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Why AI productivity creates a deflation problem
AI can generate code, engineering diagrams, plant models, mechanical drawings and product components more quickly. That productivity is valuable to customers, but it can also reduce the amount they are willing to pay for conventional software and product-development work.
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Chadha told Moneycontrol that “the deflationary part that’s there is more in the IT side” and in software tools used for product development. His proposed response is to move beyond selling engineering hours and build AI-enabled systems for physical products and industrial environments. He described the opportunity as “not a commodity game,” because the commercial value comes from solving operational and manufacturing problems rather than simply completing an existing task faster.
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What physical AI means in this strategy
Physical AI applies machine-learning systems to assets and processes that operate in the real world. The inputs can include visual images, thermal readings, sound, machine telemetry and process data. The outputs may be a product insight, an automated factory decision, a simulated plant change or the control of a robot or drone.
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This differs from office automation or software-development assistance. A physical-AI project has to account for equipment, safety, physical constraints, production schedules and the consequences of a decision in a factory, warehouse or field operation.
LTTS’s Bengaluru physical-AI lab
LTTS launched a physical-AI lab in Bengaluru on October 12, 2026, according to Moneycontrol’s report dated October 15. The lab uses technologies from Nvidia and Siemens, including Nvidia Omniverse, to develop and demonstrate applications for discrete and process manufacturing.
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The reported focus areas are:
- Digital twins that represent products, machines or factories in software.
- Factory operations, including process improvement and production visibility.
- Supply-chain planning and operational coordination.
- Drones for industrial inspection or other physical tasks.
- Autonomous robots for warehouses and distribution centers.
A digital twin lets teams model an asset or process before changing the physical environment. In practice, that can support simulation, testing and scenario planning before equipment is reconfigured or a new automated workflow is deployed. The available report does not identify customer projects, contract values, product SKUs or independently measured performance for the lab.
How LTTS organizes its AI business
LTTS divides its AI activity into three areas: engineering; products; and manufacturing and operations.
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Engineering
This area covers AI applied to engineering work and product development. It is also where productivity-driven price pressure is most direct, because customers may need fewer conventional delivery hours when tools automate parts of the workflow.
Products
Product-focused AI can combine visual, heat and sound data to generate insights at the edge. Processing on or near the device can support rapid responses and reduce the need to send every data stream to a central system. The report does not state which specific products or customer deployments are in production.
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Manufacturing and operations
Manufacturing AI is aimed at improving factory processes, reducing downtime and increasing efficiency. This is the part of the model most closely connected to physical AI: the service is tied to operating performance, not only to the creation of software or engineering documentation.
Where the pricing power could come from
Physical-AI work can change the economic basis of an engineering-services engagement in several ways:
- New scope: A factory simulation, autonomous-robot workflow or supply-chain system is an additional industrial offering rather than a cheaper version of an existing coding task.
- Operational value: Customers can evaluate the work against downtime, throughput, quality or efficiency goals, although LTTS has not published verified results for the Bengaluru lab.
- Specialized integration: Connecting models to machines, sensors, plant processes and safety requirements requires domain engineering as well as software.
- Simulation before deployment: Digital twins can provide a test environment for proposed changes before they affect a live production line.
That does not make every physical-AI service immune to competition. Hardware, data quality, integration complexity and customer adoption can all affect margins. Chadha’s point is that these engagements are less likely to be priced solely as interchangeable software-delivery capacity.
LTTS’s planned revenue shift
About 50% of LTTS revenue in fiscal 2026 came from six strategic growth areas, including digital manufacturing, software and AI, according to L&T Technology Services. Management expects that share to reach around 80% over the next five years. The figure is a company-wide revenue-mix objective, not a forecast that physical AI alone will produce the increase.
The report also says roughly 1% of revenue came from in-house tool and licence sales. LTTS did not provide further detail about those sales, so that figure should not be treated as a measure of the physical-AI lab’s current contribution.
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The lab is a concrete demonstration of the direction, but the public report does not establish commercial scale. The most important future evidence will be:
- Named customer deployments and repeat contracts.
- Revenue or margin attributable to physical-AI services and licences.
- Measured reductions in downtime, defects or operating costs.
- Evidence that simulations transfer reliably to live factories and warehouses.
- How much work is delivered at the edge, inside a plant, in a warehouse or through enterprise software.
Until those details are disclosed, the strongest supported conclusion is strategic: LTTS is positioning physical products and industrial operations as a new growth layer that can offset deflation in traditional engineering software work.
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