13 March 2026, Mumbai
India’s retail sector is on the cusp of an unprecedented growth. As per the ‘Winning Codes for Retail 2035’ report, a joint study by the Boston Consulting Group and the Retailers Association of India, the domestic market, currently valued at Rs 95 trillion in 2025, is projected to more than double to Rs 215 trillion by 2035. This aligns with India’s broader economic growth, underpinned by a projected 8 per cent GDP growth rate, positioning the country to as the world’s third-largest economy. Yet, beneath the headline growth numbers lies a subtle but profound shift. For fashion and apparel giants, the challenge is no longer just scaling store counts or shelf space, it is about establishing contextual relevance in the consumer’s life.
From floor space to consumer context
The days of linear expansion, where success was measured purely by store count or square footage, are changing. The BCG-RAI study notes that the growth differential between organized retail players and the broader market has narrowed, particularly in offline channels. In other words, simply getting bigger no longer guarantees market leadership.
Modern consumers are highly selective and highly contextual. Their purchase decisions are driven by immediate needs, convenience, and a frictionless shopping experience. P Venkatesalu, CEO, Trent Ltd explained at the Retail Leadership Summit that the market now moves in peaks and troughs, demanding continuous reinvention of desirability rather than static expansion. Brands must now decode micro-moments what the shopper wants, where they want it, and how quickly they can obtain it.
Generative AI, from pilot projects to profit engines
Technology, particularly generative AI, is emerging as a game-changer for Indian retail. The BCG-RAI report highlights that 42 per cent of urban consumers are already influenced by AI-driven product discovery, indicating the rise of ‘Agentic Commerce’ a world where AI agents not only recommend but also facilitate purchases.
For fashion retailers, this is no longer a novelty. It is a transformation. End-to-end AI deployment can deliver efficiency gains of 40 per cent to 60 per cent, a significant leap compared to the 10 per cent seen in isolated pilots. This is particularly critical in apparel, where unprofitable stores, often termed the persistent tail account for 28 to 40 per cent of networks. AI is now being used to optimize shelf velocity, predict inventory demand, and maintain product freshness, tackling inefficiencies that have historically weighed down margins.
The hyper-local opportunity
The growth frontier for Indian fashion is moving beyond metros and Tier-II cities. Retailers are betting on Tier-III, IV towns, but with a new densification-driven strategy. Rather than opening a single large-format store, brands like Zudio and Trent are establishing clusters of micro-market stores within smaller townships to capture localized demand efficiently.
These younger, digitally connected consumers in smaller cities increasingly follow global trends in real-time. Supply chain agility becomes critical; even a 15-30 day lag in store drops can erode relevance. Leading brands are integrating quick-commerce capabilities, treating essentials like white shirts or activewear as instant-need items deliverable within minutes, effectively bridging the urban-rural accessibility gap.
The playbook for winning in 2035
The BCG-RAI report offers a blueprint for traversing India’s fragmented retail environment. The important insight emphasizes AI-led operational excellence, scaling digital-first D2C brands, and leveraging the projected 10.6 per cent CAGR in consumption, which is poised to outpace global peers such as China and the US.
For India’s 500-billion-rupee retail entities, the roadmap is clear: move beyond linear growth metrics, embrace hyper-local strategies, and embed technology into the core of operations. Retailers that can synchronize relevance, convenience, and efficiency will not only survive but thrive in a Rs 215 trillion market ready to redefine the global retail hierarchy.
