Indian retail is in the middle of a quiet reset. Store expansions, festive campaigns and online discounts still matter, but a growing share of the work now happens in the background through artificial intelligence. Product recommendations update in real time, prices and offers adjust to demand, and service conversations flow between chatbots and call centres without the customer noticing the handover. For retailers, the question in 2025 is no longer whether to use AI, but how far journeys can be automated without losing the human touch.
The shift is happening on top of a large and complex base. Industry estimates suggest that India’s retail market is on track to approach 2 trillion dollars by 2030, up from a little over 1 trillion dollars in the mid 2020s. Within that, e commerce is set to cross 300 billion dollars in the early 2030s, supported by higher broadband penetration and digital payments. Surveys of retail decision makers show that a very high share of Indian retailers have either deployed AI in at least one business function or expect to do so soon, and more than half report a clear impact on sales or efficiency. These numbers explain why autonomous journeys are now part of boardroom conversations and not just innovation decks.
Consulting firms that track the sector point to a structural change in how decisions are made across retail. Puneet Mansukhani, Partner, Advisory Services at KPMG in India, has argued that retailers who embed AI at the core of their strategy will not only enhance operational efficiency but also elevate customer experiences and set new benchmarks for the industry. His view reflects a broader mood. AI is not treated as a side project. It is being wired into merchandising, marketing, store operations and service, so that journeys can continue without waiting for a human to intervene at every step.
At a practical level, the 24 by 7 journey starts with discovery. For fashion and beauty platforms, AI models now analyse browsing behaviour, search queries and purchase history to surface products that are more likely to fit a customer’s style and price comfort. A shopper who has previously bought affordable ethnic wear is less likely to be shown only premium western brands. If the person lingers on a particular colour or cut, the next set of recommendations adapts within the session. Beauty players such as Nykaa have used experiment led approaches to AI powered advertising and personalisation, with senior marketing leaders emphasising the value of an “experiment first mindset” in testing new models and creatives before they scale them. The outcome is a feed that feels curated rather than generic, even late at night when no human stylist is online.
Grocery and quick commerce apps have become one of the clearest examples of autonomous journeys at scale. Demand forecasting engines look at past orders, local weather, time of day and even festival calendars to predict what a neighbourhood might need, from milk and snacks to cleaning supplies. The same systems guide inventory in dark stores and determine which warehouse should serve a particular pin code. Routing engines then calculate the most efficient path for last mile riders. For customers, the visible part of this chain is a promise of ten to twenty minute delivery and a status screen that updates in real time. Behind the scenes, AI is trying to minimise stock outs and late orders without adding more people to the control room.
Large chains that operate both online and offline are using AI to knit those worlds together. Apparel and electronics retailers now use central customer data platforms that combine point of sale data from stores with app and website behaviour. When a loyalty member walks into a mall outlet, staff can see what that person recently browsed online and which offers have worked in the past. This context feeds into cross sell prompts on the sales associate’s device and, later, into email or app notifications. The result is a loop where store visits influence digital journeys and vice versa, rather than each channel operating in isolation.
AI is also being used inside stores in less visible ways. Computer vision systems monitor footfall, dwell time and shelf interactions to understand which aisles attract attention and where bottlenecks form. Heat maps help store managers decide where to place promotional displays or which layouts encourage longer visits. In some high traffic outlets, automated inventory checks use cameras or sensors to spot empty shelves and alert staff before a customer has to complain. These tools are marketed as part of “smart store” solutions, but at heart they are attempts to keep the physical environment in sync with the promises made online.
On the service side, conversational AI is taking on a growing share of first line support. Retailers in categories such as fashion, electronics and home care now run chatbots inside their apps, on messaging platforms and on their websites. These systems answer questions about order status, returns, warranty and product features at any time of day. When a query is too complex, the conversation is handed over to a human agent, often with a full transcript so that customers do not have to repeat themselves. Over time, the transcripts are used to train the models on new intents and edge cases. The goal is not to remove humans from service entirely, but to reserve them for situations where empathy and negotiation matter.
Indian founders in the AI and retail automation space argue that this is part of a wider transformation. Ashwini Asokan, co founder and chief executive of Mad Street Den, has spoken about a paradigm shift in retail and the need for brands to adapt to a new experience economy where every interaction matters, with AI as a driving force behind that evolution. Her company’s Vue.ai products are used by retailers globally for tasks such as automated cataloging, visual search and personalised merchandising. The underlying message for Indian brands is that automation is no longer limited to back office tasks. It shapes how stories are told, how shelves are arranged and how offers are sequenced across channels.
Direct to consumer brands are also leaning on AI as they expand offline. The Sleep Company, a comfort tech brand that began online and now operates more than a hundred stores, uses a “research online, purchase offline” strategy where digital content and performance campaigns capture demand that often converts in physical outlets. In recent interviews, chief marketing officer Ripal Chopda has spoken about using AI for creative content and automation, with machines handling a significant portion of routine work while humans focus on brand narrative and differentiation. The model illustrates how D2C players are building autonomous awareness and consideration journeys that eventually lead to store visits, trial and purchase.
Autonomy in journeys is not only about individual transactions. Long term customer value also depends on how well brands can anticipate needs. Loyalty programmes, subscription models and replenishment reminders are being reworked with AI to predict when a customer might run out of products, when interest in a category might be rising, or when a person shows signs of disengagement. Retailers in categories such as baby care, fitness and personal care now send nudges that are tied to life stages or usage patterns rather than only to calendar events. If a shopper has not opened the app or email in several weeks, models can recommend a different call to action or a softer contact pattern to protect goodwill.
For many Indian retailers, the barrier is less about interest and more about capability. Mid sized regional chains often run on legacy billing systems and fragmented databases. Stitching these into a single view of the customer is difficult and can take years. Global and Indian martech providers now offer cloud based suites that promise to abstract some of this complexity through plug and play connectors, ready made customer journeys and template reports. The degree of automation available to a retailer therefore depends on how quickly its technology stack can be modernised and how much change its teams can absorb.
Data quality and permissions are another constraint. Autonomous journeys rely on accurate, consented data flowing between systems. India’s data protection law has put sharper focus on whether brands have a clear legal basis for processing customer information and whether they can demonstrate that consent was specific and informed. Retailers have responded by revisiting how they collect data in stores and online, simplifying preference centres and tightening who can access what. Journey orchestration tools increasingly come with built in checks that prevent messages from being triggered for customers who have opted out, and that cap the number of contacts in a given period.
The more AI enters daily operations, the sharper the questions about bias and fairness become. If recommendations always push higher priced items, or if credit based offers are shown unevenly across neighbourhoods, the risk is not only regulatory but reputational. Some Indian retailers have begun adding human review for new AI models, including checks on whether promotional pressure varies across demographic lines in ways that are hard to explain. Others are using synthetic data and controlled experiments before rolling out new decision engines widely.
Despite the language of autonomy, retailers and analysts consistently stress that humans remain central to experience design. Store staff, merchandisers and marketers still decide which journeys to build, which voice the brand should use and where the boundaries for AI intervention should sit. Many customer journeys in India still cross cash counters, regional languages and informal negotiations that are hard to reduce to code. The most successful implementations so far treat AI as an invisible assistant that supports these realities rather than trying to replace them.
Looking ahead, retailers expect more of the customer journey to be handled by machines that talk to each other. Inventory management, price optimisation, route planning, creative testing and even basic product descriptions are likely to become more automated and more predictive. The competitive edge will lie in how coherently these systems are connected, how responsibly they treat data and how well they are tuned to India’s diverse consumer base. In that sense, the move to 24 by 7 autonomous journeys is less about technology replacing people and more about technology keeping pace with customers who now expect retail to be always on, responsive and relevant, regardless of channel or time of day.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.
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