How to Optimize Channel Marketing for AI Search

LLMs are the new front page for B2B buyers

For years, marketers could easily predict the path a buyer might take when evaluating a purchase online.  

If motivated to do their research, they start in a search engine. From there, they end up on a page. Maybe they bookmark it, maybe they download an asset or two, and then move on to evaluate other options before making a decision. Search engines were digital doorways, but buyers basically always walked into the store and checked out the merchandise themselves. 

That model is growing increasingly defunct.  

Even if they don’t plan on it, some sort of AI summary is often the first thing today’s buyers see. According to a survey by Gartner, 60% of B2B buyers consult GenAI tools or chatbots for purchasing decisions.

That means the first impression of your brand is often curated by another company’s technology, delivered in a text-based form factor, and generally positioned alongside other solutions that buyer is considering. That digital front door is now more of a window display, and one that isn’t at all biased towards your organization. 

But applying specificity, clarity, and structure to your marketing approach can prevent your brand from being flattened to the mean. 

Being machine-friendly earns you more human interaction

This shift does not mean marketers should abandon creativity, storytelling, or strong brand experiences. But the application of those skills needs to be context-aware and responsive to the parallel audiences of human and AI readers. 

What works for LLMs doesn’t always work for humans, but you can optimize for both. A buyer will typically want to go beyond an LLM interaction when making a meaningful purchase. As part of a survey Gartner conducted in advance of their CSO and Sales Leader Conference, 69% of B2B buyers prefer to validate AI-generated insights with sales reps.

Successful marketers will need to blend these approaches, considering what appeals to their dual audiences and what those audiences are ultimately looking for. 

For the LLMs, they need structured data and schemas they can easily tap into to extract information and compare it. Typically, this is in the service of a buyer looking to understand and evaluate what’s out there. 

And then for humans, more visible, authoritative experiences are critical—things like demos, case studies, and in-person experiences that focus on building trust and helping cement purchasing decisions. 

Make every claim clear enough to cite

The outcomes of AI search are probabilistic and have flipped how the typical person engages in a digital search. Long-winded questions typed into traditional search engines widened the scope of what was surfaced—not what anyone wanted. The opposite is true for LLMs, which generally offer better answers to questions asked with greater specificity and which include direct follow-up and encourage iterative prompting.  

So, what does that mean practically for marketers? 

AI systems are far more likely to surface and reference content that is specific, explicit, and easy to verify. That means avoiding generic claims and proprietary terms which AI will very rarely seek to paraphrase. 

Focus instead on who a solution is for, what problem it solves, how it works, and what outcomes customers can expect. The more concrete the answer, the easier it becomes for both buyers and AI systems to understand, compare, and potentially cite

Re-think campaigns as part of connected knowledge bases

AI systems rarely evaluate a single asset in isolation. They look for patterns of expertise and supporting information across multiple related sources.  

Fragmented approaches create fragmented signals. Organizations should instead build connected content experiences that reinforce a topic from multiple angles and establish clear topical authority. Campaigns should be considered as steps in a process—layers of a broader, structured knowledge base that makes it easier for LLMs to supply precise answers to the types of questions your buyers will be asking.

Help AI find and understand your content

Even the best content cannot influence AI search if AI systems cannot easily discover, interpret, or attribute it. Strong content should be supported by technical signals that clarify who created it, what it is about, and how it relates to other content across your site. 

This requires closer collaboration between marketing, web, SEO, and operations teams to ensure content is not only useful for readers but also properly structured for discovery. That includes implementing schema markup, maintaining clear metadata, connecting related content through internal links, exposing FAQs and key information in crawlable formats, and ensuring content ownership, publication dates, and sources are clearly identified.  

These signals help AI systems better understand, attribute, and reference your content. What’s more, they afford you an opportunity to inject elements of your brand’s voice into the summaries these LLMs are surfacing. This can be achieved through emphasizing brand language and voice through key quotations or through visual style and alt text for the high-res hero images LLMs commonly reference. 

Learning a new language

These changes are more than SEO best practices adapted for AI. They’re grounded in a shift in how buyers discover information and make decisions. Buyers aren’t browsing your shelves anymore—they’re having mediated conversations with and about your brand. The same qualities that have always made for good marketing are still true: clarity, specificity, authority, and structure all have value. Organizations just need to speak the right language to ensure customers get to actually hear what they have to say.