A Fundamental Shift in Consumer Behavior
The way people discover brands, products, and services is undergoing a dramatic transformation, and the metrics make the scale of that change unmistakable. In the travel sector, overall traffic is up almost 200% year-over-year since late 2024, while AI-driven travel traffic has surged by an astonishing 2,215%. These figures are not isolated anomalies — they reflect a broad behavioral shift visible across travel, retail, and even business-to-business (B2B) markets.
The underlying driver is straightforward and recognizable from everyday experience: people are increasingly turning to AI tools to make decisions. Over the course of a single summer travel season, a typical consumer may repeatedly use ChatGPT, Claude, or Google's AI to research where to go and what to look for. People want advice — where to travel, what to consider, how to plan — and they are getting it from conversational AI rather than from traditional search. The reported numbers are essentially the aggregate measurement of this real change in consumer behavior, and companies are taking notice because they recognize they must do something about it.
Why "Machine Readability" Now Matters
A crucial and somewhat unexpected concept emerges from this shift: the idea of brand visibility working alongside machine readability. When a person types a request into an AI tool — for example, asking for the best Disney itinerary, where to stay, and which parks to visit — they rarely stop to think about how the system actually chooses the results it returns. But the essential point is that a machine, not a human, is now performing the search.
This reframes a foundational discipline of digital marketing. For years the goal was to be search-engine optimized. Now the imperative is to be optimized so that large language models (LLMs) can read and interpret your content. A website is no longer built only for human visitors; it must also be legible to AI. Strikingly, among the brands evaluated, 80% have content optimized for humans but not optimized for AI readability — meaning a vast amount of valuable content is effectively invisible to the systems now mediating consumer decisions.
The Four Categories of Brand Visibility
A structured framework organizes brand visibility into four categories that companies should address:
1. Brand discoverability. The most fundamental question: can AI actually find the content on your website? Tools in this space perform an audit to confirm that a brand's content is genuinely visible to AI, ensuring that great existing material is not lost to machine searchers.
2. Brand clarity. Most brands offer multiple products and services aimed at distinct audiences — family travel, luxury travel, and business travel, for instance. Clarity means making content legible and readable to AI agents so they correctly understand which products and services to recommend for each category and target audience within a conversation.
3. Brand authority. AI agents do not confine their searches to a brand's own website; they search across the entire internet. Authority therefore depends not only on how a brand's own content feeds in, but on which key, authoritative third-party sites the AI consults for information. These authoritative sources differ by industry and by sub-segment, and identifying them is a core part of a sophisticated visibility strategy.
4. Brand trust. In the social media era, the dominant influencers were people. Now the central question is how to make AI your number-one influencer. Does AI trust your brand? This matters because all of these signals — including the human signals — feed into brand equity, the intangible asset already recognized on a company's balance sheet. Brand equity captures whether a brand is preferred and trusted, and it accumulates over time. In a complex world where consumers are inundated with choices, purchasing decisions are made on the basis of accumulated brand preference combined with what AI now tells them. This is why brand visibility has become a C-level imperative rather than a purely tactical marketing concern.
Tools, Data, and the Discovery of Blind Spots
A new generation of solutions aims to bring trust and measurement to this space. One such offering combines a large corpus of trusted, "living" data with an LLM optimization tool to create what is described as the industry's first trusted solution for understanding what is happening inside AI. The goal is threefold: to understand AI behavior, to take action and improve, and then to benchmark the results — connecting gains in brand visibility back to concrete outcomes like marketing pipeline and revenue. The trusted data corpus comes by way of a recent acquisition of Semrush, which closed only six weeks prior.
In terms of where companies are succeeding and struggling, the area where awareness is first emerging is branded search terms. Companies increasingly understand the value of entering their own brand name to see how LLMs talk about them and to gauge general sentiment. This forms the baseline of the work.
The more strategic step, however, is to examine the entire topic space — understanding all the questions people are asking within a category and whether the brand appears in those conversations at all. Here the acquired data is especially powerful: it brings in over 289 million prompts, constantly refreshed, representing what people are actually asking. This volume of real query data uncovers blind spots — questions and topics that brands did not realize people were asking about. Brands tend to believe they know their customers' questions, but this data reveals the gaps in that assumption. Notable early examples of companies beginning to apply LLM optimization include GM and Dick's Sporting Goods.
The final piece is brand engagement: what happens once a human who has been searching actually arrives on the site. Brands must capture that visitor's intent and use the moment to grow their first-party data.
AI Traffic Converts Better — and Spends More
One of the most striking findings is that AI is now converting better than traditional sources, a notable change year-over-year. Beyond converting at a higher rate, people who arrive via AI also spend more when they purchase.
Was this surprising? The speed at which it happened is surprising, but the fact that AI traffic converts at a higher rate is not surprising once you think it through. When a consumer has been conversing with ChatGPT or another AI platform, by the time they click through to a site they have already done their research and weighed their options. They are clicking through with high affinity. If a person had already dismissed a brand as "not for me," they would not click through at all. The result is that AI delivers high-quality traffic.
There is an important nuance, however. The reported conversion rate is an average — higher than that of regular traffic, but still only an average. Most brands aspire to perform better than average. To do so, they must personalize the experience and determine what would make that visitor return directly in the future. The strategic objective is twofold: first, show up and be visible; second, once the human is on the site, reinforce loyalty so they come back directly next time rather than through an intermediary.
This direct return is a genuine source of revenue. As a brand grows its direct-to-consumer and direct buyer channels, it gains far greater control over its messaging and its brand story through its own owned channels — control that is diluted when discovery and purchase are mediated entirely by third parties. In short, the AI era rewards brands that are simultaneously machine-readable, discoverable, authoritative, trusted, and deliberate about converting high-intent AI traffic into loyal, direct relationships.
