Robots cannot learn entirely inside simulations. They need exposure to the messy, unpredictable reality of homes, factories, warehouses, farms, and workplaces. A growing set of startups is collecting and preparing real-world data for global robotics and physical AI companies. Companies are building datasets, annotation pipelines, sensor-based recordings, and governance systems that can help train the next generation of robots.

As robotics companies race to build humanoid robots and autonomous systems, could India become the world’s preferred destination for collecting and managing the real-world data that powers them?

The next outsourcing opportunity

The emergence of physical AI has created a new category of enterprise services. Companies are now recording human actions, environmental conditions, object interactions and workflows that robots need to understand before they can operate independently.

The concept is similar to what Nvidia announced about the Physical AI Data Factory Blueprint in March, which aims to help organisations gather, curate and evaluate both real-world and synthetic data for robotics and vision AI systems. Many industry executives see a significant opportunity in this for India.

Manish Mamtani, chief information officer at Compass Group India, a Gurugram-based food services and facilities management company, told Business Standard that physical AI is no longer a future concept but a technology already being deployed in live environments.

“We are running autonomous cleaning robots, AI-driven kitchen environment controls, camera-based sorting machines that check the quality of raw ingredients, and camera-based safety monitoring across India,” he said.

Mamtani believes India’s diversity of physical environments, ranging from hospitals and pharmaceutical facilities to logistics hubs and corporate campuses, makes it an attractive training ground for robotics companies. He pointed to estimates suggesting India’s AI data services market could grow substantially by the end of the decade, though he cautioned that it would remain far smaller than the country’s IT exports business.

Jaspreet Bindra, founder of AI advisory firm AI&Beyond India and Tech Whisperer Limited UK, argued that physical AI data can evolve into a large outsourcing category because embodied AI systems require complex multimodal data rather than simple image or text annotation.

However, not everyone is convinced the opportunity will become another BPO-scale success story.

Amit Jaju, senior managing director at Ankura Consulting, a cybersecurity advisory firm, believes the sector is likely to remain a specialised, high-complexity market rather than a mass outsourcing business.

“Most global physical AI models are actually shifting towards synthetic data because it is cleaner and cheaper than processing messy real-world footage,” he said. “India risks ending up with only the low-margin data cleaning work, not the core outsourcing opportunity.”

Why India is attracting attention

Industry leaders say that cost remains an important factor. Large workforces are still required for data capture, quality assurance, and annotation.

“If a robot works in India, it will work anywhere,” Jaju argued, describing India’s chaotic and unstructured environments as a unique strategic asset.

Anurag Jain, founder & CEO at Delhi-based deep-tech firm Oriserve, argued that India’s combination of digital skills, diverse environments, and large-scale enterprise delivery capabilities makes it particularly attractive to robotics companies looking to scale operations globally.

Who captures the value?

According to industry executives and experts, at the bottom of the value chain sit households, gig workers, factory operators, and field workers generating the raw data. Above them are startups collecting, cleaning and annotating information. Higher up are companies building compliance systems, simulation environments, quality assurance layers, and deployment infrastructure. At the top are robotics companies, manufacturers, AI developers, and cloud providers using the data to train and improve machines.

“The premium value sits in full-stack robotics-data services and simulation-ready datasets. The real margins belong to companies that can ingest raw footage, curate edge cases, synthetically augment the data, and deliver structured environments that directly train autonomous systems,” Bindra said.

Mamtani said the lesson from robotics deployment is that value comes from building systems around the technology rather than simply supplying inputs. “Data collection alone is a commodity. Full-stack deployment is a capability,” he said.

Manish Mohta, founder of Learning Spiral AI, which provides data annotation and human-in-the-loop training services for robotics and autonomous systems, argued that companies managing the entire lifecycle – from data collection and governance to annotation, testing, and deployment – would capture significantly higher margins than pure annotation providers. He believes the biggest opportunities will emerge in simulation-ready datasets, compliance infrastructure, synthetic data generation, and full-stack robotics-data services rather than basic footage collection.

Jaju said the real value sits with organisations capable of guaranteeing that datasets are both legally compliant and technically reliable. “The company solving the legal and data integrity problem holds the value, not the data collector,” he said.

Anuj Chahal, founder & CEO of Maverick Simulation Solutions, believes simulation engineering and robotics validation services could become important revenue pools as global companies seek safer ways to train autonomous systems.

Who will buy these services?

Experts say the potential customers extend far beyond robotics startups. Enterprise demand could come from global robotics developers building humanoids, warehouse robots, and domestic robots. Manufacturers adopting smart-factory systems are another obvious market. Warehousing and logistics companies, automakers, autonomous mobility firms, appliance makers, defence technology providers, agricultural robotics developers, and drone companies are all potential buyers. Cloud and GPU platform providers are also investing heavily in physical AI ecosystems.

Indian IT services companies may become an important customer segment as well. As generative AI begins automating portions of traditional outsourcing work, physical AI services could emerge as a new revenue stream.

The privacy challenge

Datasets for physical AI differ from conventional digital datasets because they record people inside real environments. Homes, factories, and workplaces often reveal sensitive personal and commercial information.

Jaju warned that video data is inherently difficult to anonymise without reducing its training value. He said companies could face severe consequences if recordings inadvertently capture biometric information, trade secrets, or incidents that were never intended to be shared.

Rajat Srivastav, founder and CEO of Df-OS, which deals in converting industry activity into datasets, argued that physical AI cannot follow the internet-era model of collecting information first and addressing consent later. He said people must know what is being recorded, why it is being collected, how it will be used, and what rights they retain.

What happens to jobs?

Physical AI also raises uncomfortable questions about potential job losses. Warehousing, packaging, assembly-line operations, inspection, inventory management, logistics handling, and routine cleaning are among the categories most frequently identified as vulnerable to automation.

However, here, expert opinion diverges. Jaju expects a net reduction in demand for lower-skilled labour and argues that many emerging jobs will require technical capabilities that large sections of today’s workforce may struggle to acquire.

Others are more optimistic and believe the transition is more likely to involve job transformation than immediate large-scale displacement.

Bindra argued that physical AI could create more jobs than it eliminates in India, at least initially. “True displacement in India’s blue-collar sector will be slow due to low labour costs. However, repetitive warehouse sorting, structured assembly-line roles, and predictable commercial cleaning will be transformed first,” he said.

Mamtani offered a practical example from Compass Group’s own experience. When cleaning robots were deployed, he said the company was responding to labour shortages rather than replacing existing staff. New jobs emerged around robot training, operations monitoring, and maintenance.

The bigger opportunity

The experts assert that India’s opportunity is not simply to become a supplier of human-action videos, as the larger prize lies in becoming a trusted infrastructure layer for the global robotics industry.

That means building consent frameworks, privacy safeguards, quality assurance systems, simulation environments, domain-specific datasets, and deployment tools. It means combining the country’s services expertise with new capabilities in robotics, AI governance and digital twins.