When AI Is Your Shopping Wingman

The familiar ritual of seeking a friend’s opinion and scrolling through endless product listings before making an online purchase is beginning to change. With AI transforming almost everything today, shopping has become the next major use case. AI shopping assistants are moving from novelty features to measurable behaviour across the country’s ecommerce spectrum.

This means AI has not only become the first point of contact for many users during product discovery but is also helping them make an informed bet before making a purchase.

Propelling this change are Amazon’s Rufus, Myntra’s Maya, Meesho’s Vaani and Flipkart’s Flippi, to name a few, which are capable of handholding consumers throughout the purchase cycle — from discovery to checkout. 

As per a report, 41% of Indian consumers are already using AI-powered shopping tools, with another 40% expected to adopt them soon. 

“From a consumer standpoint, the future is very clear. ChatGPT today handles about 2 Bn queries, of which nearly 50 Mn are commerce-related,” AI-powered shopping assistant Flash AI’s founder Ranjith Boyanapalli told Inc42. 

He added that consumers leverage AI to conduct three kinds of shopping-related searches: for product discovery and recommendations; product usage and suitability queries; price comparison and value assessment. 

While research remains the most common use case for AI among consumers, its ability to drive conversions is now drawing significant industry attention. 

“AI-savvy consumers going to merchants are converting at anywhere between 6% and 10% from a typical conversion rate of 1% to 3%,” Boyanapalli added.

So, as AI’s influence in shopping grows, what new avenues are being opened for consumers, brands and marketplaces alike? Let’s find out in this edition of The Checkout.

Going Beyond Discovery With AI Shopping Agents

Speaking with Inc42, the managing partner of PeerCapital (an investor in Flash AI), Karthik Prabhakar, said that the traditional ‘scroll-to-cart’ journey was filled with human interruptions like discussing, comparing and seeking opinions. With AI agents, the process has become more efficient.

Meanwhile, companies are focussed on building more context-aware agents that help consumers at the checkout by constantly monitoring their shopping behaviour.

Giving an example, Prabhakar said, “If the AI notices a user often pauses before checking out, perhaps because they are double-checking whether they have forgotten something, the AI could proactively ask, ‘Would you like to review related items before completing your order?’”

Then, companies are also using AI to solve one of the biggest challenges of India’s shopping ecosystem: returns. The root cause of this consumer habit is buying without enough information or selecting a product that does not match expectations. AI is solving the same at the research stage itself. “AI-savvy consumers are less likely to return products,” Flash AI’s Boyanapalli said.

The technology is also forcing brands to improve their product information because AI agents favour products with complete and accurate data. Brands are realising that AI shopping assistants may ignore products with poor descriptions, missing specifications or weak imagery. 

Battle For Visibility 

Today, brand managers are worried about whether their brands are optimised to be selected by an agent. What this means is whether AI shopping agents will choose and recommend their products. This matters because agents do not browse the way humans do. They weigh reviews heavily and read ratings as ranking signals.

However, different stakeholders are using AI for different use cases. 

For instance, marketplaces like Myntra offer multiple in-built AI tools like Maya, MyStylist and Virtual Try-On to enable customers to try the product virtually before buying. 

Speaking to Inc42, Lakshminarayan Swaminathan, SVP and head of product management and design at Myntra, said that the tools have helped the platform drive a 1.5X increase in product consideration and 2X higher conversion. 

But at a time when millions of shopping-related queries are happening on AI platforms such as ChatGPT, Gemini or Perplexity, brands and marketplaces aren’t automatically benefiting from them.

To capture this, brands have started to integrate directly with AI platforms. 

Last year, Walmart partnered with OpenAI to enable customers to shop Walmart’s catalogue within ChatGPT through an ‘Instant Checkout’ feature.

Beyond LLM integrations and in-house AI tools, India is also witnessing the rise of startups focused on AI-assisted shopping. Flash AI is a key example. Its companion platform, HeyBuffy, helps brands such as Dot & Key and Mokobara measure and improve their visibility across AI platforms like ChatGPT and Gemini. It also enables brands to deploy AI-powered shopping assistants on their own websites.

The ROI Puzzle

With AI agents rapidly gaining traction, are brands merely investing to stay relevant, or are these investments delivering measurable ROI, too?

D2C brands are investing in AI primarily as a conversion tool on their own websites. For brands like Mokobara, Sugar Cosmetics and Butterkup that have already integrated AI assistants, the returns are measurable. 

“Users who engage with AI assistants convert at nearly 3X the rate of those who do not. However, adoption remains low, with less than 10% of website visitors currently interacting with these AI assistants,” Boyanapalli said.

Meanwhile, marketplaces are navigating a fundamentally different anxiety. For them, the AI question is not about conversion optimisation but about survival of relevance. While some are comfortable partnering with LLMs, others worry that if product discovery shifts to LLMs, they could lose ownership of the customer relationship.

Finally, while you may spot Indian customers using AI at multiple stages of shopping like discovery, comparison and trials, payments remain the untouched segment. This is because users are skeptical to let AI shop on their behalf. 

Agentic shopping in India is in its infancy, but the transition has already begun. Now the question is: will consumers trust AI enough to let it make the final call? 

SPOTLIGHT | How Cure By Design Is Bringing Hemp And CBD In Wellness Routines

• The Bengaluru-based startup offers hemp-based healthcare products. Its portfolio spans CBD oils, balms and pain-relief solutions, alongside hemp-based personal care and nutrition products such as shampoos, face washes, serums and food items.
• The company has also diversified into pet care, ayurvedic formulations and dietary supplements. Beyond products, it offers consultations through cannabinoid medicine specialists and practitioners of alternative therapies. 
• Apart from selling products via its own website, the company claims to have a presence on 40+ marketplaces and platforms, including same-day delivery channels such as BigBasket, BB Now, Instamart, and SuperTails.

Ecommerce Buzz

• RENEE Cosmetics’ ₹400 Cr Revenue Run: Beauty startup RENEE Cosmetics narrowed its FY26 net loss by 46% to ₹36 Cr while growing revenue 38% to ₹440 Cr. Strong offline sales, repeat purchases and omnichannel expansion boosted margins, with the company targeting profitability in FY27 and an IPO by FY29.
• FirstClub Bags $55 Mn: Quick commerce startup has raised $55 Mn in a Series B round co-led by Peak XV and Sofina. The fresh capital will fuel store expansion, warehouse infrastructure and category additions as the premium delivery platform doubles down on product quality, freshness and customer retention.

• Fraganote Raises $3 Mn Fundraise: D2C fragrance brand Fraganote has raised $3 Mn in a Series A round led by V3 Ventures to expand its omnichannel presence and diversify into body care products. Riding a 5X growth trajectory, the startup is targeting ₹60 Cr revenue by FY27 and plans to scale its kiosk network aggressively.

• The Bear House’s ₹500 Cr Bet: In an ecosystem chasing growth at all costs, The Bear House chose patience over blitzscaling. Six years after launch, the menswear brand crossed ₹270 Cr in revenue and grew its profits 5X in FY26. The brand intends to cross the ₹500 Cr revenue mark in FY27.   

The Deep Dive 

Operator’s Question 

What strategies are moving the needle when it comes to AI visibility and discoverability, and what are the most common gaps still holding them back?

Speaking with Inc42, the founder and CEO of TruCommerce, a global supplier of supply chain and trading partner connectivity, integration and omnichannel solutions, Viren Inaniyan said that the most prominent step which is moving the needle is off-page citations.

“In our data, 84.6% of the products ChatGPT recommends come from its cited sources and over 95% of those citations are external — editorial placements on high-authority publishers, plus Reddit and Quora seeding — not a brand’s own pages.”

Secondly, pricing and product feed discipline are the second lever. When a product is the cheapest option, it wins the top AI recommendation 67.8% of the time, versus 26.9% when priced 20% higher.

Addressing the gaps, Inaniyan said that most brands still run AEO like old SEO, optimising one headline keyword while a single shopper’s query silently fans out into 5-7 sub-queries they never cover. They skip weekly baselines, so they miss algorithmic re-ranking until rankings have already shifted. Additionally, messy product-variant data remains a significant cause of AI hallucinating the wrong specs and prices.

And that’s a wrap on this edition of The Checkout by Inc42. We’ll be back next week with a deep dive into the latest trends shaping the ecommerce landscape. 

Thanks,

Palak Sharma

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