India’s fashion industry is undergoing a tech-driven makeover. In a market valued at over $60 billion and growing rapidly, fashion brands are increasingly weaving artificial intelligence (AI) and digital marketing into everything from design to retail. The goal is clear: meet the expectations of today’s connected consumer and drive business growth in a competitive landscape. This shift is happening in boardrooms and boutiques alike, as brands adopt AI tools and marketing technology (martech) to enhance personalization, streamline operations, and measure results more precisely than ever before. The tone in the industry is notably neutral and pragmatic – it’s less about hype and more about what actually works on the ground, backed by data and real examples.
Importantly, Indian fashion brands are taking a measured, problem-solving approach to tech adoption. “The end objectives are defined based on what we are solving for,” says Ravi Hudda, Group Chief Digital and Information Officer at Raymond, one of India’s largest fabric and apparel retailers. In other words, technology is being deployed with specific goals in mind – whether it's improving customer experience, optimizing inventory, or increasing sales – rather than for the sake of experimentation alone. This grounded approach reflects a broader trend: AI and digital strategies are embraced not as shiny novelties, but as practical tools to address the fashion industry’s long-standing challenges and opportunities.
Personalization and the Omnichannel Experience
One of the most visible impacts of AI in Indian fashion retail is the rise of personalization and omnichannel shopping experiences. Indian consumers today expect a seamless blend of online and offline shopping – a trend often dubbed “phygital” retail. According to a recent Wunderman Thompson report, 77% of consumers in India prefer to shop with brands that have both a physical store and an online presence, underlining the necessity for integrated experiences. Shoppers might discover a product on social media or a website and then want to try it in a store (or vice versa), and they expect that journey to be smooth.
Fashion brands are responding by using AI-driven recommendation engines and personalization algorithms to make shopping more tailored to individual tastes. When a customer browses a retailer’s website or app, AI analyzes their behavior and purchase history to suggest relevant products in real time. In stores, sales staff armed with tablets can access the same customer profiles to recommend items, creating a continuous experience. Many retailers have also introduced AI-powered chatbots and virtual stylist assistants on their e-commerce sites and messaging platforms. These digital assistants provide style advice, answer product questions, and offer instant customer support 24/7. By learning from each interaction, they improve over time, making suggestions that feel more and more personal. Such real-time assistance not only boosts online conversion rates but can also influence in-store decisions, as customers come in already informed and confident about what they want.
A good example of the omnichannel approach is the “endless aisle” strategy now common among Indian fashion retailers. Brands like Shoppers Stop, Pantaloons, Lifestyle, Westside and others encourage shoppers in their brick-and-mortar stores to explore extended inventory online if something isn’t available on the rack. Instead of leaving the store empty-handed when a size or style is out of stock, customers can scan QR codes or use in-store kiosks to browse the retailer’s website for additional sizes, colors, or related items, and even order them on the spot. This integration of digital inventory with physical retail ensures that a customer’s desire can be fulfilled through any channel. It’s a direct response to an AI-informed insight: lost sales due to stock limitations can be reduced by showing customers everything that’s available, not just what’s in that particular store.
For legacy brands embracing modern tech, building a unified view of the customer is crucial. Raymond, for instance, has invested heavily in AI and martech tools to link its online and offline operations. Hudda notes that the company now runs a fully integrated loyalty and rewards program across its stores and e-commerce platform, powered by data. This kind of unified customer relationship management allows Raymond to track preferences and purchases across channels. With AI-driven insights from this data, the brand can tailor promotions to each customer’s behavior, whether it’s offering personalized discounts on a shopper’s birthday or recommending matching accessories based on a recent clothing purchase. The result is a more cohesive journey for the consumer and higher engagement for the brand. “The brand actively uses AI-driven insights to improve customer experience, refine recommendations, and manage inventory more efficiently,” Hudda explains, highlighting that connecting data across channels helps streamline operations and personalize marketing efforts in tandem.
Crucially, brands are keeping the focus on customer convenience and satisfaction as the measure of success in personalization. “Ultimately, we measure success by how effortlessly our customers shop with us, whether online, in-store, or through a blend of both,” says Ekta Dutta, Head of Marketing at BIBA, a leading Indian womenswear brand. Dutta notes that creating a cohesive omnichannel journey has been a priority, ensuring that a customer can transition from a mobile app to a mall visit without feeling a disconnect. If shoppers can find what they need without friction – say, by reserving an item online for trial in-store, or returning an online purchase at a store counter seamlessly – then the tech is doing its job. It’s a customer-centric definition of success: convenience is king.
Data-Driven Marketing and Customer Engagement
Behind the scenes, AI is also transforming how fashion brands in India plan and execute their marketing strategies. The modern marketing stack for a fashion brand often includes an array of tools – analytics dashboards, customer data platforms, automated campaign managers, social media listening software – and increasingly, AI is the intelligence threading them together. Brands are leveraging these martech tools to refine audience targeting, personalise content, automate routine tasks, and ultimately engage customers in more meaningful ways.
One major advantage of digital marketing is measurability, and AI amplifies this by sifting through heaps of data to reveal patterns. “We focus on key performance indicators such as customer engagement metrics, sales and conversion rates, repeat purchases and customer loyalty, omnichannel performance, and operational efficiency that reflect both business growth and customer satisfaction,” says BIBA’s Ekta Dutta. In the past, a marketing team might run a campaign and only guess at which half of it worked; now they can track precisely how a Facebook ad or an influencer collaboration translates into website traffic, store footfall or sales. AI helps by attributing outcomes to specific actions – for instance, analyzing whether a push notification about a new collection drove a spike in app purchases, or if an AI-personalized email resulted in higher repeat orders from certain customer segments.
Dutta adds that her team digs even deeper into the data to connect the dots between digital touchpoints and real purchases. “By analysing online and offline sales influenced by tech-driven touchpoints, we measure how effectively innovations translate into purchases,” she explains. If BIBA runs a campaign where a customer first interacts with the brand via an Instagram post, then visits the website, and finally buys in-store, AI-enabled attribution models can credit each step in that journey. Metrics like loyalty program participation, frequency of repeat orders, and uptake of personalised offers are closely watched, Dutta says, as they indicate long-term customer retention driven by these digital initiatives. This analytical rigor ensures that fancy new tools are not just used for show – they must prove their worth in numbers.
Indian fashion brands are also experimenting with AI in marketing to tackle perennial challenges like customer churn and returns. Vijay Shenoy, Head of Strategy & Consulting at digital agency Langoor, points out that fashion retailers have historically grappled with high return rates and difficulty retaining customers in the long run. Technology is helping on both fronts. “Some of the key pain points of fashion retail have been product returns and customer retention. Hence, while tech solutions such as personalised product try-ons and 3D product visualisation have enabled lower return rates, some of the martech solutions have focused on increasing customer engagement and retention rates,” Shenoy explains. Virtual try-on tools – which use augmented reality and AI to let shoppers see how a garment might look on them without physically trying it – are becoming more common on e-commerce sites. By giving customers a better sense of fit and style, these tools can reduce the chance that the customer will return the item due to unmet expectations. At the same time, AI-powered customer relationship management systems can trigger timely re-engagement messages (like reminding a customer about items left in their cart, or suggesting outfits to go with a past purchase), thereby boosting repeat visits and loyalty.
Beyond these customer-facing tactics, AI is enabling marketers to be far more efficient in how they allocate budgets and optimize campaigns. Machine learning models can crunch data from advertising platforms to determine which demographic should see which ad, at what time of day, and even with which product image – all to maximize the click-through and conversion. If one creative execution isn’t performing, algorithms can tweak or replace it on the fly. The end result is a marketing approach that’s continually learning and improving. What matters to brands is not just having innovative tools, but seeing tangible impact from them. “So long we see acceleration and margin improvement, the solution is working as per goals. However, the biggest measure of success, mostly with AI-driven solutions including Agentic AI, is the adoption of technology,” says Raymond’s Ravi Hudda. His point is telling: no matter how advanced a system is, it must be embraced by the people using it – whether internal teams or end consumers – to truly succeed. If store managers trust the data enough to stock an AI-recommended mix of products, or if shoppers find an AI chatbot helpful enough to use regularly, those are signs of meaningful adoption. Hudda’s emphasis on user acceptance underscores that technology in fashion is not just an IT project; it’s a change management exercise across the organization and customer base.
Another key metric that tech-focused marketers watch is the cost of acquiring customers. Shenoy notes that after the initial buzz of innovation, what really proves a solution’s worth is its effect on the bottom line: “It’s the reduction in Customer Acquisition Cost (CAC) that truly illustrates the success of tech-enabled solutions,” he says. If AI can help identify more qualified audiences or automate nurturing so effectively that brands spend less on advertising per new customer gained, that directly boosts return on marketing investment. Shenoy adds that such gains are often linked to how well innovations help sell inventory efficiently – for example, using data to ensure popular items are in stock at the right locations – and whether stock replenishment keeps up with demand. In essence, the best use of AI and digital strategy in marketing is one that not only increases revenue but does so efficiently, by marrying demand to supply and minimizing waste.
With digital campaigns, social media, influencer collaborations, and e-commerce analytics all feeding into these AI systems, marketing in the fashion industry has become a more scientific endeavor. But it remains as much art as science: understanding trends, seasonality, and cultural nuances is still vital. The human marketing teams work alongside AI recommendations to fine-tune messages that will resonate with Indian consumers’ evolving fashion sensibilities. The tone among India’s marketing leaders stays neutral and data-driven – strategies are validated by numbers and adjusted swiftly when those numbers tell a different story. As technology continues to reshape fashion retail, the consensus is that its true value lies in measurable outcomes – driving efficiency, enhancing customer experiences, and delivering tangible business results.
AI on the Runway and in the Supply Chain
AI’s impact on the fashion industry isn’t limited to marketing and front-end customer experience; it’s also revolutionizing back-end operations and the very products brands sell. One area seeing significant change is inventory management and the supply chain. Fashion retail runs on seasonal cycles and trends, and getting the predictions wrong can be costly – too much stock leads to heavy markdowns, too little leads to missed sales. In India, this challenge is pronounced by regional diversity (what sells in Mumbai might not sell in Guwahati) and rapidly shifting tastes among a young population. Traditional forecasting methods often left brands with an estimated 30% of stock unsold by the end of a season, which then had to be cleared at a discount or simply written off. This is both a financial drain and an environmental concern, as surplus clothes sometimes end up as waste.
Predictive AI is now tackling this problem by crunching vast amounts of data to forecast demand more accurately. These systems take into account historical sales, but also real-time inputs like search trends on fashion websites, social media sentiment, economic indicators, even local weather forecasts and festival calendars. For instance, if the data shows an unexpected surge in searches for “floral dress” in the south of India due to a celebrity spotting, an AI tool could alert a retailer to redistribute stock to that region or ramp up production of that style. Homegrown AI solutions and advanced analytics at major players like Reliance Retail (which operates fashion chains), e-commerce leader Myntra, and even global giants like Amazon’s India unit are proving that such demand sensing is not just theoretical. Brands that use AI for planning report significant reductions in overstock – some estimates suggest inventory levels can be cut by 30–50% using these methods, leading to 20–40% fewer markdowns. This means more products sold at full price, healthier profit margins, and less waste at the end of the season.
The benefits also extend to supply chain agility. With AI insights, manufacturing and procurement can be more closely aligned with actual demand. A fast-fashion brand, for example, might use AI to decide mid-season which designs to reorder and which to cancel based on early sales data and even customer feedback analysis. This agility was harder to achieve before, when decisions were based more on last year’s sales or intuition. Now, a brand could realize that a particular style of denim is trending and rush to restock it within weeks, while scaling back on another style that isn’t performing – minimizing both stockouts and excess. All of this contributes to a more efficient, responsive fashion ecosystem.
AI is also creeping into the product design and manufacturing side, sometimes in surprising ways. In textile mills and factories, computer vision (a form of AI) is used for quality control – scanning fabrics for defects far more effectively than the human eye. Some manufacturers employ AI-driven machines for cutting fabric with minimal waste or for optimizing how patterns are laid out on material. These efficiencies reduce costs, which in turn can make fashions more affordable or profitable. Moreover, such precision contributes to sustainability. Using resources more efficiently and avoiding over-production helps fashion brands address the growing consumer concerns about waste and environmental impact.
What’s notable in India’s context is that while these technologies are being adopted, industry leaders maintain a realistic perspective about them. Many acknowledge that the Indian fashion sector is still in the early stages of its AI journey, especially compared to some global counterparts that have experimented with AI-generated virtual fashion shows or fully automated warehouses. Yet, there is optimism that India can leapfrog in certain areas by learning from global experiences and tailoring solutions to local needs. The tone remains pragmatic: AI is seen as a powerful tool to assist human expertise, not a magic wand that works on its own. As one veteran designer, Amit Aggarwal, noted in an interview, AI is evolving as a collaborative tool – one that can help designers and planners work faster and with more precision, while freeing them to be more creative. In other words, whether on the marketing front or the supply chain, AI provides the data and muscle, but human judgment and creativity still steer the ship.
Content Creation Meets AI: Balancing Automation and Creativity
Marketing in fashion has a significant creative component – from lookbooks and ad campaigns to social media posts and influencer collaborations. This is another domain where AI is making inroads in the Indian fashion industry, though human creativity remains paramount. Generative AI tools have emerged that can create images, write copy, or even produce short promotional videos. In 2025, using AI to generate a sample ad or a mock-up of a model wearing a new outfit has become surprisingly easy and fast. Brands are experimenting with these tools to supplement their content creation process. For example, if a fashion label wants to visualize a new design in different colors or on different body types, AI image generation can do that without a full photoshoot. This speeds up decision-making on which styles to push on Instagram or how to layout products on an e-commerce homepage.
Arpan Biswas, an Assistant Vice President at AJIO (one of India’s major online fashion retailers), has witnessed this acceleration. “The scope of work for AI is changing every few weeks, not years,” Biswas observes, highlighting how quickly new AI capabilities are emerging in the creative and marketing arena. Not long ago, AI in retail was mostly confined to behind-the-scenes analytics, but now it’s generating banner images and styling advice on the fly. Biswas notes that generative AI tools are enabling content production at a pace that keeps up with fast-moving fashion trends. In the time it once took to coordinate a photoshoot – booking models, photographers, studios, and then editing photos for weeks – brands can now conjure up high-quality visuals in a matter of hours with AI. This is especially useful for reactive marketing (like jumping on a viral trend) or creating localized content (for example, quickly adapting a campaign to different regional festivals or languages).
Some Indian fashion marketers use AI to create dozens of ad variants and then test which ones resonate best with different audiences. AI can generate slight changes in background, model pose, or copy text to tailor an image for, say, a youth-oriented Instagram audience versus a professional LinkedIn audience. This level of customization at scale was unimaginable manually. Biswas points out that AI provides a trifecta of “flexibility, adaptability, and efficiency” in content creation. Once an AI system is trained on a brand’s aesthetic – say, knowing that a particular label favors pastel colors and minimalist design – it can churn out on-brand content consistently, which the human team can then review and refine.
Yet, even as automation becomes more prevalent, fashion leaders caution that it has its limits. High-end brand campaigns and emotionally rich storytelling still rely on the human touch. As Biswas cautions, “AI is yet to replicate emotions.” A compelling fashion film or a powerful imagery for a flagship campaign often aims to evoke feelings – confidence, aspiration, nostalgia – that current AI tools struggle to authentically produce. There’s also the matter of originality and brand voice; too much reliance on machine-generated content could risk making everything look and sound somewhat generic. For these reasons, many brands use AI as an assistant rather than a full creator: it might handle the heavy lifting of generating options and drafts, but creative directors and designers make the final calls to ensure the output truly connects with the target audience. In practice, an AI might propose ten tagline options for a new collection launch, but the marketing team will pick or refine the one that best aligns with the brand’s personality.
Interestingly, the Indian fashion scene has also seen a few creative experiments with AI. A couple of designers have dabbled in AI-generated fashion show visuals or digital clothing lines. There was even an AI Fashion Week concept that garnered buzz globally (though India is watching more than participating for now). These experiments hint at a future where the lines between technology and fashion creativity blur further. However, industry veterans maintain a simple, non-sensational view: AI can augment creativity but not replace the human imagination that drives fashion’s novelty and allure. The narrative in fashion circles is that of balance – leveraging the latest tools to work smarter, while preserving the artistic integrity of the craft.
As AI and digital marketing capabilities mature, the tone among India’s fashion marketers and brand leaders remains even-keeled and focused on real outcomes. The consensus is that these technologies are here to stay and will likely become as commonplace as e-commerce or social media in the fashion business. Brands that once relied purely on the intuition of a star merchandiser or the flair of a creative director are now blending those strengths with data-backed insights and automated precision. The transformation is by no means complete – many companies are still figuring out how to integrate AI into legacy systems, and smaller designers may lack resources to experiment widely – but the direction is clearly set.
In summary, the impact of AI and digital marketing on the Indian fashion industry is both broad and deep. It spans flashy consumer-facing applications like personalized shopping and virtual try-ons to behind-the-curtain optimizations in supply chains and inventory. It influences how marketing campaigns are conceived and executed, and even how products are designed and presented. Throughout all these changes, Indian fashion brands are keeping the tone neutral and practical: success is measured in customer satisfaction, sales growth, and efficiency gains, not in tech buzzwords. There is a recognition that technology is a means to an end, not an end in itself. And that end is a fashion business that can delight the Indian consumer – who is increasingly digital-savvy – while running smarter and more sustainably. As Arpan Biswas of AJIO sums it up, “AI in fashion is no longer about catching up; it’s about being one step ahead, every single time.” In that forward-looking spirit, the industry continues to experiment, learn, and evolve, one data point at a time, one innovation at a time.
Disclaimer: All quotes are either sourced directly or attributed to public statements.
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