Since the first industrial robot, Unimate, joined the General Motors production line in West Trenton, New Jersey, in 1961 to lift hot metal die-castings, robots have reshaped manufacturing.
As well as performing tasks that would be unsafe for people, robotics has enabled businesses to make considerable gains in productivity, precision and consistency. Yet for all the progress made over the past six decades, most industrial robots have remained fundamentally constrained: rigid systems executing predefined instructions in tightly controlled environments.
That paradigm is shifting.
According to John Chien, director of industry and product marketing at Microsoft, the industry is entering a new phase defined by what is increasingly known as ‘Physical AI’.
“The core narrative is this: the industry is moving from hard-coded, single-purpose automation to adaptive, AI-driven physical systems that can perceive, reason and act in real-world environments,” says Chien. “Physical AI refers to intelligence moving into the physical world… into factories, hospitals, logistics, mobility and so on. This is the next frontier for AI platforms, and it’s being shaped right now.”
But Chien is quick to stress that the true inflection point lies in scaling intelligence across entire industrial systems. “The real challenge isn’t building one smart robot,” he says. “It’s operationalising physical intelligence at scale: deploying, governing and continuously improving fleets of intelligent machines across sites, environments and vendors. That's an enterprise platform problem, and it’s where the most interesting tension sits right now.”
For decades, industrial robots have excelled in precision but struggled with flexibility. Any change in task, environment or product line has typically required significant reprogramming or engineering effort. What is changing now is the emergence of AI systems capable of generalising across tasks, supported by advances in simulation and cloud-scale infrastructure.
Chien points to a convergence of three forces: foundation models, high-fidelity simulation environments and scalable cloud platforms, which, together, enable a new kind of industrial intelligence.
For manufacturers, this means robotics is no longer just an automation tool, but part of a broader AI system capable of continuous learning and adaptation.
“The shift is that robots are becoming adaptive systems that can perceive, reason and act in real-world conditions that aren’t fully predictable,” says Chien. “What’s driving it is the convergence of foundation models that generalise across tasks, simulation environments mature enough to validate physical behaviours before deployment, and cloud platforms that orchestrate the full lifecycle at enterprise scale.”
While intelligent robotics demonstrations are increasingly common, Chien says the industry’s focus is shifting from individual machines to large-scale deployment. The challenge is ensuring physical intelligence can operate reliably across fleets of robots, multiple facilities and diverse vendor ecosystems while maintaining safety, governance and ongoing optimisation. “That’s an enterprise platform challenge, not just a robotics challenge,” he says.
A Teradyne Robotics mobile industrial robot operating at Faurecia’s Czech manufacturing facility, illustrating the growing role of autonomous systems in factory logistics (Photo: Teradyne Robotics)
A major bottleneck is data. Unlike digital environments, real-world robotics data is expensive, fragmented and difficult to capture safely at scale.
“You need simulation and synthetic data to train and validate before anything touches a factory floor,” says Chien. “You need lifecycle orchestration (design, simulate, deploy, operate, improve) that works end to end. And you need it to work across a heterogeneous ecosystem, because no single original equipment manufacturer (OEM) or software vendor covers the full stack. The ecosystem collaboration model is what makes it scale.”
This ecosystem perspective is increasingly visible across the industry, with robotics OEMs, simulation providers and cloud platforms converging around shared architectures rather than isolated solutions.
As robotics evolves into a software-defined, AI-orchestrated discipline, questions naturally arise around the role of large technology platforms. Microsoft’s position is as an enabling layer within a broader ecosystem, rather than a direct robotics provider.
“Getting Physical AI right requires world-class engineering of the machines, deep operational data from the real world and an AI platform that is safe, scalable and trusted,” says Chien. “Our role is to empower the Physical AI ecosystem, to help customers and partners accelerate innovation, operationalise solutions, and scale with enterprise-grade intelligence and trust.”
This platform-centric approach is reflected in initiatives such as the open Azure Physical AI toolchain, which integrates Nvidia’s Physical AI Data Factory Blueprint with Azure services to support the full lifecycle of robotics data.
“What stood out to me at Hannover Messe is how concrete the conversations have become,” says Chien. “A year ago, these were conceptual. This year, they were about deployment timelines, architecture decisions and operational readiness.”
He highlights a series of sessions involving Microsoft and partners including Wandelbots, Teradyne Robotics, Hexagon, Nvidia, BMW and KUKA, all exploring different aspects of what it means to operationalise physical intelligence at scale – from orchestration platforms to humanoid robotics and human-robot collaboration models.
In particular, he notes a shift in focus towards the execution layer: the systems required to bridge the gap between simulation and production.
“In our HMI masterclass with Wandelbots on operationalising physical intelligence, the discussion centred on the execution and orchestration layer, which is the missing piece between a promising model and a production-ready system,” says Chien. “That’s the gap the open-source Azure Physical AI Toolchain is designed to close, giving developers a blueprint to move from simulation through deployment and continuous improvement.”
Wandelbots held a partner demo at Hannover Messe 2026, focusing on operationalising physical intelligence (Photo: Wandelbots)
As the industry moves towards more intelligent and autonomous systems, Chien argues that manufacturers need to rethink how they approach robotics – not as isolated projects, but as scalable platforms integrated into broader operational strategies.
“The value I’m seeing today is in the foundation layers,” he says. “Companies investing in cloud-based robotics platforms, operational data pipelines and simulation environments are already seeing faster deployment cycles, better fleet-level visibility and more consistent operations across sites.
“The manufacturers moving fastest are the ones treating data as a strategic asset, approaching robotics as a platform rather than a project, and building ecosystem relationships early. And increasingly, the picture I see emerging across these conversations is one where humans, robots and AI agents work together as coordinated systems. That’s the shift to get ready for.”
Partner perspective
Technology Record asked Coretek how it is leveraging Microsoft's AI technology to help manufacturers leverage robotic solutions in order to drive efficiency, analyse data and operate equipment
“Coretek integrates Microsoft AI with industrial robotic and IoT systems to create intelligent, responsive manufacturing environments,” said Brian Barnes, chief technology officer at Coretek. “Using Microsoft Azure IoT Hub, AI Foundry and computer vision models, we deliver real-time equipment health monitoring, predictive maintenance and autonomous quality inspection. A large automotive parts manufacturer in Michigan, USA, deployed AI-powered robotic process monitoring across its production floor, reducing unplanned downtime by 35 per cent and improving defect detection accuracy by 28 per cent. By unifying machine data with generative AI insights, Coretek enables manufacturers to move from reactive maintenance to proactive operations – maximising throughput, safety and profitability at scale.”
Brian Barnes
Chief technology officer, Coretek
Discover more about how organisations are using Microsoft’s AI technology in manufacturing and insights from experts at Microsoft Partner businesses including Intermedia, Velosio and AVEVA in the Summer 2026 issue of Technology Record. For more content like this, subscribe to the print edition or free digital edition of the quarterly Technology Record magazine.