The enterprise artificial intelligence landscape is moving beyond the question of which AI model is the most powerful, with businesses increasingly focusing on where AI should operate, which tasks it should handle, how data should be managed and what it costs to run at scale.
The shift reflects a broader move from AI as a standalone chatbot or software tool toward AI as part of an organisation’s underlying technology infrastructure. Growing investments in computing capacity and AI infrastructure are highlighting this change. In July, HCLTech announced financing of ₹14,257 crore for an AI data centre in Bhubaneswar through Sarvam AI, signalling the scale of infrastructure being developed around enterprise AI in India.
Data Security Shapes AI Deployment
For businesses handling sensitive information, the choice of AI model is closely linked to how and where the technology is deployed. Financial institutions, insurance companies, healthcare organisations and government bodies need to consider factors including data localisation, security, verifiability and regulatory requirements.
As a result, enterprises may require different deployment models, including public cloud, private cloud and, in some cases, on-premises infrastructure. Sarvam is pursuing this approach through cloud, private cloud and on-premises deployments. The company has raised $234 million as part of a $300 million Series B round, giving it a reported valuation of $1.5 billion.
This suggests that India’s opportunity may not depend solely on developing a model capable of matching every global frontier system. Instead, locally relevant infrastructure and deployment options could help Indian businesses maintain greater control over how AI is implemented.
Global and Local Models Could Work Together
The enterprise AI market is also seeing increased activity from global AI companies. Anthropic has said India is its second-largest Claude.ai market after the US, with an average usage share of about 5.8%. Research cited by ETCIO indicates that Claude users in India have significant professional and technical usage, including computer and mathematics-related tasks.
For Indian enterprises, this could result in a combination of global and local AI models rather than reliance on a single system. Global models could be used for complex reasoning or programming, while local models could be deployed for applications requiring greater regional relevance.
Cost Becomes a Key Factor
As AI adoption expands across organisations, the cost of running these systems is becoming increasingly important. While accessing an AI tool may appear inexpensive or free during initial experimentation, enterprise-scale deployment involves expenses related to computing, networking, storage, integration, energy and security.
The cost difference becomes more significant when AI moves from limited employee trials to thousands of users or automated systems operating continuously. Consequently, enterprises may increasingly evaluate models based on the cost of producing a useful business outcome, rather than relying solely on model performance rankings.
India’s AI Opportunity Lies in Local Relevance
The discussion also points to an opportunity for India to develop AI applications tailored to local languages, businesses and public services, rather than focusing exclusively on building the world’s largest AI model.
Sarvam says its AI platform handles more than two million conversations per day, while its voice systems process more than half a million hours of audio each month. These applications highlight the potential value of AI adoption driven by local relevance and usage.
The emerging enterprise approach is therefore unlikely to involve one AI model replacing another. Businesses could instead use multiple systems based on specific requirements—for example, one model for coding and technical work and another for customer-facing applications in India.
The enterprise AI competition is consequently shifting from identifying a single “best” model toward building an AI technology ecosystem that balances intelligence, cost, governance, deployment requirements and business context.
