Making Your Brand Matter To The Models | AdExchanger

"AI search is a very different animal from traditional search. You can't buy your way to the top of a large-language model. At least not yet. If you want your brand to show up - and show up well - you've got to play by a whole new set of rules, says Tracy Morrissey, SVP of media and performance at full-service agency Innocean USA, on this week's episode of AdExchanger Talks."

"The most important thing is to create content that directly answers the types of questions people are actually asking. Prompts are an excellent indicator of interest and intent. And brands should also make sure their content appears on trusted, authoritative sites - as in, sources that LLMs frequently reference. Wikipedia, retailer sites and a brand's own website are all great examples,"

"Meanwhile, there's also a growing category of companies that measure how and where brands are being referenced in AI-generated search answers, including Profound, Bluefish and Semrush (which was acquired by Adobe for $1.9 billion just one week after this podcast was recorded). Even so, there's no guaranteed formula for success. "Every LLM and gen-AI tool is going to have their own methodology and reasons and algorithms for delivering on the answer," Morrissey says. "It's just [about] getting as much factual content out there from trusted sources [as possible] - it's the most crucial thing.""

AI search behaves differently from traditional search, and paid placements do not guarantee prominence within large language models. Brands can influence AI-generated answers by creating content that directly answers real user questions and by monitoring prompts to identify interest and intent. Content hosted on trusted, authoritative sources such as Wikipedia, retailer pages, and a brand's own website increases the chances of being referenced by LLMs. Firms now measure AI-driven brand mentions, but each LLM uses distinct methodologies, so distributing factual content across trusted sources is the most reliable approach. Human expertise and orchestration remain necessary amid tool fragmentation and automation.

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