Earlier, a brand’s website or social media page was considered to be the place where buyers would visit and make their purchase decisions based on the information they gathered. This has changed massively with AI becoming an integral part of our lives and decision-making, and buyers today are increasingly looking for answers from AI assistants while making a purchase.
As marketers, we can see this pattern unfold in all the sectors. Be it a soap purchase, a cafe visit, or getting new earphones, buyers go straight to their AI searches and form a perception there. Many brands feel that they are being described inaccurately, inconsistently, or not at all. This is not a technology problem; this is a marketing issue.
The buyer has already moved
In early 2026, OpenAI’s chief executive said that India has 100 million weekly active ChatGPT users, which makes it the company’s second-largest market. In the same statement, OpenAI also shared that almost half of the messages in India come from users aged 18 to 24, while 80 per cent come from people under age 30. For marketers, this means that this age group is now forming brand preferences based on AI searches and conversations.
As per BCG’s Global Consumer Survey, covering almost 5,500 consumers in China, India, Japan and South Korea, shows the effect on purchasing. LLM search reaches 22 per cent of purchase journeys in India, against 5 per cent in Japan. Across the four markets, over 60 per cent of consumers using LLM searches mention that it shapes their brand preferences.
BCG also found that in more than half of LLM-assisted journeys, consumers consider brands they would not otherwise have found. For brands outside the top tier of recall, this is an opening, but only for those that AI systems can read, verify and recommend.
How buyers ask and how brands publish
AI searches do not work in the typical search box way. Now, consumers ask problem-led queries and comparative questions. For instance, if a consumer is buying a protein powder, they would ask questions regarding their protein requirements, compare brands and their products, pricing and quality. These questions are not answered on the brand’s websites, as the brand mostly lists their products with names, prices, ingredients, etc. So, while the websites or social media handles of the brand might have the information, it does not appear in searches when consumers ask their specific questions.
Reporting habits widen the gap. Marketing teams are accustomed to rankings, clicks and impressions. An AI-generated answer produces none of these. A brand is either named or absent, and few teams are testing which applies.
Where visibility is typically lost
Several issues recur across brands of different sizes. The first is technical access. Key content is sometimes locked inside scripts, images or documents, or crawler permissions block AI systems from reading pages that search engines can reach.
The second issue is the inconsistent brand information. The content mentioned on the brand’s website, social media page and e-commerce tends to differ sometimes, which creates confusion. AI systems weigh corroboration, and conflicting signals reduce confidence in any single version.
Another issue is the promotional content which is posted without any substance. Pages built on superlatives and unsupported claims give an AI system little to cite. Factual, specific and well-structured material is easier to use.
The fourth challenge is thin third-party validation. BCG notes that LLM search draws heavily on sources beyond brand-owned sites, including forums, review platforms, social content and Wikipedia. A brand with strong owned content but little independent coverage, few reviews and no expert mentions is at a disadvantage.
The fifth is language. Many Indian brands publish in English alone, while their customers ask questions in Hindi, regional languages or a mix of them. Where no content exists in those languages, the brand is unlikely to appear in the answer.
What is distinctive about India?
In India, social media leads the purchase decision-making today, supported by a large creator ecosystem. AI discovery sits alongside this, and creator content, reviews and community discussion often become the material that AI systems draw upon. Mobile-first usage shapes how answers are read and acted on, and trust in peer validation remains strong.
The buying intent also varies from category to category. While someone purchasing an FMCG product asks about the ingredients and price, a fintech query could be more focused on safety, fees and regulation. On the other hand, B2B buyers use AI to build vendor shortlists, checking integration, credentials and references. A single content approach will not serve all three.
Building on the SEO foundation
None of this replaced the already established search practice. Technical SEO still acts as a foundational pillar: a well-structured website that is fast, crawlable, authoritative, and gives AI systems material they can read and trust. Generative engine optimisation (GEO) extends that foundation into AI-generated responses, with focus on phrasing, structure and corroboration.
Closing the gap
Audit is where the brands should begin. Find what your buyers are asking online and put those questions, in the same language, to leading AI assistants. Findings usually point to a short list of fixes: opening technical access, aligning brand information across every channel, producing direct and factual answer content, and earning independent coverage and reviews that corroborate it.
This should be followed by measurement. Share of mentions and citation frequency belong in leadership reporting beside rankings and traffic, with ownership shared across marketing, product and technology teams so that one function does not carry the work alone.
India's buyers have shown where discovery is heading. Brands that treat AI visibility as part of their core search strategy and build on the foundations they already have will be the ones named when the question is asked.
