Indian enterprises are moving more artificial intelligence projects from experimentation into production, shifting the focus from choosing AI models to building the data, security and governance infrastructure needed to operate them reliably.
The trend was highlighted by Elastic’s India leadership in a discussion reported by Express Computer on October 6, 2026. Atul Ahuja, Area Vice President and General Manager, India, said organisations are increasingly tying AI initiatives to specific business outcomes rather than treating them only as proof-of-concept exercises.
Why enterprise AI is moving beyond pilots
For companies deploying AI in real business workflows, access to a capable model is only one part of the problem. Enterprise systems often contain information across databases, PDFs, scanned documents, images, video, audio and older applications.
That fragmentation makes it harder for AI applications and agents to retrieve the right information at the right time. It can also increase the amount of context sent to models, affecting both cost and performance.
Data readiness is becoming the bigger challenge
Elastic’s view is that production AI depends on making enterprise information searchable and usable without necessarily moving every data source into one central repository. Its platform supports retrieval across distributed data through APIs and search capabilities.
This reflects a broader shift in enterprise AI architecture. Instead of asking only which model performs best, technology teams increasingly have to consider data quality, retrieval, permissions, observability and how much context an AI system actually needs before it takes an action.
Security and governance matter more in sensitive sectors
The issue becomes particularly important for sectors such as banking, financial services, government and healthcare, where enterprise systems may contain sensitive or regulated information.
According to Elastic, Indian organisations are therefore considering security and data-governance requirements alongside AI deployment. The company also points to observability and security tooling as part of the infrastructure needed to monitor systems and respond to incidents.
Elastic’s own recent work in India has similarly positioned search, observability and security as foundations for production agentic AI. The company has argued that AI agents need governed access to enterprise context before they can be trusted with operational workflows.
India’s AI adoption is already moving into production
The shift is not limited to one technology vendor. Deloitte’s 2026 India insights found that Indian enterprises reported higher levels of significant or full AI usage than the global average, with adoption extending across functions including product development, strategy and operations, marketing and sales, and supply chain.
That does not mean every AI deployment is mature. Instead, it suggests that Indian businesses are increasingly testing where AI can produce measurable operational or commercial value while dealing with the infrastructure and governance requirements that come with deployment at scale.
What this means for technology teams
As AI moves into production, enterprise technology teams are likely to spend more time on data integration, retrieval systems, access controls, monitoring and security rather than treating the AI model itself as the entire product.
For Indian organisations, the challenge is particularly significant because large enterprises often operate across hybrid infrastructure, multiple applications and highly regulated datasets. Building reliable context and governance around those systems could become as important as selecting the underlying AI model.
Sources
- Express Computer, October 6, 2026.
- Elastic: Forging India’s Digital Future, August 26, 2026.
- Deloitte India, State of AI in the Enterprise 2026 India insights.
Featured image: Representative technology/enterprise image. Photo by A Chosen Soul via Unsplash.
