Data is not a moat: What India’s CTOs say will define the next tech edge

A technology moat is a durable advantage that competitors cannot easily copy. In an AI-driven market, simply owning exclusive data may not create that protection. Data becomes valuable only when a company can understand it, apply it to a real problem and build dependable products around it.

Innovaccer illustrates the point in healthcare. Information is scattered across medical records, billing systems and medication databases. Connecting these sources is only the first step. The harder task is deciding what should happen next. That requires a context and intelligence layer that understands workflows, exceptions and operational needs.

The article’s broader message is that data must be combined with industry expertise, customer understanding and strong execution. Healthcare products must work reliably across hospitals and providers. Companies that build this practical intelligence, rather than merely collecting proprietary information, are more likely to create advantages that are difficult to replicate.

Four technology leaders made the case for a more practical Indian technology advantage. Nitin Jain, CTO of ShareChat and Moj; Ajit Narayanan, CTO of Angel One; Srividhya Srinivasan, CTO of Amagi; and Ankit Maheshwari, founder and CTO of Innovaccer, spoke at TechSparks 2026.

Their panel, titled The New CTO Playbook: India’s Next Tech Advantage, focused on how companies can compete as AI tools become easier to access. Jain discussed language, culture and recommendation systems. Narayanan focused on trust in financial technology. Srinivasan described building media products for complex markets. Maheshwari explained why healthcare data needs an intelligence layer.

Together, they identified a shared principle. AI is not a lasting advantage by itself. Companies must connect it to specialised knowledge, customer behaviour, reliable systems and real-world execution. India’s technology edge will therefore depend on solving specific problems better and scaling those solutions effectively.

The clearest scale marker is ShareChat and Moj’s roughly 200 million monthly active users, cited by CTO Nitin Jain. That audience is not uniform. Indian technology companies must serve people with different languages, cultural preferences, devices, network conditions and ways of using products.

This creates a demanding design problem. A service must offer experiences comparable with global platforms while working within India’s cost constraints. ShareChat and Moj use audience behaviour to improve recommendations and identify emerging preferences. Their systems must also remain flexible enough to support varied users and content formats.

The article does not provide a combined figure for India’s entire technology user base. It presents diversity as the more important challenge than one overall number. Companies that learn from this complexity can expand faster, build stronger products and potentially carry solutions developed for India into other markets.

India is difficult because technology companies cannot design for one uniform user or operating environment. Language and cultural differences shape what people watch, understand and prefer. Device types and network quality affect how products are accessed. These variables make a single, rigid product less effective.

ShareChat and Moj show how companies can respond. Nitin Jain said the platforms use audience behaviour to improve recommendations and spot emerging preferences. Their technology must deliver experiences comparable with global platforms while respecting India’s cost constraints. Supporting varied users requires flexible systems, not one fixed experience.

That complexity can become an advantage. A company that learns to build for different languages, devices, networks and preferences develops technology suited to difficult conditions. Those capabilities may help it expand faster. Amagi similarly used India’s complex media environment, including languages, subtitles, demographics and advertising formats, to develop products for global markets.

Incorrect or unauditable AI decisions can create direct harm, especially when money, treatment or essential operations are involved. The risk is not only that a system gives the wrong answer. People may also be unable to understand what happened, reverse it or verify responsibility.

Angel One’s example makes this concrete. If a trading customer receives no acknowledgement, they may not know whether an order was processed. Retrying could create duplicate positions and potential losses. Narayanan said automation should be judged by the cost of an incorrect decision, whether it can be reversed and whether it can be audited.

Healthcare presents a related problem. Fragmented information must lead to the right next action across hospitals, providers and complex processes. General-purpose AI may not work reliably without knowledge of workflows and exceptions. The panel therefore supported meaningful safeguards and human oversight, rather than oversight that merely assigns blame after failure.

If data is not durable by itself, companies need to build advantages around how they use it. The panel highlighted deep industry knowledge, customer understanding, trust and execution. These strengths help a company solve problems in ways that generic tools cannot easily reproduce.

Innovaccer’s healthcare example shows the mechanism. Data from medical records, billing and medication systems is fragmented. A valuable product must connect it, understand healthcare workflows and exceptions, and determine the next operational action. ShareChat applies a similar learning loop by studying audience behaviour, testing content ideas and refining recommendations.

Trust is especially important in financial services. Angel One’s Ajit Narayanan said safeguards, accurate information and accountability must be designed into systems. The forward advantage will belong to companies that combine AI with specialised knowledge and dependable delivery. Talent, cost and scale remain foundations, but execution determines whether they become a lasting edge.

AI systems use data to identify patterns, but useful results depend on what the data represents and how it connects to a real task. User behaviour can reveal preferences. Industry knowledge explains workflows, exceptions and operational requirements. Together, these inputs help systems produce more relevant recommendations or actions.

ShareChat provides a clear example. The platform analyses how users engage with videos and other formats. It uses those signals to test content ideas, understand audience preferences and refine recommendations. Feeding responses back into the system creates a continuous improvement cycle. In healthcare, Innovaccer’s proposed context and intelligence layer would connect fragmented records and help determine what action should follow.

The article presents this as a practical advantage, not an automatic result of using AI. Systems must work reliably in their operating environment. They also need safeguards, especially when decisions affect finances or healthcare. Better outcomes come from combining data, feedback, domain knowledge and careful execution.