80% of Festive Shoppers Want an AI Assistant. Are You Ready?

Ask yourself one question before you read further: if a customer typed your product category into ChatGPT or Gemini right now and asked for a recommendation, would your brand show up, and would what it says about you be correct? Most marketing and e-commerce teams in India can't answer that today, and InMobi's latest festive-season research says their customers have already moved on without them.

InMobi Advertising's "Marketer's Guide to India's Festive Season 2026," an on-device survey of 1,110 consumers run between August 7 and 11, 2026, found that four in five festive shoppers, 80%, now actively want an AI shopping assistant involved in how they decide what to buy. This isn't a niche behavior anymore. It's the majority.

What the data actually shows

A few findings from the coverage of this report are worth sitting with, because together they describe a shift in how people shop, not just a new feature they're trying out:

• Millennials are ahead of Gen Z on this, not behind, by about 10 percentage points, per Mediabrief's coverage. The assumption that AI adoption is a young-shopper thing doesn't hold here. Millennials with more money to spend and less patience to compare forty tabs are the ones leaning on AI the hardest.
• Trust is now edging out discounts as the main purchase driver. IndianTelevision's report on the same study found 27% of shoppers cite brand trust as their top factor, against 22% for discounts and festive offers. That's a real shift from a market that has run on sale-season discounting for years.
• 77% of shoppers raised their festive budgets this year, and three in five planned to spend more than ₹10,000, per the same coverage.
• Tier 2 cities aren't lagging on AI, they're catching up fast. They now roughly match Tier 1 cities on quick-commerce adoption, though they still trail by about 10 points on interest in AI shopping assistants specifically.

Read those together and the picture is: customers have more money, less loyalty to discount timing, more trust in brands they already believe, and a growing habit of asking an AI assistant to help them decide. None of that depends on Diwali. It's a shift in shopping behavior that happened to show up clearly during the festive data collection window, and there's no reason to expect it reverses in December.

Why this isn't just an e-commerce story

If you sell a product with a catalog, this applies to you directly. But the same behavior shows up further than retail. A B2B buyer researching vendors, an HR team comparing training providers, a homeowner comparing renovation contractors: all of them increasingly start that research with a question to an AI assistant, not a search engine query they'll click through ten results for. If your business has any kind of online presence with product or service information, the question "does the AI assistant describe us correctly" applies.

A checklist for this week

1. Test your own category, cold

Open ChatGPT, Gemini or Perplexity and ask the question a real customer would ask: "What's a good [your product category] in India?" or "Which companies offer [your service] near [city]?" Don't search for your own brand name first; ask the question the way a stranger would. See whether you show up, what the assistant says about you, and whether it's accurate. Do this across two or three assistants, since they draw on different sources and will give different answers.

2. Audit your product and service content for "AI readability"

AI assistants work from clear, structured information: specific product names, accurate specifications, real prices, genuine customer reviews. Vague marketing copy that reads well to a human but says little ("premium quality, best in class") gives an AI assistant nothing concrete to repeat. Go through your top 20 product or service pages and check whether a stranger reading just that page could answer basic factual questions about what you sell.

3. Fix your reviews and trust signals before your discounts

Given that trust now narrowly beats discounts as a purchase driver, the cheapest lever most teams are ignoring is also the most boring one: respond to reviews, keep your Google Business Profile and marketplace ratings current, and make sure your "About" page actually says who you are and how long you've operated. AI assistants and human shoppers are both reading the same trust signals now.

4. Don't assume Tier 2 customers are behind

If your targeting or content strategy still treats Tier 2 and Tier 3 cities as "catch up later" markets for anything AI-related, this data says to reconsider. Quick-commerce adoption there is already close to Tier 1, and the AI-assistant gap is closing. Regional-language product descriptions and reviews matter more here, not less.

5. Brief your support and sales teams on what "AI-referred" traffic looks like

A customer who arrives because an AI assistant recommended you often arrives with more specific, pre-researched questions, and less patience for a generic sales script. If your support or sales team can't tell the difference between a cold lead and someone who's already compared you to two competitors through an AI assistant, you're wasting the advantage you worked to get.

The part that's easy to get wrong

Don't treat "showing up in AI answers" like SEO with a new name. Classic SEO optimizes for ranking against a search algorithm you can study and game over time. What an AI assistant says about you depends on training data, live retrieval, and the model's own judgment about what's trustworthy, three things that are harder to reverse-engineer and that change as each vendor updates its model. If your organization is also deciding how it wants its content to be used by AI companies in the first place, that connects to the training-preference changes we cover in our guide to the new robots.txt signal: visibility in AI answers and control over AI training are two sides of the same decision, and they're worth making together rather than letting IT and marketing decide separately.

Getting your team fluent in how customers now actually shop, rather than how you assume they shop, is exactly the kind of gap that a short, structured corporate AI training program closes fastest: pick the two or three AI assistants your buyers actually use, test your own presence in them as a team exercise, and fix what you find.