Who are Indian brands writing for: Human shoppers or AI search agents?

What do you do when the 'customer' reading your product page is an AI agent rather than a person, what actually has to change — and is the average Indian brand's content built for humans to be persuaded or for machines to parse?

Roshan Mohan CMO Flow Blinq, has some answers.

Roshan Mohan: The question has two parts. First, even when an AI agent is reading a product page, there is still a human behind that query. The end objective of the brand does not change, it still needs to persuade a human buyer. What is changing is the way information is discovered, processed,and consumed.

This is not limited to Indian brands. Globally, we are witnessing a shift from traditional search engines such as Google and Bing towards what can be described as "agentic search." 

Historically, brands optimised their websites for Search Engine Optimization (SEO), assuming users would search, click on top listed links, visit websites, and navigate multiple pages before making a decision. This was about being ‘listed and found’.

Today, increasingly, users are asking questions directly to AI assistants and receiving answers within a chat interface. Which means, it is more about being ‘visible and then being cited.’

For example, a consumer researching running shoes earlier would search Google, open multiple brand websites, compare features, pricing, and read reviews. Today, the same user may ask an AI assistant, "What are the best running shoes for long-distance runners under ₹6,000?" and then continue with follow-up questions such as "Which one has the best grip in monsoon conditions?" or "Which brand offers the best warranty?", all within the chat window, without necessarily visiting a single website.

This is where the second part of the question becomes important. Most brand websites today are built for humans to read and for search engines to index. The challenge is whether they are also structured in a way that Large Language Models (LLMs) can easily parse, interpret, and retrieve information from. The Princeton research found that citing sources, adding statistics, and including direct quotations improves AI visibility by 30-40% compared to unoptimised content.

In the era of Google Zero and AI-generated answers, websites are increasingly becoming data sources for AI systems rather than direct destinations for consumers. Brands therefore need to build an additional layer that makes their content machine-readable and AI-friendly. 

Product specifications, pricing, comparisons, FAQs, use-cases, reviews, policies, and brand claims should be structured clearly so that LLMs can accurately understand and surface them in responses. The question is no longer whether your website ranks on Google Search. It is whether an AI agent can confidently read, understand, and recommend your product when a customer asks for it.

PRmoment India: If an agent is choosing on specs, price, and structured data, what happens to everything PR is built on — story, emotion, brand premium? Is agentic commerce quietly a commoditisation machine?"

Roshan Mohan: It is a shift, but visibility should not be confused with machine readability in isolation. Traditional search is not disappearing overnight, but discovery is increasingly moving to AI interfaces. Gartner estimates that traditional search volume could decline by over 25% by 2026 as users turn to AI assistants and virtual agents.

For brands, the current, immediate challenge is AI visibility. FlowBlinq's research suggests that 77% of brands’ websites are currently invisible to AI platforms. 

This makes LLM readability critical, if AI systems cannot understand and retrieve information about your brand, they cannot recommend it. Visibility is also the first step towards agentic commerce, where consumers discover, compare, and eventually purchase products directly within AI interfaces. McKinsey estimates that agentic commerce could intermediate $3–5 trillion in global retail spending by 2030.

In the AI model, PR becomes all the more important for AI commerce since LLMs place significant weight on editorial coverage, third-party validation, reputed publications, community discussions, and websites like Reddit and Wikipedia, when deciding which brands to surface. Storytelling, credibility, and brand reputation therefore become even more important.

In fact, GEO research shows that authoritative citations, statistics, and quotations are among the strongest drivers of visibility in AI-generated responses. As per Gartner’s research in 2025, fueled by AI search, PR budgets will double by 2027. This is not a distant vision, it's happening today, for example, in Singapore, particularly in the B2B SaaS and fintech sectors, have reframed their media relations programmes around a GEO objective. AI engines strongly favour authoritative third-party.

The winners in the agentic commerce era will not be brands that choose between machine readability and brand building. They will be the brands that combine both: structured, AI-readable information for discovery and strong PR, storytelling, and third-party credibility for recommendation and trust.

PRmoment India:  Behind the protocol alphabet soup — ACP, UCP, AP2 — what's the one thing a brand or comms leader should be doing this quarter, and what's pure hype they can safely ignore for now?

Roshan Mohan: The one thing brands should be doing this quarter is preparing for agentic commerce. McKinsey estimates that agentic commerce could intermediate USD 3-5 trillion in global retail spending by 2030, making it one of the most significant shifts in how products are discovered and purchased online.

For brand and communications leaders, this means ensuring that their products, content, and digital assets can be understood, evaluated, and recommended by AI agents. Importantly, if a brand is preparing for agentic commerce, it is already addressing AI visibility. Visibility is the first step in the transaction journey, an AI agent cannot recommend, compare, or purchase a product it cannot find or understand.

Every significant shift in digital marketing has produced the same pattern: a period of genuine opportunity for early movers, followed by a rush of late adopters who find the best positions already occupied and the cost of entry substantially higher. It happened with search, with social, with content marketing. GEO is following the same curve, with one meaningful difference. In AI search, the advantage compounds more durably than it did in any of those previous shifts. 

The source preference bias documented in the Princeton research means that AI models, once they establish trust in a source, favour it for related queries, and that preference grows stronger over time. The brands building AI visibility now are not simply ahead in the rankings. They are setting the citation defaults that will shape how their categories are understood by AI engines for years.