From Share of Voice to Share of Model: Marketing’s next big shift?

Since its inception, digital marketing has largely revolved around a single question: how do you get found?

The answers evolved over time. First came search engine optimisation. Then app store optimisation. Then marketplace rankings, social algorithms and retail media. Each shift created new winners, new metrics and new marketing disciplines.

Now, the rise of AI assistants and recommendation engines is forcing marketers to ask a different question: what happens to traditional strategies when consumers increasingly discover products through AI-generated answers and surfaces rather than search results?

The conversation is being driven by a broader shift in how consumers discover information online. Google is steadily integrating AI-generated answers into search through AI Overviews and AI Mode, while also introducing infrastructure such as the Universal Commerce Protocol to support agentic commerce. At the same time, platforms including Amazon, OpenAI, Perplexity and others are experimenting with AI-powered shopping and recommendation experiences that increasingly sit between consumers and traditional search results.

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Abhay Singhal, Co-Founder of InMobi and CEO of InMobi Advertising, believes the shift is being driven by the emergence of specialised agentic commerce layers that understand consumer intent more directly than traditional search. However, he argues that discovery and purchasing decisions are not uniform across categories.

“For highly involved categories or those tied deeply to self-expression—like fashion, accessories and travel—consumers don't want to be automated out of the experience; they actually enjoy the process of discovery. In these spaces, AI agents exist to make decision-making easier, not to remove the consumer from the picture,” he says.

The implications extend well beyond search. Retail media networks are becoming more intelligent, recommendation engines are becoming more conversational, and social platforms are investing heavily in AI-powered discovery. Meta has integrated AI assistants across its apps, Amazon is building AI shopping assistants, and OpenAI is increasingly positioning ChatGPT as a destination for research, recommendations and product discovery. Together, these developments are creating a future in which discovery may increasingly happen through AI-generated answers rather than lists of links, forcing marketers to rethink how brands are surfaced, evaluated and ultimately recommended.

Dipanjan Basu, Partner at Fireside Ventures, believes AI recommendation systems are changing the nature of brand equity itself. “Brand trust becomes a data asset. Brands will increasingly compete through machine-readable signals such as review authenticity, return rates, sustainability credentials and other forms of verifiable credibility,” he says.

The idea is straightforward. Search engines return links and leave users to do the evaluation. AI systems increasingly attempt to perform that evaluation themselves. Instead of presenting ten options, they may recommend three. Instead of sending users across multiple websites, they may summarise reviews, compare features and produce a shortlist.

If that behaviour becomes mainstream, marketers may need to optimise not just for discoverability, but for recommendation.

The concept is already spawning new terminology. Depending on whom you ask, it is being called Generative Engine Optimisation (GEO), Answer Engine Optimisation (AEO), recommendation visibility, AI discoverability, or even “model reputation management.”

While the vocabulary remains unsettled, the underlying concern is becoming increasingly common among marketers: how does a brand ensure it gets surfaced when an AI system is making recommendations on behalf of a consumer?

That distinction is becoming increasingly important because AI systems rely on a different set of signals than human consumers. Structured product information, third-party validation, authoritative reviews, pricing data, customer feedback and consistent digital footprints may all influence how a recommendation engine interprets a brand.

For some marketers, that means discoverability itself is becoming a strategic capability.

Prashin Jhobalia, CMO of House of Hiranandani, argues that marketers are already adapting to a world where AI and traditional search coexist.

“The dominance of Google Search in the purchase research journey is being steadily challenged by AI-powered platforms, which are fast becoming the preferred starting point for today's discerning consumer,” he says.

According to Jhobalia, AI platforms are indexed differently from traditional search engines and require brands to think more deliberately about how content is structured, distributed and surfaced.

At House of Hiranandani, he says, the company has begun optimising content for both AI-driven and Google-powered discovery, ensuring visibility across both environments.

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For Singhal, the challenge is not simply about optimising for recommendation engines but about learning to operate in an environment where both consumers and AI agents influence outcomes simultaneously. “Marketers will have to move from simply buying eyeballs to actively managing the data feeds and brand agents that make them visible to the AI systems doing the actual shopping.”

The implications extend beyond content strategy. If recommendation engines become an important discovery layer, marketers may eventually need entirely new metrics to understand performance.

Basu argues that the shift could also reshape disciplines beyond advertising itself. As AI systems increasingly rely on third-party sources to evaluate brands, public relations, expert reviews and independent validation may become more influential in determining recommendation visibility. “PR gets reframed as model reputation management. Authoritative third-party citations influence AI outputs, not just human perception,” he says.

Yet some marketers caution against treating AI discoverability as a solved discipline. Karishma Manga Bedi, Founder and CEO of Those Good Distillers, says brands are still learning where AI delivers the greatest value and where human judgement remains irreplaceable.

According to Bedi, AI is already creating efficiencies across consumer insights, creative ideation, planning and data evaluation. However, she warns that marketers must remain careful not to outsource strategic thinking entirely to algorithms, particularly in areas that require originality, cultural nuance and emotional intelligence.

Yet not everyone is convinced that the industry's growing obsession with AI discoverability changes the fundamentals of marketing.

Rahul Vengalil, co-founder and CEO of tgthr, cautions against treating AI recommendation systems as a substitute for brand building. He argues that consumers remain significantly easier and cheaper to convert when they already have a predisposition towards a brand before entering the purchase journey.

“If a person gets into the consideration funnel without having a predisposition towards any brand, then the cost of converting that person becomes super high,” he says.

For Vengalil, the purpose of advertising has always been to ensure consumers enter the buying process with some degree of familiarity, trust or preference. AI may influence discovery and consideration, but it cannot create that emotional connection on its own.

“If a person does not know a brand, no matter how much AI you use, no matter how much influence that you try to do with AI agents and AI recommendation engines, after a point it becomes extremely difficult if you are an unknown brand,” he says.

That argument is echoed by Avirup Mukhopadhyay, Head of Marketing at Victorinox India, who believes the industry risks overstating the role of recommendation engines. “Being found matters less than being chosen,” he says.

According to Mukhopadhyay, AI may reshape how consumers discover products, but it cannot replace the trust, familiarity and emotional resonance that brands build over time. “Algorithms can surface options, but they cannot replicate the emotional resonance that comes from decades of heritage, craftsmanship, and consistent quality.”

His view is that marketers who treat AI optimisation as a replacement for brand building may be solving the wrong problem. “The brands that thrive in an AI-mediated world won't be those that optimise for visibility. They'll be the ones consumers already trust before the AI ever makes a recommendation.”

That tension may ultimately define the next chapter of marketing.

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On one side are marketers who believe recommendation visibility is becoming as important as search visibility once was. On the other are those who argue that while AI may influence discovery, the underlying drivers of consumer preference remain remarkably stable.

The reality may lie somewhere in between.

Search did not eliminate branding. Social media did not eliminate branding. Retail media did not eliminate branding. Instead, each created new routes through which brands could be discovered and evaluated.

AI recommendation engines may prove no different.

What appears increasingly likely is that marketers will need to learn a new discipline alongside the old one. The challenge is no longer simply ensuring consumers can find a brand. It is ensuring that AI systems can understand, interpret and confidently recommend it.

Whether that becomes the next SEO or simply another layer in an increasingly complex discovery ecosystem remains to be seen.

But for an industry built around attention, the possibility that algorithms may increasingly decide who gets considered at all is proving difficult to ignore.