India's Fashion Brands Have a New Gatekeeper and It Is Not Human

What is Agentic AI

Simply put — AI is moving from helping us shop to doing the shopping for us.

Today, we do all the hard work ourselves: searching, comparing, deciding, buying. With an AI agent, you just say what you want, and it takes over most of these steps.

Tell it “I need a smart business casual outfit for a corporate event, under ₹8,000,” Instead of giving you 100 links, the AI could:

• Understand what you are looking for
• Search different platforms
• Compare products, prices and reviews
• Check your size and availability
• Look for offers
• Suggest the best options
• And, with your approval, complete the purchase
• So, search → compare → decide → buy could increasingly become one seamless process.

This isn't just a fancier version of online shopping — it's a real shift. The person browsing your website might not be a person at all anymore; it could be their AI agent.

Which means Retailers may increasingly have two audiences to satisfy:

• The human, who cares about the brand story, the visuals, the feeling of the product.
• The AI agent, who couldn't care less about your homepage banner and only cares whether your product data is accurate, prices are clear, sizes are listed properly, delivery is reliable, and returns are simple.

Websites or Apps built to look pretty for human eyes will also need to be easy for machines to read.

McKinsey’s, Three Possible Agentic Commerce Models

According to management consultancy giant McKinsey, agentic commerce will operate primarily through three architectural flows. (Kindly note, these Models are still emerging in fashion retail space):

Model 1: Agent to Site - My AI Shops for Me (on a website)

A personal AI agent searches a retailer's website, evaluates products against my requirements and, with my approval, helps complete the purchase. This model is beginning to emerge, but fully autonomous fashion shopping is still developing.

Model 2: Agent to Agent - My AI Talks to the Retailer's AI

This is still emerging. The idea is that my personal shopping agent could communicate directly with a retailer's commerce agent to check availability, negotiate within agreed rules and complete a purchase.

Model 3: Brokered Agent to Site - A "Broker AI" Shops Across Many Stores

This remains an emerging model. In the future, a broker agent could connect a consumer's personal AI with multiple retailers, compare products and create a multi-retailer basket.

Glance AI offers a useful glimpse of where this could be heading. It already uses an intelligent shopping agent to personalise fashion discovery and connect users to shoppable looks. It is not yet the full brokered, multi-agent model McKinsey describes, but it shows how fashion discovery is moving beyond the traditional search box.

Estimated Market Size

McKinsey estimates that agentic commerce could orchestrate $3 trillion to $5 trillion in global retail revenue by 2030.

Whether the number lands exactly there or not, the direction is obvious — this is bigger than a shiny new feature.

The real question fashion brands need to ask isn't “should we use AI?” anymore. It's: “when my customer's AI agent goes shopping, will it pick us?”

1. The Shopper Who Doesn't "Shop" Anymore

Consider a typical friday evening: you realize you have an important wedding event on Sunday. You prompt your assistant: "I need an elegant Kurta Pyjama under ₹7,000 delivered by Saturday afternoon."

Today, that trigger sends you on a multi-hour manual journey across Myntra, AJIO, Amazon, and Google or personal visit to Manyavar, Shoppers Stop or Lifestyle Stores.

In an ideal agentic environment, your agent will analyse your historical size data, preferred fabrics, past return triggers, and local logistics options to return with a shortlist of verified matches, ready for one-click approval.

2. The Shift from Discovery to Execution

We will be witnessing a clear evolutionary timeline in commercial retail:

Traditional Search → Recommendation Engines → Generative Assistants → Conversational Commerce → Agentic Action

When technology moves from making recommendations to taking action, the agent could increasingly become a gatekeeper between the brand and the consumer.

3. The Decline of the Traditional Search Box

For the last 25-30 years, the digital catalogue search box has been the front door of e-commerce. Shoppers typed keywords like "White Cotton Shirt under ₹3,000" and spent hours scrolling.

As buyers transition to describing end goals to an agent, the reliance on keyword search bars will steadily drop. Users will express intents rather than manage keywords.

4. Brand vs. AI Agent: The New Competitive Landscape

Historically, retail battles pitted brand against brand—Zara versus H&M, or Myntra versus AJIO.

Agentic commerce introduces a new battlefield: Brand vs. AI Agent.

When a buyer requests a "100% Pure Cotton Shirt under ₹3,000," the AI agent evaluates raw product parameters, customer satisfaction scores, and delivery speeds.

It may select a lesser-known Direct-to-Consumer (D2C) brand over a massive global label simply because the D2C brand's product attributes matched the user's intent more precisely.

5. Re-evaluating Brand Loyalty

Will AI weaken or strengthen brand loyalty? It can go both ways.

On one hand, an agent will effortlessly surface hidden or emerging brands that meet a user's criteria better than their usual go-to label, lowering barriers to brand switching.

On the other hand, if an agent learns that a customer consistently keeps and loves items from a specific brand while returning items from others due to fit issues, it will double down on that brand.

AI can either fragment traditional brand loyalty or make it hyper-personalized.

6. Disintermediation of the Store Interface

The legacy digital sales funnel runs from

Consumer → Search Engine/social media → Retailer Storefront → Product Page → Purchase.

The agentic path compresses this to

Consumer → AI Agent → Dynamic Multi-Retailer Sourcing → Purchase.

Retailers risk losing ownership of the primary customer interaction. While the retailer continues to handle product design, manufacturing, and physical fulfilment, the AI agent could increasingly mediate the customer relationship.

7. Who Controls the Underlying Algorithms?

This transition introduces serious commercial and ethical questions. If an AI agent dictates the shortlist, who governs its underlying logic?

• Can dominant platforms monetize placement by letting brands pay for preference?
• Will commercial commission agreements tilt recommendations?
• How will consumer privacy and sizing data be safeguarded?
• What stops algorithms from displaying systemic bias against smaller, independent labels?

Building trust through algorithmic transparency will become a major strategic challenge for fashion platforms.

8. Transforming the Retail Operating Engine

Agentic AI, combined with existing AI and automation, could increasingly transform backend operations:

• Merchandising Design: Analysing millions of social signals, search patterns, and fashion imagery to forecast micro-trends before they hit the mainstream, while creating digital garments that minimize physical sampling.
• Dynamic Pricing: Real-time adjustments based on localized demand, inventory velocity, and competitor moves.
• Supply Chain Inventory: Connecting live demand signals directly to factory floors, dynamic allocation, and automated store replenishment.

9. The Rise of a New Key Performance Indicator (KPI)

Traditional e-commerce teams obsess over web traffic, click-through rates (CTR), conversion percentages, and average order value (AOV). I believe the agentic era could introduce a new KPI: Agent Selection Rate.

Retailers will need to track:

• How many third-party AI agents queried our product database this week?
• What percentage of those queries resulted in our items being shortlisted?
• What specific attribute deficits (e.g., slow delivery, missing material details, higher price) caused an agent to reject our product?
• What is our final agent-driven conversion rate?

The Indian Fashion Market, By the Numbers

• India's retail story has its own scale and momentum. The IMF's latest outlook puts India's real GDP growth at around 6.4% in 2026.
• A Deloitte–FICCI estimates India's retail sector could grow from about $1.06 trillion in 2024 to $1.93 trillion by 2030.
• Quick commerce, valued at $5–6 billion today, is projected by McKinsey to reach $35–40 billion by 2030.
• Redseer forecasts India's apparel market could reach $130–150 billion by 2030, growing at 10–12% annually from 2024 to 2030
• Online fashion alone is already one of India's strongest digital categories, with one 2026 industry estimate placing 2025's online fashion market at around $35 billion.

Rolling out Agentic AI in India isn't as simple as copying what works in the West. India isn't one fashion market — it's hundreds of smaller ones, shaped by language, culture, income, and climate. What sells in Mumbai might not sell at all in Bhubaneshwar, Kochi, or Guwahati.

And a few things make India especially tricky — and especially interesting — for Agentic AI:

• Millions of small sellers:

India's retail story is very different. Nearly 60 million MSMEs form the backbone of India's highly fragmented retail ecosystem, with small sellers and local traders operating alongside large national and international retailers. An AI agent here can't just plug into a handful of big brand APIs — it needs to understand a huge, fragmented network of smaller sellers too.

• Everyone sells everywhere:

A single fashion brand might sell on its own website, on Amazon, Flipkart, Myntra and AJIO, through Instagram, and out of a departmental store such as Shoppers Stop or Lifestyle — all at once. D2C brands are increasingly opening real stores too. An agent has to know what's actually in stock across all these places at the same time.

• Small towns are leading, not lagging:

Redseer estimates that 59% of online fashion GMV comes from Tier-2+ cities. An agent trained only on “metro-centric preferences” would miss most of the market.

• Price still matters, a lot:

Indians love fashion, but we also love a good deal. Redseer's latest research shows that online fashion growth is strongest in the value segment, particularly below ₹800. So, for an AI agent, finding the right style at the right price—and spotting a good offer—could be just as important as knowing our fashion taste.

• Festivals change everything:

Purchasing patterns in India fluctuate heavily around cultural events—Diwali, Eid, Durga Puja, Onam, regional harvest festivals, and local wedding seasons. Generic recommendation engines may struggle to capture this complexity. Agentic AI must understand localized calendar context, regional dress codes, and tight delivery deadlines tied to event dates.

• Language is a real hurdle:

Indian shoppers often think and type in Hindi, Tamil, Telugu, Kannada, Bengali, Marathi, or a natural mix with English. Agents need to handle all of that comfortably.

• UPI makes this easier here than almost anywhere else:

India’s digital payment ecosystem is already highly mature. UPI processed 21.70 billion transactions worth ₹28.33 lakh crore in January 2026. This strong payment infrastructure could give India a major advantage as AI agents begin to discover, recommend and eventually complete fashion purchases.

• Quick commerce is creeping into fashion:

Quick commerce is no longer just about milk and groceries. India's quick-commerce market has crossed $5-6 billion in GMV, and fashion is beginning to join the race. Fashion-focused services such as Myntra M-Now and Slikk show how fashion is beginning to move towards faster delivery, while horizontal quick-commerce platforms such as Blinkit, Zepto and Instamart are also expanding their non-grocery assortment.

• Returns are a genuine pain:

Indian online apparel has historically seen high return rates, often cited in the 25–40% range, with fit being a major contributor. Better AI-assisted sizing, virtual try-ons and use of past fit data could help reduce avoidable returns.

• Inventory is scattered everywhere:

Fashion brands struggle to maintain real-time inventory tracking across thousands of Stock Keeping Units (SKUs) distributed across central warehouses, regional centres, and retail stores. Agentic AI could provide an operational bridge by connecting live demand signals with stock allocation, markdown decisions and replenishment systems.

The bottom line: Agentic AI in India has to be built for India — its many languages, its price-consciousness, its festivals, and its wonderfully messy mix of sales channels — not simply copy-pasted from a Western playbook.

Here's something that doesn't get talked about enough. Fashion has a serious waste problem — India is estimated to generate around 7.8 million tonnes of textile waste every year. The real opportunity for Agentic AI isn't just selling more clothes faster. It's helping brands make and move the right clothes in the first place.

Traditional apparel supply chain (high waste)

Mass Production → Central Warehouse → Broad Allocation → Excess Stock / Unsold Markdowns

 AI- driven, demand-driven supply chain (low waste)

Localized Trend Signals → On-Demand Production → Direct Local Allocation → Minimal Waste

Picture an AI agent noticing that a particular style is trending in Bhopal but nobody wants it in Bhubaneshwar. Instead of manufacturing 100,000 pieces for the whole country, it could help a brand make just the right amount for the right city — cutting down on markdowns, overproduction, and waste.

It could also help designers catch trends earlier using social and search signals, while virtual sampling reduces the need for physical prototypes altogether.

India already boasts pioneering brands leading sustainable and circular fashion initiatives:

• Doodlage: Built its core business model around collecting, upcycling, and redesigning post-industrial textile waste into premium apparel.
• Fabindia: Focuses heavily on traditional handloom textiles, artisan communities, and circular retail practices. For instance, Fabindia introduced wooden hangers across its supply network, placing over 20 lakh wooden hangers into circulation and directly diverting more than 100 metric tonnes of plastic waste.
• Circular Material Innovators: Emerging designers showcase new approaches to sustainable fashion. At the Circular Design Challenge, designers presented garments constructed from banana leather, Korai grass, Calotropis fibers, and repurposed deadstock materials.

Now imagine pairing this kind of thinking with an AI agent that quietly asks, before every big production run: “Do we really need 20,000 new shirts, or can we re-dye and reuse what's already sitting in the warehouse?” It could match unsold deadstock in Surat with a fresh order somewhere else, or route a returned item to a repair shop instead of shipping it all the way back to a central warehouse.

For an industry where cost, waste, and messy supply chains are all tangled together, this could turn out to be one of Agentic AI's most genuinely useful contributions.

Coming Back to That Lunch

I keep thinking back to that afternoon at my friend's house when we casually tried Glance AI and, within minutes, found fashion choices that felt surprisingly close to our personal style. Some of us even ended up buying what the AI suggested. What started as a bit of fun made me realise that AI may not just help us shop differently—it could change who makes the shopping decision.

For decades, fashion brands have learned how to influence people through advertising, stores, celebrities, influencers and personalisation. Now they may have to learn how to influence the AI agents making decisions for those people.

Agentic commerce is no longer something fashion retailers can simply watch from the sidelines. It is already beginning to change how consumers discover, compare and buy products.

The big question is no longer “Will AI shop for us?”

The more important question is: “When AI comes shopping for me, will it choose your brand, your store, your website or your app?”

For fashion businesses, this could mean rethinking almost everything—from marketing and customer engagement to pricing, inventory and even the business model itself.

Tomorrow's winning fashion brand may not simply be the one that attracts the most shoppers. It could be the one that AI understands, trusts and recommends most often.

And after that lunch conversation, I am convinced of one thing: the future of fashion retail may not be about getting more people to shop—it may be about getting the machines that shop for them to choose you.