allows intelligence to operate safely, continuously, and at scale.

The Tech Services Opportunity in Physical AI

The Physical AI decade represents a USD 300 Bn opportunity for Tech Service Providers through 2030. While the headlines focus on humanoids and autonomous fleets, the real transformation lies beneath; in the systems that enable sensing, reasoning, acting, and learning at scale. This foundation is where Tech Service Providers can create the most immediate and sustained value.

• Build the data spine. Enterprises need unified, time-synced data fabrics that connect sensors, assets, and systems across the physical and digital worlds. This is where Tech Services can integrate operational and information technologies into a single, trusted source of truth.
• Wire perception. Retrofitting existing assets with the right sensors and calibration unlocks visibility and responsiveness. Designing fused, real-time perception systems is becoming a fast-growing services market of its own.
• Keep twins alive. Digital twins are evolving from static models to living systems that learn continuously from telemetry. Maintaining and validating these twins as a managed service represents a new recurring revenue stream.
• Run the learning loop. The next enterprise stack will rely on RobOps, MLOps, and human-supervised autonomy. Managing these loops safely, with governance built in, will define the next generation of operations services.

Together, these capabilities allow Tech Service Providers to turn Physical AI from potential into performance.

Operating Intelligence at Scale: The Next Chapter for Enterprises and Tech Services

Enterprises are entering a new phase where intelligence is no longer confined to software or the cloud. It now powers how factories, warehouses, and hospitals operate and improve every day.

To scale this transformation, they need partners who can connect data, perception, and autonomy into one intelligent system. Tech Service Providers are essential in building the digital-physical foundations that make Physical AI real, scalable, and reliable.

Download Zinnov’s report, Physical AI: The Next USD 300 Bn Opportunity for Tech Services, to explore the full market landscape, growth potential, and execution models that will define success in this decade.