Ramanujam Komanduri, Country Manager India At Everpure, On Why Data, Not AI Models, Will Define Enterprise AI Advantage

AI amplifies what is already in the data; it does not create enterprise advantage from thin air. As organizations move from pilots to production, data quality, accessibility, and governance matter more than the model alone. A recent IDC study found 94% of IT leaders consider data quality the most important factor in AI success. Pilots typically run on hand-picked, curated data, whereas production runs on the real data estate, which is often inconsistent, duplicated across systems, and lacks a shared understanding of what that data actually means. The model doesn’t change between those two moments; the data does.

The practical shift is to data primacy: where data becomes the enterprise’s primary asset, carrying its context and semantics, with governance embedded at the data layer. Conserving data rather than copying it reduces cost and the risk of AI using the wrong information. Everpure Data Intelligence provides discovery, governance, and AI-ready context across hybrid environments. Data Stream prepares data through a governed pipeline, keeping it on-premises where sovereignty requires it.

The backup mindset is no longer enough. Organizations need to assume the perimeter can fail and make the data layer the last line of defense. AI makes this all the more urgent. The question is not just how to prevent an attack, but how quickly the business can recover when one succeeds.

That means immutable snapshots that cannot be altered or deleted, isolated recovery environments, administrative separation, and human oversight for sensitive actions. It also means knowing which data actually matters. Data intelligence helps identify what is most critical, so recovery can be prioritized with precision.

True resilience isn’t about having more backup copies. It’s about ensuring clean, trusted data remains protected and the business can recover in hours rather than weeks.

India’s AI ambitions are putting the quality, accessibility, and governance of data firmly in focus. IndiaAI is helping build the compute and innovation ecosystem, while the DPDP Act is raising the bar for how organizations collect, manage, protect, and use data. Together, they are pushing enterprises to think about AI adoption not just in terms of compute and models, but also in terms of trust and accountability.

This makes data governance a strategic requirement, not a compliance exercise. Organizations need clear visibility into where data resides, who can access it, what it means, and whether it can be used for a specific purpose. This is the foundation of data primacy: putting data at the center, with context, governance, and security built into the data layer.

The goal is not to choose between cloud and on-premises, but to create a flexible architecture that keeps organizations in control of their data while giving AI the access it needs. That balance will be critical to scaling responsible AI in India.

The center of gravity is shifting from applications to data. For decades, data was largely organized around the application that created or consumed it. AI breaks that model because it needs access to data across systems, sources, and environments to deliver meaningful outcomes.

Data primacy puts data at the center. Instead of applications owning their own copies, data becomes a shared, governed asset, with its context and semantics intact. It can be accessed by multiple applications and AI agents without being repeatedly copied or moved.

For technology leaders, this means moving beyond infrastructure silos toward an architecture that provides visibility, governance, security, and access across where data lives. The priority is to modernize around the data itself, not simply add more applications, compute, or storage.

The answer is not to force everything into one environment. It is to manage a distributed data estate through one consistent operating model.

That means applying the same policies for visibility, access, governance, and protection across on-premises, cloud, and edge environments. A unified data plane provides consistency, an intelligent control plane automates management, and data intelligence and governed pipelines help make the right data available for AI in real time.

Ultimately, enterprises need a pragmatic hybrid approach: put data where it makes the most sense for the workload and risk without losing control or creating unnecessary complexity.

For me, it comes down to clarity, trust, and accountability. People move faster when they understand where we are going, why it matters, and what they own. My role as a leader is to provide that context and direction, then give teams the space to make decisions and take ownership.

Customer trust is equally important. In technology, especially in a market evolving as quickly as India, relationships are built over time through consistency, transparency, and delivering on what you promise. That means listening closely to customers and staying focused on outcomes rather than chasing short-term wins.