CMOs are facing a new measurement challenge: proving that visibility in AI search results actually drives sales. A recent survey found that 73% of marketers have invested in tools to monitor their brand's presence in AI overviews and chatbot responses, but linking those metrics to revenue remains an unsolved problem.
"Every CMO we know has been under excruciating pressure over the last 18 months from their boards and C-suites or investors to crack this nut," said John Barham, managing partner at performance media agency Roast.
Tools like SEMrush, Profound, and Scrunch can show how an LLM presents a brand and which sources it uses. They cannot show whether that exposure leads to purchases. "There is no one tool out there that can paint you a picture of the universe," Barham said.
Marketers are building makeshift measurement systems
Without a single platform that connects AI visibility to revenue, marketing teams are stitching together their own solutions. Alicia Yoon, founder and CEO of skincare brand Peach & Lily, said the brand "triangulates" proxy signals to estimate the business impact of appearing in Google AI Overviews and ChatGPT.
At B2B SaaS company Rippling, head of growth Neel Murthy described a setup that combines visibility data from Profound and AirOps, conversion data from paid ChatGPT ads, web traffic from branded and unbranded queries, and a bespoke media mix model built on Google's open-source Meridian platform. "All these systems allow us to triangulate value," Murthy said.
For marketers seeking structured guidance, AI Learning Path for CMOs offers frameworks that help build measurement strategies for AI-driven search channels.
The need for solutions is growing. According to a survey by Demandbase, ChatGPT-referred visits to B2B brands rose 303% from June 2025 to June 2026. Rachel Truair, CMO at Demandbase, said the attribution question has "plagued" marketers for years.
Roast has used Google's Causal Impact model, an open-source Bayesian tool, to connect search inputs and business outcomes. "There isn't the tech or tools to draw a clear line from visibility on a given LLM and sales going up," Barham said. "You have to start going down the modelling route."
Advertisers try alternative levers
The industry is pushing toward standardized solutions. The IAB released measurement guidelines in early August. Google added a Search Console feature in June that provides generative AI performance data.
"The only real way to do it is to model it yourself by putting together your first-party data from pages," said Mulenga Agley, founder and CEO of marketing agency Growthcurve.
Some brands are taking direct action on community platforms that influence LLM results. Seeking.com, the dating app, hired agency Disruptive Advertising to intervene in Reddit threads that gain traction. "It's a little bit of reputation management," said co-CEO Dana Rosewall.
Agencies are also trying to fill the gap. In March, Stagwall's Assembly partnered with startup Emberos to launch a "Search+" solution. Dan Roberts, global SVP of search at Assembly, said the agency is building a tool to track the business impact of a brand's "share" of a given AI model. "We're now starting to look at ways we can integrate more business outcome data into the platform. Conversions, revenues, leads," he said.
Why this matters for marketers
The core problem is that measurement methods for AI search don't yet exist as they do for traditional search with its clear clickthrough metrics. CMOs must either build custom models using statistical tools like Google's Causal Impact or accept that conversion data will be incomplete. "So much is not directly captured," said Yoon. "There's going to be a gap." Barham said marketers should think of search in "probabilistic" terms. "Our teams have had to get more comfortable explaining to clients the value of their work and their media activity when there isn't a CSV they can download from a platform," he said. For more on the broader shift, AI for Marketing explores how teams are adapting attribution methods to an environment where standard analytics platforms fall short.
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