How Indian Startups Are Adopting AI And Real-Time Data To Drive Rapid Decisions

AI is entering a new and transformative era. The shift from traditional AI and ML to GenAI and streaming analytics is more than a technical leap. It has led to a full-blown business reinvention. Enterprises operating can act faster, decide smarter and enhance customer experience to hold a competitive edge. Startups, in particular, are harnessing the combined power of GenAI and real-time data analytics to accelerate innovations and disrupt incumbents.

As companies increasingly adopt AI-GenAI and its many variants, they rapidly shift from experimentation to action. According to a McKinsey survey, 78% of respondents say their organisations use AI in at least one business function, up from 72% in early 2024 and 55% a year earlier. Also, 71% of respondents report regular use of GenAI in marketing and sales, product and service development, service operations and software engineering. Similarly, per a PwC report on AI Business Predictions, 49% of technology leaders say AI is fully integrated into their companies’ core business strategy, and a third report full integration into products and services.

These trends prove the growing dominance of AI in driving business success in today’s economy.

To decode the power of AI and real-time data insights, Inc42, in collaboration with the data streaming platform Confluent, recently organised a roundtable in Bengaluru as part of its ongoing knowledge series — Executive Boardroom: Harnessing Data For Growth. The second edition of the roundtable brought together 14 tech leaders from diverse industries such as insurance, ecommerce, automotive, software development, artificial intelligence and more. The discussions centred around the key theme – How Indian Startups Are Adopting AI And Real-Time Data To Drive Rapid Decisions.

Sameer Dhanrajani, founding partner of MIRAI Ventures and CEO of 3AI, moderated the session. 

Among the participants were:

• Alok Mishra, cofounder & CTO, Goodmeetings
• Shashi Kiran. H, head, AI & application engineering, Altigreen Propulsion Labs
• Mayank Satnalika, director, AI & engineering, CloudSEK
• Pranjal Singh, staff data scientist, Udaan
• Rishabh Jain, head of engineering, Vahan.ai
• Avinash Pandey, cofounder & CTO, LILA Games
• Yash Gupta, cofounder & CTO, Nova Benefits
• Ramit Bhardwaj, CTO, IndiQube
• Dr Raja Mugasimangalam, founder & CEO, Genotypic
• Mohan Krishna Devarapalli, senior director of engineering, PayU
• Raghunandan Gupta, director of enginering, Eka.Care
• Bhargav Joshi, data scientist at Digantara
• Rohit Vyas, country presales leader (India), Confluent
• Ajay Yeluri, CTO, SID Global Solutions

AI And Real-Time Data Boost Decision-Making & Productivity

Businesses have long relied on data and traditional AI for high-impact decisions. But the rise of GenAI is a turning point. It isn’t just about automation; it’s about creating new possibilities. From generating content to parsing complex data quickly, it unlocks context-rich insights that drive intelligent workflows and better customer experiences. It is not just a new tool; it is a new way of working.

Accordingly, real-time, data-driven decisions are now table stakes for businesses navigating complex digital ecosystems. Devarapalli of PayU emphasised its criticality in the payments space, where companies use multiple payment gateways to process transactions and milliseconds matter. 

“Identifying the right payment gateway for routing each transaction is business-critical. To maximise success rates and protect margins, companies must analyse earlier patterns and live data to pick the optimal gateway instantly,” he said.

Electric Vehicles: Accelerating Innovation Cycles

EV makers also lean hard on AI and real-time data to quickly roll out new product variants. With so many sensors built into today’s electric vehicles, companies are getting constant feedback on what features people rely on and how the usage varies across geographies. That kind of insight means one can update 20-30% of the components and launch a variant in six to eight months. It is a massive leap from the historical two-year cycle ICE (internal combustion engine) vehicle makers take to bring out a new model, partly due to the lack of such data. 

EV players can even rethink the entire vehicle architecture based on what they are learning in real time.

“This shift is changing how the entire industry innovates,” said Shashi Kiran of Altigreen. 

Vyas from Confluent underscored the competitive edge organisations gain by harnessing real-time data for faster decisions and deeper insights.

“It is quite clear that fast data beats slow data every time,” he said. “When life happens in real time, why should data lag? Data is horizontal. It powers every industry and function, from application operations to cyber-resilience. Fast data isn’t just an advantage; it’s the foundation of AI.”

Three Pillars Of AI Integration: Data Governance, Risk Management & Ethics

Aligning AI with business strategy is not a box to tick. It is a dynamic, ongoing process shaped by a company’s digital maturity. In the enterprise tech world, the old model of using AI to crunch historical data is giving way to a faster, more agile approach — harnessing real-time data streams. These continuous live data flows enable businesses to move beyond hindsight and make informed decisions almost instantly. The outcome? Smart customer engagement, more resilient supply chains, and faster, proactive risk management.

Before companies can unlock the full potential of AI, they must first get their data house in order. It means building a robust data governance framework for data collection, storage, updation and access. AI models risk being ineffective and non-compliant without accurate and unbiased data.

With AI regulations still lagging, the burden of ensuring data quality and securing AI systems falls squarely on organisations. That means rigorously vetting third-party models for vulnerabilities before integration, not after.

A recent survey by Fortanix reveals the stakes: While 97% of companies plan to buy or build GenAI tools to automate operations or unlock new revenue streams, 87% of security executives admitted to breaches within the past year. The takeaway: AI security can’t be an afterthought. It must be baked into development from the start.

Pandey of LILA Games also emphasised that without strong regulations, the onus is on companies to ensure the AI tools and frameworks they use are secure. 

“For example, if you are downloading an AI model from platforms like Hugging Face, it is essential to check for vulnerabilities. Just recently, a developer testing a model for a Next.js tool found it was highly vulnerable,” he said. “The warning even mentioned the possibility of user data leaks. This highlights why developers must be extremely cautious and cannot unquestioningly adopt tools without proper vetting. Security has to be a priority from the start.”

One thing is clear. Businesses are not just adopting AI and real-time data. Technology is fundamentally changing how companies operate. What we once called ‘strategy’ is no longer a static plan. It is becoming a living, evolving discipline increasingly inseparable from the systems, data streams, and algorithmic intelligence driving decision-making.

From electric vehicles to life sciences, industries are using AI to compress innovation cycles and adapt faster than ever. But the breakneck pace has a downside. Validating, governing and complying with ethical standards while deploying these technologies at scale are becoming increasingly complex. AI unlocks new speed and insights. However, it raises profound questions regarding data integrity, compliance and the dangers of over-automation.

“Data is no longer the initial barrier,” said Jain of Vahan.ai. “But it is still the defining factor for AI product quality.” In other words, while access to data and AI tools is easier than ever, excellence now hinges on how well companies integrate, curate and govern their data.

As AI moves from predictive to generative, from static models to autonomous agents, the rules of competition are changing. Business advantage won’t come from owning the smartest model but from designing the smartest interaction system that orchestrates people, data and machines.

That is the new ‘strategy’ frontier. Winning there won’t just require better tools. It will demand sharper judgement, tougher questions and, above all, better data.