AI Is Compressing Brand Discovery, and Marketing Has Not Caught Up

AI assistants now answer product questions with a short list of recommendations where search engines used to return pages of links. The shift is turning AI brand discovery from a side channel into the front door of consumer choice, and it is forcing marketers to redefine what visibility even means.

Six Indian marketing leaders, from Dabur India, EaseMyTrip, Howl, Glu, DashLoc and Mobavenue AI Tech, told a single story across separate conversations. Visibility still matters. Reputation now decides whether AI names a brand at all.

Fewer Choices, Higher Stakes

Search engines have spent two decades rewarding brands that show up everywhere. AI assistants reward a much smaller group. The new floor is being one of the few names a system chooses to surface.

Rahul Kirpalani, Business Head, Client Success and Planning at Howl, said the compression carries real cost. “Search has always been competitive, but AI has the potential to compress choice even further. If consumers receive three recommendations instead of thirty search results, the cost of not being remembered becomes significantly higher.” His prescription is not to chase algorithms. “That means investing in brand distinctiveness, trust, and increasingly, ensuring these attributes are understood and recorded by assistants,” Kirpalani said. Trade Desk research on AI discovery moments has separately found that fewer active discovery moments now remain for marketers to influence potential customers.

Not every executive sees the shift as a wholesale rewrite. Manmeet Ahluwalia, Chief Marketing Officer at EaseMyTrip, said AI may amplify existing brand equity rather than replace the channels that built it. “If AI-driven discovery becomes more prominent, the importance of building strong and trusted brands could certainly increase. However, consumers are unlikely to rely on a single source of information. Discovery will continue to happen across multiple channels,” he said. Anindo Samajpati, Vice President, Marketing at Dabur India, pushed harder on the risk. “That makes brand building even more critical. For us, that means the focus must stay on product quality, consumer trust, and a strong, consistent brand story across every touchpoint,” he said.

What AI Actually Reads in a Brand

The executives are nearly unanimous on which signals carry weight. Visibility alone is not one of them. Brands that get cited are the ones that read the same way everywhere an AI can look.

Rahul Pandey, Founder and Chief Executive Officer of Glu, said AI weighs clusters of signals rather than any single ranking. “When an AI tool recommends a brand, it is usually responding to a cluster of signals rather than one single factor. A brand may be well known in its category, but if its product information is weak, its claims are inconsistent, or its proof points are scattered, AI tools may not interpret it with the same confidence that a consumer would,” Pandey said. Sumit Singh, Co-Founder and Chief Executive Officer of DashLoc, drew the same line from a different angle. “AI tools tend to favor brands that are easy to verify across the web, not just brands that are well known,” he said. HubSpot’s 2026 State of Marketing report echoes the shift, with marketers across categories ranking trust signals above raw reach for the first time.

Tejas Rathod, Founder and Chief Technology Officer of Mobavenue AI Tech Limited, summed up the new criteria. “Recommendations are increasingly influenced by credibility, consistency, trusted content and accurate information rather than attention alone,” he said.

Put together, those qualities describe how a brand now has to be assembled. Clarity asks whether a brand knows what it stands for. Consistency asks whether the same story survives contact with listings, reviews and PR. Proof asks whether outside sources will back the claim. Structure asks whether product data are formatted for machine parsing.

The New Discipline of AI Legibility

Pandey has a name for the work. He calls it AI legibility. The term is meant to sit alongside an older idea, visual distinctiveness, and add what brand books have missed. AI legibility is a discipline of making sure the right signals reach the right systems, in the form machines can parse.

In Pandey’s framing, the discipline rests on the four qualities he keeps naming. None of them are optional. The list below is what a brand has to build if it wants an AI to surface it on the first pass.

Brands have spent years becoming visually distinctive and emotionally memorable. They now also need to become machine-readable and easy to recommend.

Rahul Pandey is the Founder and Chief Executive Officer of Glu, an Indian brand solutions firm.

• Clarity of claims and positioning
• Consistency across every public touchpoint
• Proof points corroborated by external sources
• Structure of product and brand data so AI can parse it

One Reputation, Read by Two Audiences

A second question came up in every conversation. Do brands now need a different reputation for AI than the one they have for people. The six executives answered the same way. They said no.

Pandey drew the line most clearly. “Brands do not need two different reputations. They need one reputation that can be understood in two very different environments,” he said. What differs between the two, in Pandey’s framing, is the reader of the reputation. “Consumers build reputation through experience, memory, and word of mouth, while AI attaches an evidential entity to the whole realm of reputation,” Pandey said. The brand’s job is the same in both cases; only the reader changes.

Kirpalani made the same point in fewer words. “They’re the same reputation viewed through two different lenses,” he said. He argued that AI reflects the trust signals people already create. “If your brand is consistently trusted by people, spoken about positively, covered by credible publications, and represented accurately across platforms, AI is simply reflecting that reality,” Kirpalani said.

Ahluwalia agreed. “I would not necessarily view them as two separate reputations. The factors that help a brand build trust with consumers, such as credibility, consistency, customer experience, and authentic engagement, are also likely to influence how AI systems understand and represent that brand,” he said. Samajpati at Dabur India put it more bluntly. “We do not see these as two separate reputations. At the end of the day, a brand’s strength comes from consumer trust, and that trust will also shape how it shows up in AI-led discovery,” he said. Rathod closed the loop with his own words: both reputations are “ultimately built on trust.” All six reached the same answer by different routes.

When Every Digital Signal Has to Agree

Once AI treats every public surface as evidence, the siloed structure of most brand marketing starts to fail. PR, content, reviews, listings, structured product data and customer feedback were usually managed as separate functions. In the AI era, they are read as one document, and the brand with contradictions across surfaces loses. Brands whose signals agree across every surface get cited.

Pandey put the rule in plain language. “AI tools also compare claims across sources. A claim that lives only on a brand’s own website, with nothing in the wider web to support it, will carry limited weight,” he said. Singh of DashLoc went further: “A brand that appears differently across listings, websites, reviews, and third-party coverage creates uncertainty, and AI systems generally avoid uncertainty,” he said.

Kirpalani offered the cleanest framing of the shift in AI’s role. Google’s 2026 marketing predictions on AI platforms now describe those platforms as the new ground for brand discovery, and Kirpalani’s read fits the same arc. “AI is a synthesiser rather than an advertiser,” he said, with the work that role now asks for being to combine every public signal about a brand into a single judgment. The push of an advertiser has given way to the pull of a synthesiser. Citation by the system, on the strength of coherent public signals, is the new job.

If customers love your brand, credible sources talk about you consistently, and your digital presence is accurate and well-maintained, you’re creating a much stronger foundation than media spend alone ever could.

Rahul Kirpalani is the Business Head, Client Success and Planning at Howl, an Indian brand experience and digital marketing firm.

What Marketers Should Not Do Next

Three of the six executives warned against treating the shift as another arms race. The temptation, they said, is to repeat the early SEO playbook. Build for the algorithm. Game the system. Move on when the rules change. They argued the AI era punishes that approach faster than search ever did.

Pandey opened the warning. “the best AI optimisation is simply better brand and content infrastructure,” he said. His argument is that the only durable AI strategy is the same brand work marketers should already be doing, with the discipline now extending to making it legible to machines. Singh pushed on the human side. “Some brands will start tailoring everything for AI readability and lose the human side of communication,” he said. His test is whether a brand still sounds like itself. “The goal should be stronger substance and cleaner signals, not content that sounds engineered for algorithms,” he said.

Rathod closed the caution with a longer view. “long-term value will come from expertise, transparency and customer trust rather than technical shortcuts,” he said. The three warnings form one coherent rule when read together: build for legibility over exploitation, and let expertise outlast any optimisation trick. The three warnings converge on the same diagnosis, across three very different industries.

Three cautions deserve to be carried into the rest of the work. Each is named to the leader who raised it.

1. Do not treat AI optimisation as a new SEO arms race (per Rahul Pandey, Glu).
2. Do not strip out the human voice in pursuit of machine readability (per Sumit Singh, DashLoc).
3. Do not chase technical shortcuts over expertise and transparency (per Tejas Rathod, Mobavenue AI Tech Limited).

What Should Be Measured Instead

The shift also changes what marketers count. Click-through rate and impressions measured whether a brand appeared. In an AI-first world, those measures undercount the work. A brand that never sends a click because the AI cited it instead is a brand the metrics cannot see. Ahluwalia at EaseMyTrip held the line on attention. “Attention remains an important part of marketing because brands still need to be discovered and remembered,” he said. Samajpati at Dabur India agreed and added the longer lens. “in categories like ours, trust and repeat usage matter far more,” he said.

Kirpalani made the most pointed forward-looking claim. “We’re entering a phase where reputation becomes a measurable business asset again,” he said, naming customer experience, thought leadership, PR, community building, credible partnerships and long-lasting content as the investments that build it. Attention metrics still have a role, and reputation has to be measured on its own terms once AI becomes the layer that recommends.

Frequently Asked Questions

What is AI-led brand discovery?

AI-led brand discovery is the practice by which AI assistants name a small set of brands in response to a consumer question, instead of returning a page of links. Rahul Kirpalani of Howl described the shift as a compression from thirty search results to roughly three recommendations, raising the cost of being unremembered.

Why does AI compress search results into fewer recommendations?

AI assistants are designed to return a concise answer, not a ranked list. Rahul Pandey of Glu argues that the systems weigh a cluster of public signals, from product information to third-party coverage, rather than any single ranking. The result is a smaller recommendation set built from signals a search engine never checked.

What does “AI legibility” mean for a brand?

AI legibility is Rahul Pandey’s term for the work of making a brand readable to machines as well as to people. He frames it around four qualities: clarity, consistency, proof, and structure. A brand with those four gives an AI a clean document to recommend from.

Do brands need a separate reputation for AI?

No, and the six executives are aligned on the point. Pandey’s framing is the cleanest: brands need one reputation that can be understood in two very different environments. Kirpalani calls those two readers two different lenses on the same underlying trust.

How should marketers measure brand reputation in the AI era?

Kirpalani argues reputation is becoming a measurable business asset again, with measurement built around customer experience, thought leadership, PR, community building, credible partnerships and long-lasting content. Ahluwalia at EaseMyTrip adds that attention metrics still matter, but they no longer tell the whole story once AI is the layer that recommends.