India as a monetisation test case
For investors and the broader ecosystem, the stakes are clear:
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If it works, it will become a blueprint for scaling across emerging economies -
If it fails, AI revenues remain concentrated in developed markets
For now, however, India remains primarily a usage-led market, not a revenue-led one.
According to Rahul Agarwalla, managing partner at SenseAI Ventures, India is largely a scale and usage story, and that usage brings across a lot of data. “That gives AI companies a huge edge because they understand what users are trying to solve. That’s where the company wins,” he told Business Standard.
Agarwalla, whose firm has backed AI-first startups such as Vernacular.ai and Cureskin, said usage-driven improvement over revenue may delay monetisation.
The four-part strategy
According to the experts, OpenAI’s India play is not a single lever but a combination of four parallel bets.
1. Consumer: solving for affordability
The first layer is access. With an aim to widen the funnel and monetise over time, OpenAI has experimented with low-cost offerings such as ChatGPT Go to expand adoption in price-sensitive markets like India.
But India’s history with digital products suggests that conversion from scale to revenue – at least at the consumer level – is not guaranteed.
Sunil Kharbanda, founder & COO of Trezix, a Surat-based technology innovation company, told Business Standard, “India’s low willingness to pay for AI reflects a deeper truth: enterprises will not pay for access to AI, but they will pay for outcomes.”
2. Enterprise: where the real money is
If consumer monetisation is uncertain, enterprise is where the real opportunity lies.
OpenAI’s partnership with the Tata Group to accelerate AI-native transformation in India, including deployment through TCS and focusing on building massive AI infrastructure, signals a clear push into large-scale enterprise adoption.
Early signs suggest that monetisation is already happening. “OpenAI and others are already monetising Indian businesses through APIs. Those revenues are not insubstantial,” Agarwalla noted.
At the same time, enterprise demand itself is evolving. According to Kharbanda, Indian enterprises are clearly transitioning from AI experimentation to committed, return on investment (ROI)-driven spending.
3. Infrastructure: the sovereignty layer
OpenAI is also investing in local infrastructure, marking a shift from purely global delivery models. The company plans to work with TCS’ HyperVault data centre business.
“OpenAI will become the first customer of Tata Consultancy Services’ HyperVault data center business, beginning with 100 megawatts of capacity and with potential to scale to 1 gigawatt over time,” OpenAI said earlier this year.
This reflects a broader structural shift.
Agarwalla explained why localisation is no longer optional. “It is always easier to have compute closer to the user. There are also data sovereignty issues. For OpenAI, establishing a footprint in every major market is a given,” he said.
He added that India’s infrastructure gap makes this even more urgent. “India has about 3 per cent of global data centre capacity but produces around 20 per cent of the data. That gap has to shrink.”
4. Ecosystem: locking in future users
Beyond pricing and infrastructure, OpenAI is also focusing on ecosystem-building by investing in broader AI adoption initiatives, developers, education, and workforce skilling.
However, Kharbanda said while OpenAI accelerates access to AI capabilities, it does not solve the harder challenge of embedding AI into enterprise systems, governance frameworks, and workflows.
“Sustainable adoption happens only when AI becomes part of the operating layer of the business, not a standalone tool. This is where domain platforms play a critical role in translating AI into measurable outcomes,” he said.
Why India is hard to monetise
India’s digital economy has long been characterised by massive scale and weak monetisation. According to Kharbanda, India is not purely “price-sensitive” but “value-sensitive”.
“Clear ROI is the key to unlocking enterprise spending,” he said.
According to experts, there are also some enterprise constraints. Even as adoption rises, budgets remain disciplined, and spending is majorly tied to:
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measurable outcomes -
efficiency gains -
compliance requirements -
Open-source pressure
While India’s developer ecosystem is highly cost-conscious and open-source friendly, Kharbanda argues this may not significantly impact enterprise monetisation. “Open-source models will drive experimentation, but enterprise deployments require reliability, governance, and auditability at scale,” he said.
According to Kharbanda, AI may be one of the first digital categories in India where monetisation is driven by productivity, not advertising.
“AI by itself is not a product; it becomes valuable only when embedded into real business processes,” he said.
This shifts the conversation from access to outcomes, and from pricing to value creation.
What this means for India Inc
Experts highlight that OpenAI’s strategy could reshape how Indian companies adopt and pay for AI. For startups, the implications are immediate. Kharbanda said, “Foundational models are becoming a commodity layer, but platforms that combine AI with domain expertise and workflow integration are structurally advantaged.”
According to him, this creates pressure on horizontal AI startups, but an opportunity for specialised ones.
For IT services firms, the outcome may be hybrid. “They will act as both partners and competitors, but the ecosystem will largely evolve toward co-creation rather than direct competition,” he added.
