Physical AI Solutions

DXC Physical AI helps enterprises turn operational bottlenecks into validated, deployable and scalable industrial improvements. It brings artificial intelligence into robots, vehicles, machines and devices that sense, understand, act and learn in the real world.

The approach is built around three core capabilities: Live Twin, Sensor Platform and Operations Management. Together they connect live operations, simulation-ready models, validation, embedded execution and rollout into one practical path from pilot to production.

DXC designed this approach to close the gap between AI demos and real industrial deployment, which is where many Physical AI initiatives stall.

Physical AI is becoming a priority because most industrial AI initiatives currently stall at the demo or simulation stage and break down once moved into a real production environment. Manufacturers are under growing pressure to show measurable results from AI investment rather than isolated pilots.

Without reliable operational data, up to 60 percent of AI projects fail to deliver value. This is pushing organizations to look for frameworks that combine simulation, validation and real-world deployment rather than treating Physical AI as a research exercise.

DXC Physical AI was built specifically to address this shift, giving manufacturers a staged, evidence-based path from pilot to production.

DXC Physical AI is currently focused on industries where robots, vehicles and machines operate in complex physical environments. This includes automotive manufacturing, aerospace and defense, logistics and warehousing, and government industrial modernization initiatives.

In these settings, DXC's three pillars — Live Twin, Sensor Platform and Operations Management — address recurring problems like slow commissioning, unreliable operational data and fleet coordination at scale.

DXC has applied this approach in heavy industry as well, including a fleet management and autonomy deployment for a large-scale mining operation.

DXC works with a multi-vendor technology ecosystem for Physical AI, giving customers the freedom to choose hardware and simulation providers that fit their environment rather than locking into a single vendor.

Key partners include NVIDIA, AMD and Qualcomm for edge and chip capabilities, along with Unreal, Unity, Blender and ROS for simulation and modeling.

This approach means DXC Physical AI can be built around the tools that a customer's environment already supports, rather than requiring a specific proprietary stack.

DXC Physical AI works with robotic fleets primarily through its Operations Management pillar, which coordinates robot and AMR fleet behavior through task decomposition, routing, station service and priority logic.

This addresses a common failure point: as robot and AMR fleets grow, poor coordination creates delays and unreliable material flow. Task decomposition alone has been shown to increase robotic fleet operational efficiency by 10 percent.

Sensor Platform supports this by feeding the fleet with live operational data from AMRs, robots, sensors and cameras, keeping coordination decisions grounded in real conditions rather than static assumptions.

DXC Physical AI reduces risk through Live Twin, which models a production line change in a digital twin before it happens in the real environment. This enables teams to see where time and stability would be lost before making physical changes.

Customers using this approach see new factory setup and line repurposing run 30 percent faster, with up to 95 percent of errors detected before go-live.

This matters because commissioning and line changes are typically the highest-risk moments in a manufacturing operation, and proving a change virtually first removes much of that risk before production is touched.

What sets DXC apart from other physical AI companies is the combination of integration, validation and rollout in one end-to-end engineering approach, rather than simulation, robotics or consulting delivered on their own.

DXC’s approach is built for brownfield integration, meaning it fits into existing plants, fleets, systems and suppliers without forcing replacement. It pairs SimReady engineering, built on tools like NVIDIA Isaac Sim, NVIDIA Omniverse and NVIDA Cosmos, with a live operational data layer that keeps training and validation grounded in real signals rather than assumptions.

That combination enables DXC to move a customer from a proven pilot to embedded execution and enterprise rollout, rather than stopping at a one-off proof of concept.

DXC Physical AI addresses three connected challenges that typically prevent projects from scaling:

1. Unsafe or slow commissioning of new or changed production lines; solved through Live Twin's pre-production validation.
2. Lack of reliable operational data for AI training and compliance; Sensor Platform addresses this by turning camera, sensor and robot data into a traceable evidence layer.
3. Fleet coordination breaking down as robot and AMR fleets grow; Operations Management resolves this through task decomposition and routing logic that increases fleet operational efficiency by 10 percent.

Each pillar of our approach targets a specific point where Physical AI initiatives typically stall.