Gen-AI rivals Google, Microsoft, OpenAI and Anthropic are set for an AI-shaping contest for India Inc

Winning revenue from Indian enterprises will soon become one of their most consequential battlegrounds. The reason: India combines a vast base of digitally ambitious companies with price sensitivity, a deep developer workforce and industries eager to automate knowledge work.

For model providers, success here does not simply mean selling software. It means embedding their models into the daily workflows of companies.

The early manoeuvring already reveals different strategic instincts. Google is leaning on the relationships it cultivated through its startup programmes and developer ecosystem. Many startups experimenting with AI infrastructure are familiar with Google developer tools and cloud platforms. By bundling Gemini models with Google Cloud services, it aims to make GenAI feel like a natural extension of the tools already in use.

Google Cloud and developer programmes emphasize credits, model access and engineering support designed to reduce the friction of experimentation. For startup founders with tight budgets, such bundling can matter as much as model performance.

Microsoft is pursuing a strategy that mirrors its success with enterprise software platforms. Azure OpenAI services are integrated tightly into its Azure cloud stack, allowing companies to access OpenAI models through Microsoft’s enterprise infrastructure.

Microsoft also benefits from long relationships with large enterprises and technology service providers in India. Many companies already rely on Azure infrastructure for part of their workloads. When these organizations experiment with AI copilots or internal automation tools, the path of least resistance is to stay within that ecosystem.

OpenAI has adopted a different tactic in India by competing aggressively on pricing. Its strategy reflects a familiar pattern, where companies adjust pricing to accelerate adoption in large but cost-sensitive economies. For developers, freelancers and small businesses experimenting with GenAI, even a modest price difference can determine which tool becomes the everyday default. Small differences scale quickly in a country with millions of potential users.

In the past, Perplexity partnered with Airtel to offer its Perplexity Pro service free for a limited period to the telecom company’s subscriber base. Technology coverage in Mint described the move as an attempt to seed familiarity with AI-powered search. The logic echoes earlier internet strategies in India, where companies first focused on scale before revenue.

Anthropic occupies a different position in this contest. Its approach emphasizes building specialized tools aimed at professional communities. It has promoted products such as Claude Code for developers, positioning it as a programming assistant integrated into coding workflows. It also supports Claude in Excel through integrations that allow the model to assist with spreadsheet-heavy financial analysis.

In a country where financial services rely heavily on spreadsheet-driven processes, such specialization may be a differentiator.

The structure of India’s enterprise landscape adds another layer of complexity. Large IT services firms such as Infosys, TCS and Wipro function as tech intermediaries for clients. Their core business model has long relied on large teams of engineers billing clients by the hour or by project size. That model depends heavily on labour (and its scale). GenAI introduces an uncomfortable dynamic.

When software development productivity rises sharply, fewer engineers may be required to deliver the same outcome.

Evidence of this shift is already emerging. A survey by EY India reported by Reuters suggests GenAI could increase productivity in India’s IT services sector by 43 to 45% over the next five years, with particularly large gains in software development and business process outsourcing roles. This is good for clients but not for firms whose revenue scales with headcount.

If a project that once required 50 programmers can be completed by 20 with AI assistance, billing models built around manpower weaken. Services firms understand this and are experimenting with outcome-based pricing to capture more value.

Switching costs between GenAI models are modest. Developers can move workloads between model interfaces with limited adjustments to code and orchestration frameworks discussed widely in developer communities allow experimentation across models. This lets enterprises test several systems before committing to one.

Yet, technology markets can’t be fluid forever. As companies build internal applications, integrate models into operational systems and train workflows around specific tools, the cost of switching inevitably rises. A bank that embeds AI into compliance systems or a manufacturer that integrates it into supply-chain planning cannot easily replace the underlying model without disruption.

India, therefore, represents something rare in the global tech economy. It is a vast market where the rules of engagement for GenAI are still being written. Technology pioneers often treat such environments as strategic laboratories. The GenAI race may follow the same path. This contest is open and every model provider wants a foothold in the workflows of Indian industry.

The author is co-founder of Siana Capital, a venture fund manager.