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Physical AI could give engineering-services companies a way to escape the price pressure that artificial intelligence is creating in conventional software and product-development work, according to L&T Technology Services (LTTS) chief executive officer and managing director Amit Chadha. Rather than selling faster delivery of the same engineering tasks, LTTS is applying AI to products, factories and industrial operations—areas that can create new revenue streams.
Why AI productivity is creating deflationary pressure
AI can generate code, engineering models, diagrams, plant models, mechanical drawings and component designs more quickly. For clients buying conventional software engineering or product-development capacity, that productivity can reduce the amount they need to pay for a given deliverable. Chadha described this pressure as concentrated on the IT and software-tool side of engineering work.
That does not mean every engineering service becomes a commodity. It does mean providers need to sell outcomes that are harder to substitute with faster code generation. LTTS’s answer is to use AI in environments where software interacts with machines, materials, workers and physical constraints.
What LTTS means by physical AI
Physical AI applies artificial intelligence to physical products and industrial processes rather than limiting it to office automation or software production. It can interpret sensor and machine data, simulate a factory or product, and support decisions made at the edge, on a production line, in a warehouse or across a supply chain.
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LTTS divides its AI activity into three operating areas:
| Area | How AI is used | Potential business effect |
|---|---|---|
| Engineering | AI-assisted development of product and engineering systems | Higher delivery productivity, but potentially more conventional price pressure |
| Products | Insights from visual images, heat and sound for edge-based solutions | New product capabilities and recurring solution opportunities |
| Manufacturing and operations | Process improvement, downtime reduction and efficiency gains | Value tied to industrial performance rather than hours delivered |
What the Bengaluru physical-AI lab does
LTTS launched a physical-AI lab in Bengaluru on October 12, 2026, as reported by Moneycontrol on October 15. The lab uses technologies from Nvidia and Siemens, including Nvidia Omniverse, to build and demonstrate applications for discrete and process manufacturing.
Digital twins and factory simulation
A digital twin is a software representation of a physical product, machine, line or facility. Engineers can use it to model how a system behaves, test changes and identify problems before making an expensive physical alteration. In the lab’s manufacturing focus, simulation can connect design decisions with factory operations and provide a controlled environment for evaluating AI-driven workflows before deployment.
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Factory operations and supply chains
LTTS identifies factory operations and supply chains as target areas. AI can help analyze process data, identify bottlenecks and support decisions about production flow, inventory or logistics. The commercial proposition is not simply that an engineer works faster; it is that a plant or network operates with less downtime and greater efficiency.
Drones and autonomous robots
The lab’s described demonstrations also include drones and autonomous warehouse or distribution robots. These systems combine perception, planning and physical movement. Their development requires attention to real-world conditions such as changing surroundings, safety constraints and machine behavior—factors that distinguish physical-AI projects from software-only automation.
How physical AI could create pricing power
Chadha said physical-AI solution pricing is “not a commodity game.” The underlying logic is that an industrial customer is buying a combination of domain engineering, integration, simulation, deployment and operational results. A provider that helps reduce downtime, improve throughput or validate a new production design can charge for business impact instead of only for development effort.
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This strategy changes the economic effect of AI in two ways:
- Lower delivery cost: AI reduces the time required for traditional engineering and software tasks, which can pressure prices.
- New revenue: AI-enabled products, factory systems and operational solutions create work that did not previously exist as a standard engineering-service line.
Physical AI does not eliminate the first effect. It is LTTS’s proposed route to making the second effect larger.
LTTS’s planned shift in revenue mix
About 50% of LTTS revenue in fiscal 2026 came from six strategic growth areas, including digital manufacturing, software and AI, according to the company’s 2026 disclosure. Management expects those areas to account for around 80% of revenue over the next five years.
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The figures describe a company-wide growth-area mix, not physical AI alone. They therefore should not be read as a forecast that physical-AI projects will generate 80% of revenue. They indicate the strategic direction in which LTTS expects its business to move.
The report also states that roughly 1% of revenue came from in-house tool and licence sales, although LTTS did not provide further detail about those sales. That small reported share suggests the present model remains primarily engineering services, with tools and licences a limited component rather than the main commercial engine.
Where the opportunity is strongest
| Application layer | Typical data modality | Deployment environment | Value proposition |
|---|---|---|---|
| Product | Visual, thermal and audio data | Edge device or embedded system | New product features and diagnostics |
| Factory | Machine and process data, plus visual and thermal inputs | Production line or plant | Less downtime and improved efficiency |
| Supply chain | Operational, location and inventory data | Warehouse, distribution network or enterprise system | Better flow, planning and automation |
Digital twins and simulation can support all three layers by allowing teams to test models and processes before committing to physical deployment.
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What is established—and what is not
The Bengaluru lab is a concrete proof point for LTTS’s strategy, but the public account does not name customers, disclose contract values or provide independently verified performance results. It also does not specify a detailed Nvidia Omniverse or Siemens product configuration for each demonstration. The evidence supports a strategic direction and set of application areas, not a measured return on investment or market-size estimate.
For engineering-services buyers, the practical question is whether a proposed project can connect an AI model to reliable physical data, simulate or test its behavior, integrate with existing equipment and produce a measurable operating improvement. Those requirements make implementation expertise and industrial context as important as the model itself.
What this means for engineering-services companies
LTTS is betting that the next phase of AI competition will move beyond making software development cheaper. Companies that combine engineering knowledge with digital twins, edge intelligence, factory integration and autonomous systems may be able to participate in new industrial spending rather than compete only on lower delivery prices.
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