Is AI the gatekeeper for brands and customers?

AI is no longer just a channel. It has become the new gatekeeper, determining which brands customers see first. As discovery shifts from traditional search engines and social feeds to ChatGPT, Gemini, and AI Overview, the algorithmic shelf that surfaces recommendations increasingly dictates who is considered, and who is overlooked.

If AI cannot see your brand, neither can your customers. This simple shift is already reshaping marketing economics across healthcare, BFSI, FMCG, automotive, hospitality, and other sectors.

AI As the Algorithmic Shelf

The mechanics are stark. Brands with unified clean and crawlable data consistent narratives and credible third party signals are being favoured by AI systems. The shift is already reshaping how marketing teams operate on the ground.

Dr Ashish Bajaj, Group Chief Marketing Officer at Narayana Health, observes the speed of this transformation, "Marketing has become weightless." He explains that earlier, "We used to wait at least 15 days for the campaign to work" whereas today "results surface almost immediately and targeting has moved from broad segments to near person level."

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Bajaj recalls a decisive reallocation in his own organisation where he "brought 6 crore monthly spend on performance marketing to close to zero and added only 2.5 crores back to brand building" a move that delivered rapid organic growth and reduced uninstall and bounce rates from 75% to between 20- 29% in just four quarters.

His experience underscores a wider truth emerging across industries. Winning AI recommendations is no longer a matter of keyword optimisation or paid discovery but about demonstrated trust, measurable outcomes and public credibility. Shubham Chaudhary SVP and BU Head at PolicyBazaar places this in perspective saying "As long as you have the best product in the market the demand will always be there." His view reframes growth strategy for an AI-first world where retention and product strength secure short term stability but algorithmic visibility drives long term scale.

The economic asymmetry becomes sharper when AI’s impact on acquisition costs is considered. Sourya Public Policy Communications Consultant at Jajabor Brand Consultancy explains that "AI may cut acquisition costs by 30–37% as per current estimates but only for brands with clean data that has been crawled and consistent narratives." He warns that "If your products are not coming up in these searches then you are still relying on traditional acquisition strategy." That invisibility becomes an expensive handicap pushing companies back into high-cost performance channels and exposing them to rising CAC pressure.

Retention As Fuel for Discovery

Retention remains invaluable. In long-lifecycle categories such as insurance or medical devices, existing customers and renewals deliver the most efficient revenue in the near term. However, retention alone no longer guarantees growth. Younger cohorts, Gen Z and Gen Alpha, are increasingly beginning their journeys with AI assistants and do not default to family choices or legacy brands. They compare options instantly and select what the algorithm presents as most credible and relevant. In this context, retention becomes the raw material that powers AI recommendations.

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That change alters how marketers must measure outcomes. Positive clinical results, service quality, verified user reviews, and third-party validation feed the signals that AI digests and amplifies. The healthcare devices executive who described how generic searches now surface multiple manufacturers summed up the effect, saying: “I would say positively disrupting.” He also noted that lead quality has improved, adding: “If we get 50 leads, it will be five jobs,” meaning the funnel is narrower but more qualified.

The policy and governance overlay makes this operationally urgent. As India implements its DPDP Act, data minimisation rules will constrain retargeting and limit predictive personalisation. Sourya warns that evolving privacy frameworks are changing the mechanics of acquisition, making publicly trusted assets more important than ever. Brands will need first-party consent flows and crawlable, authoritative content, as retargeting that relied on broad behavioural data will no longer be straightforward.

A New Playbook for Growth

For CMOs and growth leaders, the mandate is both tactical and strategic. Content pipelines must be designed for crawlability and structured clarity. Product metadata schemas, verified review engines, and partnerships with authoritative publishers are no longer optional. Data hygiene and a single source of truth are now table stakes. This work transforms retention into discoverable evidence.

Operational examples already exist. Bajaj highlights the importance of publisher partnerships and credible sources, saying: “You need credible sources to write about it, where I provide the right data to them and they tell a good story about it.” This network effect of structured content, third-party validation, and user reviews creates a positive feedback loop in AI recommendations.

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The insurance sector illustrates the balancing act. Chaudhary notes that “retention marketing happens to be a big chunk of our total business” and that repeat customers generate outsized value in the short term. At the same time, he concedes that channels are saturated and staying ahead of technology is essential to maintain recommendation share. He adds: “If you don't stay ahead of the curve, or if you don't stay abreast of the technology, you may dwindle, these apps may stop suggesting you.”

A strategic divergence in cost dynamics is emerging. One group of companies will institutionalise algorithmic trust readiness, benefiting from lower customer acquisition costs, improved retention economics, and sustained discovery. The other group will maintain traditional acquisition strategies, incur higher media costs, and risk a gradual erosion of market relevance. The gap could prove as consequential as the SEO divide in the early internet era.

What Leaders Must Do Now

The first priority is audit and repair. Brands must understand what AI systems see when querying their category. This involves cataloguing crawlable assets, verifying data feeds, and ensuring narratives are consistent across owned pages, partner sites, and publishers. The second priority is converting retention into recommendation. Systems for soliciting verified reviews, demonstrating outcomes, and amplifying credible third-party validation are essential. The third priority is partnerships. Publisher relationships and authoritative content placement remain vital, as AI models rely on trusted sources when generating summaries and recommendations.

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These changes also demand new measurements. Visibility in the knowledge graph must be considered alongside impressions and click-through rates. Proof of outcomes and the velocity of verified reviews should be included in growth scorecards. Leaders must be willing to reallocate budgets from one-off performance experiments to durable content credibility infrastructure.

Broader questions remain about who sets the signals that determine market outcomes and how transparent those signals are. Public policy debates and platform governance will shape the rules of discovery. For now, the commercial imperative is clear.

The bottom line is clear. In a market where AI guides the first moment of discovery, brands that fail to appear in AI-driven recommendations effectively disappear from the consumer’s consideration set. Invisibility to AI forces companies back into traditional acquisition channels, which are slower, more expensive, and increasingly inefficient.

Brands that invest in data credibility and build the content infrastructure required for algorithmic visibility will secure a long-term cost advantage and stronger discovery momentum. Those that do not evolve will face rising acquisition costs and a gradual loss of relevance, as consumers shift decisively toward AI-filtered decision journeys.