PAID PIPER: Using it is fine, but who is paying for artificial intelligence? Only companies. Not individual users.

Blitz Bureau

NEW DELHI: The challenge of monetising AI in India is a classic “volume vs. value” puzzle. While India is often the top market by downloads and active users for platforms like OpenAI, the conversion to paid individual subscriptions remains lower than in the West.

However, other companies are bypassing the “usage-led” trap by shifting their focus from B2C (individual users) to B2B (enterprises) and “invisible” AI monetisation. Here is how the landscape looks in 2026:

1. The B2B pivot (enterprise & API)

Most large-scale AI monetisation in India is happening behind the scenes. Companies like Microsoft (Azure OpenAI) and Google (Gemini for Google Cloud) are seeing significant revenue growth by selling AI as an infrastructure tool rather than a standalone app.

Outcome-based models: Instead of monthly fees, firms are charging Indian enterprises based on “business outcomes” (e.g., reduction in customer service costs or improvement in lead conversion).

API Dominance: Companies are increasingly focused on the developer ecosystem. Revenue is generated every time an Indian start-up or tech firm calls an API to power their own features, rather than waiting for individual users to pay for chat-bots.

2. Meta’s “invisible” AI monetisation
Unlike OpenAI, which relies on direct subscriptions, Meta is monetising AI in India through its existing advertising and WhatsApp ecosystems.

AI-enhanced ads: Meta’s GEM (Generative Ads Model) has seen high adoption in India. Small and medium businesses use these tools to automatically generate localised ad creative, which increases their ad spend and conversion rates.

WhatsApp Business: Meta is integrating AI agents into WhatsApp for Business, allowing Indian companies to automate sales and support. Monetisation comes from the per-message fee or premium business tiers, making AI a “value-add” to a service users are already paying for.

3. Indian start-ups & “lean success”
Home-grown start-ups are finding success by focusing on niche, sector-specific AI.

Vertical AI: Companies like Wyzard AI or Prodigal (in loan servicing) are monetising by solving specific Indian industrial problems (e.g., vernacular support or debt collection) where the ROI is clear and immediate for a business client.

The lean advantage: Indian AI start-ups are reportedly building pilot projects at roughly one-eighth the cost of global peers, allowing them to remain profitable even with lower price points.

Usage versus revenue

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 start-ups such as Vernacular.ai and Cureskin, said usage-driven improvement over revenue may delay monetisation.

“I think the battle is a long one. If usage helps them (AI firms) win the AI race, I don’t see this going away anytime soon,” he added.

Agarwalla explains that 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.”

Why India is hard to monetise

India’s digital economy has long been characterised by massive scale and weak monetisation. According to Sunil Kharbanda, founder & COO of Trezix, a Surat-based technology innovation company, 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: Measurable outcomes; Efficiency gains; Compliance requirements and 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

For start-ups, 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 start-ups, 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.

While the “subscription wall” is tough to scale for 100 million general users, the real money in India’s AI space is in Infrastructure-as-a-Service and Outcome-based B2B solutions. Companies are learning that in India, you don’t charge for the tool; you charge for the work the tool accomplishes.

Data-heavy; moolah light

India has emerged as one of OpenAI’s fastest-growing artificial intelligence (AI) markets, with weekly active users having quadrupled in the past year. An in-depth analysis of this issue by Barkha Mathur of Business Standard says that India is now the company’s second-largest market behind the US, particularly in coding, reasoning and data-heavy tasks.

CEO Sam Altman confirmed that India has over 100 million weekly active ChatGPT users. But this is no longer just a usage story. For OpenAI, India matters not just in terms of data, but also as a test of whether AI can become a real revenue business outside the Western markets.

For investors and the broader ecosystem, the stakes are clear: If it works, it will become a blueprint for scaling across emerging economies; If it fails, AI revenues remain concentrated in developed markets.

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.

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.