For years, performance has operated within a relatively straightforward premise: identify the right signal, optimize the channel, and measure the outcome. The crown jewels of search queries, keywords, clicks, conversions, impressions, and ROAS have given marketers and brands a sophisticated way to understand what is working and where to invest their next dollar.
However, with the rise of AI, large language models, and AI-powered search results, the way consumers find and evaluate brands is changing, and the tools marketers use to measure those interactions are evolving alongside them. The performance landscape is getting bigger, extending well beyond the channels marketers have traditionally controlled.
In a recent discussion presented by eMarketer's Stephanie Paterik, she described the emerging AI environment as an “ecosystem of signals.” When AI generates a recommendation, it is not relying solely on what a brand says about itself or the advertising dollars it puts behind its message. Instead, it draws from brand websites, publisher content, customer reviews, and other authoritative sources to develop an understanding of the brand and the category in which it competes.
A brand's AI visibility increasingly depends on the broader set of information that surrounds it, not just the content and media it directly controls.
The AI influence layer is getting bigger
As AI becomes a layer between customers and the information they are seeking, third-party sources become part of the data AI uses to determine which brands and products should surface.
Consider the role publishers play. AI draws heavily from authoritative reporting, trusted product reviews, and category-specific expertise when it builds a picture of a brand. That gives publishers new relevance, and it gives brands and agencies a reason to rethink those relationships. You need a credible presence in the sources that AI systems actually pull from when evaluating a category. That means investing in earned media, contributing expert commentary to third-party publications, and making sure your own content is structured well enough for AI to parse.
Reviews carry similar weight. AI recommendations lean on review sentiment and volume across high-authority platforms, not just your own site. Actively cultivating reviews directly shapes how AI represents your brand.
And this extends beyond media into the customer experience itself. At the recent Marketecture Live event, Julia Fedor, Director, Brand Marketing Operations at United Airlines, shared that in a survey of United customers, customer experience ranked as the most important factor. In an environment where AI is synthesizing that story from sources across the web (from news articles to Reddit discussion threads), brands have less ability to control the narrative and more incentive to make sure the underlying experience actually lives up to it.
AI visibility shouldn’t replace brand equity
If AI is becoming another layer through which consumers discover and evaluate brands, it’s tempting to think that the answer is simply making sure your brand shows up in AI. But visibility is not the same thing as consideration, and consideration is not the same thing as conversion.
Consumers still seek out brands they know, those that have built trust over time, even when they don’t show up in an AI search. Because there are a lot of reasons a brand may not surface in every AI-generated recommendation. The model may draw from a limited set of sources, or the query may be interpreted differently than a traditional search. But that doesn’t remove the brand from the consumer's consideration set. A consumer who already knows the brand, understands what it stands for, and has had a positive experience with it may still seek it out, even without an AI recommendation.
That is where brand equity becomes even more important. The work brands have historically done to build awareness, trust, and associations doesn’t disappear because the way consumers discover information is changing. In some ways, it becomes more important because consumers are receiving answers with context instead of just a list of links. The stronger the foundation a brand has built, the more likely it is to remain part of the consideration set even when the exact path to that consideration is no longer visible.
Research from BCG puts a business case behind this. Across hundreds of public companies, the research found that for every near-term dollar of brand investment cut, companies would need to reinvest $1.92 to recover lost market share. That means brand and performance are not as separate as marketers have historically treated them. The equity built over time becomes part of the performance engine, even when we can't draw a straight line from an individual exposure to an individual conversion.
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First-party data becomes the bridge between signals and activation
The path a customer takes to a decision may span a publisher review, an AI assistant, a Google search, or a longstanding brand preference. And that path can cross a wide range of signals that no single marketer controls.
But brands have access to something incredibly valuable: what they know about their own customers. They know what people buy, how they behave, and, in many cases, what signals tend to precede a purchase. That information can help give AI systems better context for understanding intent. Rather than expecting AI to understand intent in a vacuum, marketers can use first-party data to help guide these systems toward the customers and moments that matter.
We can see this starting to play out in products like Google's AI Max for Search. Instead of relying solely on the exact keywords a marketer has manually identified, AI can interpret broader intent and identify additional opportunities to connect with people based on what they’re looking for. Marketers can use what they know about their customers to help these systems make better decisions, even when the full path to purchase isn't visible.
First-party data helps close that loop. It gives marketers something grounded in their own customers that can inform activation, help AI identify intent, and create a stronger connection between what a brand knows and how AI interprets demand.
The agency role in AI-powered marketing is changing too
So what does this mean for agencies? One of the bigger questions we should be asking is how agencies control or optimize against channels and decision layers that are beyond their traditional purview. Historically, if performance was happening within search, social, display, or video, agencies could directly influence the levers that shaped the outcome. They could change the audience, the creative, the bid, or the budget, and then measure what happened.
That scope gets a lot wider when agencies recognize that part of the decision is happening beyond the media plan. A customer may see an ad, read a publisher review, ask an AI assistant, search for a product, and then make a decision based on a combination of all of those signals. Agencies don't need to own all of those inputs, but they do need to understand how all of these channels work together and where they can influence the outcome.
That also changes how we think about measurement. When customer decisions are shaped by a wider set of inputs, measurement needs to match that complexity. Directional reporting, incrementality testing, and marketing mix modeling become more important here. They give us ways to rise above the individual touchpoint and understand whether the broader ecosystem of investment is influencing business outcomes.
The agency role is becoming more about connecting available signals with owned data and the channels where agencies can make an impact. We can move beyond just asking “what happened in the channel?” to include “what’s influencing the decision, and where can we make an impact?”
The definition of performance marketing is getting bigger
The set of signals influencing customer decisions now extends well beyond the channels marketers buy and the content they control. Brand equity, customer experience, publisher authority, first-party data, and the growing number of AI systems sitting between consumers and the information they need all play a role.
Marketers and agencies don't need to control every piece of that landscape, but they do need to understand it well enough to know where they have influence, where the opportunities are, and how to use the data and technology available to act on them.
We help brands and agencies connect measurement strategy, first-party data, and media activation across a complex landscape. If you're rethinking how to measure performance in an AI-driven environment, we'd love to talk.
