Agents are the most important change to purchasing since the rise of search. They now help buyers discover, compare and, increasingly, purchase a brand’s products and services. Yet marketers have no rigorous way to measure how those interactions impact the marketing funnel, and they know it: 86.6% of marketing leaders are prioritizing AI search, but only 23% feel confident measuring its impact (source). Until that changes, AI search will stay a test program instead of a scalable growth investment.
Today, a group of top brands and measurement leaders, led by AirOps and including Paramark, Chime, and Hitesh Wadhwani, the co-founder of Google Meridian, are introducing the AI Search Measurement Standard.
I’m relocating to Paris for work. What should i know ahead of time to make banking and personal finances transfer straightforward?
Searching the web…
I live in London and get paid in euros. Which account will help me avoid conversion fees
Searching the web…
We’ve moved in together. What’s the best joint account for rent and bills in the UK?
Searching the web…
The focus of this effort is to identify and standardize the components needed to understand the revenue impact of AI search. This open standard is still being created, but we’ve seen promising results over the last few months as we’ve worked to define input data required to represent AI Search in different marketing attribution models. We look forward to seeing it mature as the standard evolves and more brands help shape it.
The problem to be solved
Marketing measurement and attribution is one of the most complex and varied systems in businesses today. Teams use different methods, with different levels of rigor, depending on their stage, size and industry. At the same time, marketers want to invest in AI search now. Their buyers are already there, and no one wants to fall behind. But to invest with confidence, they need a shared standard of measurement and clarity on how AI search ties to their pipeline and revenue goals. Rigorous measurement of AI search requires three main components:
- Data: AI platforms release very little direct data today. Panels of real AI conversations and tracked answers can provide a useful proxy for exposure, but can misrepresent the total impact of the channel if not calibrated accurately. Mapped to the topics a brand cares about and tested in its own measurement, the data becomes meaningful. Taken alone, it can overstate impact. What’s needed is shared agreement on which data inputs matter and how they are used. As AI platforms share more data with brands, we expect the agreement to only get stronger, just as marketing measurement matured alongside Google’s data sharing for traditional Search over the years.
- Methodology: Brands already have proven ways to measure marketing, including marketing mix models and incrementality testing. What’s missing is an agreed way to represent AI search inside them. Without this, AI visibility tools and measurement platforms are all trying to build their own approach. Brands end up with numbers that are hard to compare and hard to defend on spend. No measurement is perfect, but a shared approach is how AI search earns the same trust as every other channel.
- Actionability: Even brands that track their AI visibility closely can’t make confident decisions about AI search. They can see where they appear, but not what that exposure is worth next to the channels it competes with for budget, so they can’t tell whether to invest more, hold steady, or pull back.
None of these problems are easy to solve, and getting them right will be an iterative process. That’s why we’re bringing measurement leaders, AI platforms and marketers together to forge the path.
Who’s building it
The AI Search Measurement Standard is being built by marketers and measurement leaders who have spent their careers defining how marketing gets measured and investing in what works.
The first component is AI Search Impressions, a standardized unit of measurement for AI search exposure designed to help marketers understand not just whether a brand appeared in AI answers, but how much that exposure was worth. Paramark, a leading marketing measurement platform, co-developed the AI Search Impressions methodology and is the first to bring AI search into a marketing mix model.
Chime, working with Paramark, is the first brand to value AI search alongside every other channel it measures. Hitesh Wadhwani, co-founder of Google Meridian, joins as an outside measurement expert, bringing his experience building measurement at Google to shape the methodology end to end.
“No CFO funds a channel because it’s visible. They fund it when you can show what it adds next to everything else in the budget. Building this with AirOps and Chime meant starting from the decision a marketer actually has to make, whether to spend more on AI search or not, and working back to the data and the model needed to answer it.”
“For the past two years, the industry has over-indexed on visibility as the key metric for AI search, but what we truly need to understand is what that visibility is worth to the business. As third-party publishers and other sources beyond our own websites play a bigger role in shaping which brands AI agents select and recommend, we need a clearer understanding of the return on our investments in those channels. Developing a shared measurement standard will help us better understand that broader business value, beyond what we attribute directly to organic and AI search.”
“One consistent learning from over a decade in marketing measurement is that every emerging channel must mature through the same three milestones: standardizing raw exposure data, calibrating the methodology, and translating results into actionable budget recommendations. A channel only becomes truly investable when the industry agrees on a rigorous, common currency. Right now, AI search is caught between uncalibrated visibility scores and broken click-based attribution, and rather than replacing one black box with another, we must translate vanity metrics into defensible, incremental business impact. By initiating this journey to establish an open, econometrically sound standard for AI search exposure today, our aim is to give the industry the empirical confidence to treat AI search not as an unmeasured experiment, but as an accountable growth engine.”
Supporting participants
Supporting participants include G2 and over a dozen other brands including Clay, Sanity, Turo, Linqia, LegalZoom, Sprout Social, and UserEvidence.
“Trust has become the ultimate differentiator in the age of AI. While verified customer reviews have long helped human buyers make confident software decisions, they’re now playing an increasingly important role in how AI answer engines recommend software. For software brands, showing up with trusted, authentic customer voices is essential to winning the answer, whether the buyer is human or AI. And for marketers, understanding and measuring how winning those answers ties to real revenue is critical to competing in this new era of software discovery.”
