A little more than a year ago, we predicted that LLM-powered search would fundamentally disrupt the buyer journey. It has. Across the US, for example, 44% of online buyers surveyed by Bain & Company mostly start their journey in an LLM or split their search between AI tools and traditional search engines.

Although we see roughly twofold faster adoption among Gen Z and millennials compared with baby boomers and the Silent Generation, this is not niche behavior confined to younger generations (see Figure 1). The shift to AI-mediated search has moved faster than the rise of social shopping or e-commerce search and has now spread to B2B markets as well. Marketers who view the change as merely an update to SEO are already behind the curve.

The inflection point

Traditional search dominance in consumer discovery has gradually been hollowed out over the past decade. E-commerce marketplaces, social shopping, and video channels have each carved off a piece of search for consumers of all ages. But the rise of AI-powered search differs in kind, not just degree.

The rise of conversational, natural language interactions that allow for long, context-heavy queries has created a more personalized discovery experience for consumers. In parallel, those who still use traditional search engines are increasingly relying on AI-generated summary information rather than clicking through to links on the page. AI overviews, Q&A carousels, and paid placements are burying blue links below the fold in nearly every category. Half of online shoppers in our survey trust generative AI for initial research and product comparisons, although this varies among product categories and consumer age cohorts (see Figure 2). For chief marketing officers (CMOs), this means that the page-one real estate they’ve invested years in building has become less valuable.

Figure 2

B2B buyers have started to embrace the bots

Turning to B2B markets, our research suggests that buyers at small and medium-size businesses have already started to build their vendor short lists inside LLMs. They’re using AI to construct the consideration set, then turning to websites, review platforms, and YouTube demonstrations to validate what the model suggests. If a vendor’s brand doesn’t surface in that first AI-generated list, it may never make it to the validation stage.

Extrapolating these buying behaviors to large companies, which already incorporate digital discovery into their vendor list development, we expect significant dislocation in the sales and marketing funnel. CMOs with enterprise business customers should engage with their C-suite colleagues and prepare for this trend to start migrating into high-value enterprise deals.

Half the battle is just showing up

Our work with agentic AI platforms consistently shows that the sources LLMs rely on to build recommendations overwhelmingly consist of nonbrand-owned media. Third-party review sites, industry publications, analyst commentary, social platforms, and affiliate published content dominate, not a company’s own home page, blog, or paid ads. An analysis of proprietary ScrunchAI search data spanning about 500 million citations showed that 89% of unbranded prompts (which do not mention a specific brand) are fulfilled by third-party sources. The dispersion by topic is 76% to 99%, far more than the 89% to 90% dispersion by AI platform.

Marketing resource and expense allocation thus requires a structural reset. Classic search optimization, search marketing, and lower-funnel conversion tactics become necessary but not sufficient. The sources LLMs trust look more like a strong public relations and earned media strategy than a performance marketing dashboard.

The brands making the most progress in AI-driven discovery focus their investment in three areas:

• category fame, being the brand that’s reflexively cited when an LLM answers a category query;
• accurate brand portrayal, ensuring that third-party sources reflect its current positioning, not outdated or incorrect descriptions; and
• content freshness and LLM readability, restructuring site content and creating dedicated pathways that AI crawlers can parse cleanly.

Organizing for a cross-functional approach

Many leading companies are starting to rethink how they organize their marketing operating model. A traditional siloed structure with SEO in one lane, public relations, marketing communications, and influencer marketing in another, and web and content generation in another cannot move at the speed or with the coherence that this shift demands. It’s more effective to redesign ways of working cross-functionally to incorporate shared performance metrics, unified messaging governance, and rapid experimentation across earned, owned, and technical teams. In practice, this becomes less a campaign and more a capability. Marketing communication tiger teams should serve not as temporary task forces but as enduring components of the capability.

We see leading organizations creating a playbook that ranges from no-regret optimizations to bigger structural bets:

• Measure generative engine performance across top personas, categories and prompts, tracking, share of voice, citation frequency, and sentiment trajectory across engines.
• Revamp on-site content strategy for LLM readability and top prompt and category coverage, ensuring that high-quality structured content is available, fresh, and crawlable.
• Increase engagement with and investment in earned media, affiliate management, and influencer and reputation management for top-cited sources to shape what third parties say about the brand.
• Bifurcate one’s own website to create dedicated bot-optimized journeys designed to answer complex queries without polluting the human-centric experience with dense AI-oriented content.
• Explore application programming interface integrations and agent-powered partnerships with leading LLMs to ensure real-time accuracy of product information in chat and access to transaction-enabling flows.

Of course, the models will evolve, the sources they weight will shift, and the strategies that work today will need to adapt. Companies should aim to better understand how their customers’ needs and behavior are evolving along the discovery-to-purchase journey. They’ll need to improve their experimentation capabilities and ensure that their operating model can move in an agile fashion.

To that end, CMOs who want to lead at the agentic frontier should prioritize the following questions:

• Where are we acquiring our next set of customers? How much of that path now runs through an AI interface we don’t control?
• What is our current presence across the AI engines our buyers use? Is that presence accurate, favorable, and consistent with our positioning?
• If AI is constructing our buyers’ short list, are we shaping that short list or are our competitors?

Engaging the marketing team on these questions will help brands identify risks throughout the current funnel and gauge the level of urgency for attracting and converting customers in the agentic era.