NTT DATA named market shaper in Gartner physical AI

NTT DATA has been named a Market Shaper in Gartner's inaugural Emerging Market Quadrant for Physical AI Services. The report assessed 27 providers in what Gartner described as an emerging enterprise market.

The designation places NTT DATA among vendors seeking to define a category focused on deploying artificial intelligence in physical operating environments rather than purely digital applications. The emphasis is on systems that can interpret conditions in factories, logistics networks, utilities, and other industrial settings, then act on that information through connected devices, robotics, and software.

Gartner's first quadrant for Physical AI Services reflects a broader shift in corporate AI spending. After an intense focus on foundation models and generative AI tools, technology suppliers and industrial groups are pushing use cases tied to production lines, inspection systems, transport equipment, and operational infrastructure.

NTT DATA provides services across manufacturing, transportation and logistics, energy and utilities, and smart city environments. Its offering includes edge-based AI systems, wireless connectivity, simulation tools, robotics support, digital twin deployments, and managed IT and operational technology services.

The company also highlighted its managed Edge AI platform, introduced last year, as part of that strategy. By processing data closer to machinery, sensors, and connected assets, edge systems are intended to reduce latency and enable operational decisions to be made on site rather than through distant cloud platforms.

Industrial focus

As part of that push, NTT DATA is highlighting a series of industrial projects intended to show how AI can move from pilot programs into day-to-day operations. One of the most prominent examples is its work with Hyster-Yale Materials Handling and Archetype AI on a manufacturing assembly solution that embeds intelligence directly into the production environment.

According to NTT DATA, the system is designed to support quality assurance and efficiency on the factory floor. It also cited applications including AI-based anomaly detection, crack detection using digital twins, task verification in manufacturing settings, and industrial inspections using quadruped robots.

Another example is its work with Cargill on a global private 5G deployment for manufacturing modernization. The project is intended to connect assets, robots, and industrial systems across operational environments that require low-latency, resilient communications.

Such deployments underline a central argument in the physical AI market: AI in industrial settings depends not just on models, but on a broader infrastructure stack. Connectivity, edge computing, cybersecurity, IT and operational technology integration, and ongoing management are all becoming part of the commercial offering.

Beyond software

Physical AI has emerged as a distinct term for systems that combine software intelligence with machines and equipment in the real world. In practice, that can include autonomous vehicles in warehouses, robot-assisted inspections, sensor-driven monitoring systems, and AI-based controls embedded in assembly operations.

For large enterprises, the commercial appeal lies in tying AI investment to measurable operational outcomes such as reduced defects, better equipment monitoring, improved throughput, or lower downtime. That differs from many generative AI deployments, which have tended to center on office productivity, customer service, or software development.

NTT DATA said its services cover the full lifecycle of these systems, from design and engineering to governance, monitoring, and updates. It also said it works with a broader ecosystem of established technology providers and startups to deliver projects at scale.

That ecosystem approach matters because physical AI projects often span multiple disciplines. An industrial customer may need data pipelines from machines, secure wireless networks across a plant, simulation tools to test workflows, robotics integration, model monitoring, and operational oversight once a system goes live.

"Physical AI is transforming how organizations operate across industries, but success depends on deploying it securely and safely," said Shahid Ahmed, Head of Edge Services, NTT DATA.

"NTT DATA's comprehensive portfolio of Physical AI solutions helps clients build more intelligent operations that improve productivity, strengthen operational performance, and deliver measurable business value," Ahmed said.

A second executive framed the market around practical deployment rather than concept testing, saying the company's position depends on whether projects can be turned into working industrial systems.

"NTT DATA's Physical AI story is grounded in applied innovation," said Pietro Scarpino, Head of Applied Innovation for NTT DATA.

"Our Global Innovation Centre experiences showcase physical AI solutions, while production examples - such as private 5G-enabled robotics and edge AI - prove how ideas move from demonstrations to real-world operations," Scarpino said.