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How Startups Are Future-Proofing Tech Stacks For An AI-First World

24 Oct'25 | By Inc42 BrandLabs

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A farmer snaps a photo of a diseased crop. A patient describes symptoms during a teleconsultation. An irate customer disputes a questionable charge on a banking app. Every day, these routine interactions generate billions of unstructured data points, raising a tricky question. How can companies turn this flood of data into business intelligence and informed strategy-making?

Artificial intelligence (AI) has become the core engine powering vast volumes of data. But as businesses rush to adopt it, they have to face tough truths — disconnected systems, soaring storage bills, privacy concerns, and the constant fear of a breach.

According to Gartner, the demand for AI/ML is set to surge, with hyperscalers leading the shift by embedding AI capabilities into their infrastructure and optimising compute resources. Gartner estimates that 50% of cloud compute will be devoted to AI workloads by 2029, up from less than 10% in 2025. This fivefold increase underscores the need for organisations to reassess their data and cloud strategies to ensure they can handle the rising AI/ML demand.

This data can help generate sharper predictive insights, smarter real-time decisions and more personalised customer experiences. But it also raises pressing questions. How can companies store such vast datasets securely? How can they retrieve insights in real time? How do they ensure compliance and protect sensitive information?

These questions framed the virtual panel discussion hosted by Inc42 and cloud applications leader Oracle. The session was part of the ongoing series titled Boardroom: Powering Data With AI, where technology leaders explored the core theme: How Startups Are Future-Proofing Tech Stacks For An AI-First World.

Moderated by Kshitij Shah, CTO of Ratnaafin Capital, the discussion highlighted the promise of AI and the practical hurdles of embedding it into business functions with data at the core. The panel featured:

Gaurav Bagga, SVP and head of engineering & product (CTPO), Pristyn Care
Krithika Muthukrishnan, chief product & AI officer, Scripbox
Sachin Nandwana, cofounder & director, BigHaat
Vivek Pandey, CTO, Simpl
Ish Babbar, cofounder & CTO, InsuranceDekho
Pijush Mukherjee, head of digital natives, JAPAC, Oracle

AI At Scale: Where Opportunity Meets Complexity

AI’s promise is often measured by model sophistication, but the real challenge lies in how it works with data at scale. In sectors such as healthcare, insurance, agriculture and fintech, success depends less on algorithms and more on seamlessly managing messy, high-stakes information.

In fact, the best models across sectors improve through continuous refinement and careful recalibration. In healthtech, this can vary from turning patient interactions into accurate medical records to detecting early signs of illnesses through data analysis and insight. In insurtech, integrated frameworks can bring together information scattered across providers. In agritech, AI can also build an expanding library to help identify crop diseases early.

The interplay between structure, governance and constant improvement does not entirely wipe out operational complexity. But it helps build resilient, accountable systems capable of delivering real impact.

Take, for instance, Pristyn Care, a leading secondary-care surgical provider that simplifies surgical journeys and ensures affordability. Bagga is building an AI co-pilot to assist care co-ordinators during patient calls.

“We transcribe and translate all calls between patients and care co-ordinators. The biggest AI challenge we are trying to solve is to bring a co-pilot into that conversation, giving our people the best information at the right time,” he said. However, the goal hinges on securely managing vast troves of unstructured voice and medical data.

As for insurance, making services accessible and personal remains paramount.

“It is not a one-time sell-it-and-forget-it kind of product. It is a promise,” said Babbar of InsuranceDekho. “AI helps match customers in smaller towns (Tier III and IV) with the right policies and speeds up claim processing. But managing things across 49 insurers while complying with India’s new data protection law is no small feat.”

If data powers AI, governance provides the guardrails. Muthukrishnan of Scripbox cautioned that even a single breach could destroy a financial services company.

“Our business can end if just one piece of personally identifiable information (PII) leaks. That’s why we have normalised and masked PII data across all analytics from Day One,” she said.

Adding a platform perspective, Mukherjee of Oracle pointed to one of the toughest challenges facing enterprises today: Applying AI to sensitive datasets without breaching compliance.

“When companies already hold vast amounts of transactional data in validated databases and meet strict regulatory standards, the question becomes — how do you apply AI to that data without taking it out of those databases? Our approach is to bring AI to the database, not move data to AI,” he said.

Across the board, one truth stands out. AI is not just processing data. It redefines how organisations function, protect information and stay resilient in a changing landscape.