By Rahul Pandey, Founder & CEO, Glu
India is becoming one of the most active markets for AI adoption. Sam Altman confirmed India had crossed 100 million weekly active users on the platform by early 2026. The numbers may look modest against Google’s search volumes, but referral traffic from ChatGPT grew 206% year on year between 2025 and 2026, and the trajectory is only continuing upward.
The anatomy of search itself is changing. Traditional search was linear: query a category, get flooded with options, compare, shortlist, visit brand websites, research further and eventually decide. Buyers moved through stages across multiple sessions and platforms.
That structure is now being compressed. Globally, shopping-related queries on ChatGPT doubled in the first half of 2025, and Adobe research found that more than a third of surveyed users discovered a new product or brand through AI. The queries driving this are not simple keyword lookups. Someone asking “What is the best protein powder for a vegetarian woman with insulin resistance in India?” or “Which CRM works best for a 20-person B2B SaaS team with a lean sales function?” is consulting, not searching. Context, constraint, urgency and intent all surface in a single prompt. One chat. One window. Zero clicks.
Discovery, comparison, consideration and purchase intent are converging in one exchange. A user may still verify on Google or a brand website afterward, but by that point the behaviour has shifted. They are deciding, not browsing.
This compression has two significant implications for marketers and enterprises.
User behaviour is the first. Consumers are adapting to conversational, AI-mediated discovery. They expect answers that reflect their specific situation: budget, location, use case, constraints. The expectation has moved from “show me options” to “help me decide.” When a user takes an AI recommendation to Google for verification, that is a high-intent buyer seeking confirmation, already well along in the decision journey.
Machine behaviour is the second, and this is less well understood. Answer engines evaluate brands differently from search engines. They interpret intent, connect entities, read context signals and draw from multiple sources to construct a response. It is almost like you are speaking to an expert, a confidant, a negotiator and an ally all rolled into one. And that changes everything about what it means to be discoverable.
What an answer engine is really looking for is confidence: enough credible, structured and consistent information across sources to recommend a brand without hedging. Product pages, FAQs, schema markup, reviews, third-party mentions and category authority signals all feed that judgment. A vague brand claim on a homepage is weak signal. A well-structured product page, supported by specific use cases, credible reviews and consistent language across the web, is strong signal. Machine legibility, how clearly an answer engine can understand, contextualise and trust your brand, is becoming a competitive variable.
This levels the playing field. In traditional search, larger brands often won through media budgets and domain authority. In AI-led discovery, a smaller brand with clear, structured, well-indexed information can outperform a larger one that has not been built for machine legibility. Clarity and specificity are competitive advantages.
For marketers, the task is to extend brand building. Distinct positioning, tone of voice and visual identity remain essential for human audiences. They must now be accompanied by structured meaning, contextual depth and authority signals that answer engines can parse and trust.
The next era of brand discovery will be shaped by two audiences: the human who buys and the AI that influences what gets considered. Brands built for both will be the ones that show up and get chosen.