Indian CTOs at TechSparks 2026: Data isn’t the moat—execution is

Why data isn’t enough for competitive advantage

At the TechSparks 2026 event, technology leaders from ShareChat, Angel One, Amagi, and Innovaccer agreed that India’s tech industry must evolve beyond talent, cost efficiency, and scale to compete in an AI-driven market. According to YourStory, the panel highlighted that differentiation will depend on how effectively AI is integrated, rather than the data itself.

Nitin Jain, CTO of ShareChat and Moj, explained that India’s linguistic and cultural diversity presents an opportunity to build products for varied audiences. ShareChat, which reportedly has around 200 million monthly active users, uses AI to accelerate content production and improve recommendations by analyzing user engagement with videos and other formats.

Ankit Maheshwari, Founder and CTO of Innovaccer, stated that while proprietary data is valuable, it does not automatically create a competitive moat in healthcare. Instead, companies need an intelligence layer that understands industry workflows, exceptions, and operational requirements.

Trust and scalability as differentiators

Ajit Narayanan, CTO of Angel One, emphasized the importance of embedding trust in system design for financial services. According to the panel, AI automation in this sector must account for the cost of incorrect decisions, whether they can be reversed, and whether they can be audited. Trust, Narayanan argued, is built through accurate information, safeguards, and accountability.

Srividhya Srinivasan, CTO of Amagi, shared how the company’s experience in India’s complex media environment helped it develop solutions that later scaled globally. She noted that Amagi first built products for the Indian market before taking some of them international, demonstrating the value of solving local challenges first.

The role of execution and specialization

The panel concluded that execution and specialized knowledge are critical for gaining a competitive edge in AI. While data and technology provide a foundation, the ability to tailor solutions to specific industries, user groups, or markets is what sets companies apart. For Indian startups, this means leveraging local insights to create products that resonate globally.

Context for India’s AI push

India’s startup ecosystem has seen a surge in deeptech initiatives, with accelerators like Peak XV’s Surge selecting startups for specialized cohorts. Programs like DEEP LEAP, a partnership between IIMK LIVE and Walmart Global Tech, are focusing on fostering innovation in areas such as AI and hardware.

The e-commerce sector in India is also evolving, with a shift from discovery to access for consumers in Tier II cities and beyond. Affordability and convenience are becoming key battlegrounds, reflecting broader trends in how Indian consumers interact with technology.

In financial services, platforms like Groww have strengthened their market position by adding record numbers of active clients, highlighting the growing demand for tech-driven solutions in traditionally regulated industries.

What this means

Lazyfounder analysis — our interpretation, not reported fact.

This discussion underscores a shift in how Indian tech leaders think about competition in AI. For years, the narrative was about leveraging India’s talent pool, cost efficiency, and scale. Now, the conversation is moving toward how AI is integrated—whether it’s building trust in financial services, tailoring solutions for diverse linguistic markets, or solving local problems before scaling globally.

For founders and operators, this is a reminder that technology alone isn’t a differentiator. The winners will be those who combine AI with deep domain knowledge, execute flawlessly, and design systems that earn user trust. India’s complexity—whether in media, healthcare, or finance—can be an advantage if startups treat it as a lab for innovation rather than a limitation.

Key takeaways

• Data is necessary but not sufficient for competitive differentiation in AI; execution and domain expertise matter more.
• Trust and accountability must be embedded in AI systems, especially in regulated industries like finance and healthcare.
• India’s linguistic and cultural diversity can be a strength for startups building products for varied audiences.
• Solving local challenges first can create solutions that scale globally, as seen with Amagi’s media technology.
• Founders should focus on AI’s role in accelerating workflows and improving decision-making, not just automation.

FAQ

Why isn’t proprietary data enough to create a competitive moat in AI?

According to the panel, proprietary data is valuable but does not automatically differentiate a company. Competitive advantage comes from how data is used—such as combining it with industry-specific workflows, trust mechanisms, and execution capabilities—to solve real-world problems.

How are Indian companies leveraging local diversity for global scalability?

Companies like ShareChat and Amagi are using India’s linguistic, cultural, and operational complexities as a testing ground. By building solutions that work in India’s diverse market, they create products that can later be adapted for global audiences.

What role does trust play in AI adoption for regulated industries?

Trust is critical in sectors like finance and healthcare. AI systems must be designed with safeguards, accountability, and auditable decision-making to ensure they align with regulatory requirements and user expectations.

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