ET Soonicorns Summit 2025: How AI is personalising shopping in India

The future of retail isn’t just about selling products, but about anticipating what customers need before they know it themselves. While most retailers are still stuck in the “recognise and recommend” phase of personalisation (read: suggesting products based on past purchases), a new wave of AI-driven experiences promises to fundamentally reshape how Indians shop, both online and offline.

The transformation is already underway at companies like Reliance Retail and Razorpay. What they’re discovering is that true personalisation goes far beyond showing relevant product recommendations; it’s about understanding intricate behavioural patterns that vary dramatically across India’s diverse markets. These and other key insights emerged from the dialogue ‘AI-Driven Personalisation and Omnichannel Commerce: The Next Retail Battleground’. Featuring Razorpay COO Rahul Kothari and Ankit Gupta, VP – Head of Product & Tech, Reliance Retail, it was one of many panel discussions, fireside chats, keynotes, and presentations at the fourth edition of the ET Soonicorns Summit on August 22 in Bengaluru.

The complexity hidden in simple choices

Consider something as basic as clothing sizes. For Ankit Gupta, the challenge of stocking the right inventory reveals the sophisticated data science now required for physical retail.

“The demand for XS (extra small) size in Meghalaya, for example, is quite different from that in Punjab,” he explained, highlighting how regional preferences create complex optimisation problems that require AI to solve at scale.

This isn’t just about knowing what sells where, but about predicting when it will sell. Festive seasons, regional celebrations, and even weather patterns create demand spikes that can make or break a quarter’s performance. Managing inventory across hundreds of stores while avoiding both stockouts and excess inventory requires the kind of predictive capabilities that only AI can deliver.

The complexity multiplies when retailers try to bridge online and offline experiences. Unlike e-commerce, where shelf space is infinite and recommendations can be endless, physical stores operate under strict spatial constraints. Every square foot must generate maximum revenue, turning personalisation into a high-stakes optimisation challenge where wrong decisions carry immediate financial consequences.

The age of anticipation
Razorpay’s Rahul Kothari envisioned a retail future that moves beyond reactive personalisation toward proactive assistance. “Imagine a grocery app that knows I usually buy three days’ worth of groceries on Sundays. It should anticipate that and proactively recommend what I need,” he said . But even that scenario represents just the beginning of what’s possible.

The real breakthrough will come when different apps start working together to understand complete life contexts. A fitness app tracking your health goals could communicate with your grocery app to automatically surface ingredients that align with your diet plan. Such cross-platform AI collaboration transforms shopping from a series of isolated transactions into an integrated support system for various lifestyle choices.

The shift from recommendation to “co-creation” represents a fundamental change in how companies think about customer relationships. Instead of trying to sell what they have, retailers are positioning themselves as partners in helping customers achieve their broader life goals.

The data dilemma: Trust vs. personalisation
The foundation of this personalised future is data, but collecting it responsibly has become increasingly complex as privacy regulations tighten and consumer awareness grows. The challenge is particularly acute in India, where digital adoption is accelerating but digital literacy around privacy remains uneven.

Both Gupta and Kothari emphasised transparency as the cornerstone of their data strategies. Customers, they said, need to understand not just what data is being collected, but why it’s necessary and how they can control its use. This requires a fundamental rethinking of how products are built.

“As organisations, we have to build the kind of muscle where data privacy is embedded in product design. Customers must be told what exactly their data is being used for,” Kothari underlined. This means building systems that handle granular permissions, allowing customers to consent to data use for recommendations while opting out of targeted marketing, or easily access, manage, and delete their information when desired.

The cookie crumbles: First-party data as competitive advantage
The decline of third-party cookies is forcing a fundamental shift in how retailers approach personalisation. Companies that can build direct relationships with customers and collect rich first-party data are gaining significant competitive advantages over those dependent on external data sources.

This shift is improving the quality of personalisation. AI models built on first-party data tend to be more accurate and less prone to the “creepy” personalisation that makes customers uncomfortable, Gupta said. Companies like Reliance, which has massive amounts of transaction data from millions of customers, can build incredibly sophisticated engines that smaller competitors struggle to match.

“First-party data is a moat in a world where third-party data, including cookies, is declining. AI models can also be far more customer-centric if trained on first party data,” Gupta underlined. But he added that there needs to be regulatory intervention to ensure competitive markets don’t become impossible for new players to enter.

A human touch in an AI world
Even as AI capabilities expand, the question of what should remain human-led versus automated continues to evolve. Simple interactions like trying on lipstick or visualising furniture placement can now be handled through AR and VR technologies. Reliance has partnered with companies that create 3D replicas of luxury stores, allowing customers to browse virtually before visiting physically.

However, certain experiences remain stubbornly analogue. The need to touch fabric, smell perfume, or feel the weight of an object can’t easily be replicated digitally. Rather than viewing this as a limitation, successful retailers can use this as an opportunity to create hybrid experiences where digital discovery leads to personalised offline consultations.

The next battleground
Looking ahead, the next phase of AI in retail will focus on connecting personalisation directly to operational efficiency. This means AI systems that can forecast demand, optimise inventory, and determine product placement with enough precision to dramatically improve profitability across entire retail networks.

The ultimate vision is hyper-personalised content delivered at scale, with unique brand messaging and product recommendations tailored to each individual customer’s current life context and goals. This requires AI systems sophisticated enough to understand not just what customers have bought, but where they are in their life journey and what they’re trying to achieve.

For Indian retailers, this represents both an enormous opportunity and a significant challenge. The companies that can master AI-driven personalisation while maintaining customer trust and regulatory compliance will dominate the next decade of retail growth. Those that can’t risk becoming irrelevant as customer expectations shift toward experiences that feel truly personal rather than merely customised.

The era of one-size-fits-all retail is ending. What’s replacing it is something far more complex, but potentially far more valuable: a shopping ecosystem that understands you well enough to be genuinely helpful rather than just convenient.

360 ONE is the Presenting Partner of the ET Soonicorns Summit 2025, with Shiv Nadar University as the Ecosystem Partner, Raymond as the Wardrobe Partner, Pi42 as the Gold Partner, Bank of India as the Banking Partner, Tracxn as the Knowledge Partner, and K-Tech Startup Karnataka as the State Partner. The Gifting Partners of the Summit are The Mind & Company, Plum, Clinikally, EM5, and True Elements.

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