The Interactive Advertising Bureau (IAB) is developing a framework to help the advertising industry measure and credit conversions influenced by AI. The framework is scheduled for release on November 12, according to Digiday, which reported details from an interview with Caroline Giegerich, vice president of AI at the IAB.
IAB wants to “create a shared framework for measuring and crediting AI’s role in conversions,” Giegerich said in an interview with Digiday. The work is focused particularly on cases where traditional attribution signals are reduced, while a working group includes technology companies, publishers, agencies, measurement providers, and brands.
Giegerich told Digiday that the framework will likely separate AI’s impact into two categories: when AI surfaces something to a user at the awareness or intent stage, and when AI helps the user make a decision.
Where AI attribution breaks
IAB has identified gaps in existing measurement systems when AI intermediates customer journeys. At its Measurement Leadership Summit in July, the organisation said AI can research products, compare alternatives, synthesise reviews, recommend options, and increasingly execute commerce.
IAB described one example in which an AI agent completes part of a purchasing journey without encountering the advertisement that originally influenced the consumer. The resulting purchase can contain no observable link to that original exposure, making its contribution difficult to capture through conventional attribution or marketing mix modelling.
IAB also identified a more indirect case. Advertising can influence someone who later creates content or expresses a preference that enters the information an AI system uses to advise another consumer, leaving the connection to the original advertising exposure difficult to establish.
IAB said existing measurement methodologies need not be replaced. Its summit discussed attribution, marketing mix modelling, incrementality, brand measurement, and commerce data alongside emerging measures for AI-mediated activity.
AI shopping creates new measurement signals
Research published by IAB and Talk Shoppe provides evidence that AI is already being used during consumer purchasing journeys. The study combined more than 450 observed AI-powered shopping sessions with a survey of 600 US consumers aged 18 to 64.
Nearly 40% of US shoppers surveyed said they used AI when shopping. Among consumers who use AI for shopping, 46% said they use it most or every time they shop, according to the research.
AI use did not eliminate other steps in the purchasing process. IAB found that 95% of consumers took at least one additional action after an AI shopping interaction before ending their session, while the average number of online steps increased from 1.6 before AI engagement to 3.8 afterwards.
The study also identified a trust gap. Eighty-nine per cent of respondents double-checked AI-provided information before making a purchase, while 46% fully trusted the shopping recommendations they received from AI.
IAB said AI is therefore not necessarily removing steps from the purchasing journey, but creating additional points at which consumers validate information before completing a purchase.
Separate data from Adobe Digital Insights shows that traffic arriving at retail websites from AI sources is already producing measurable commerce activity. Adobe analysed more than one trillion visits to US retail websites and reported that AI-sourced traffic increased 393% year over year during the first three months of 2026.
In March 2026, visitors arriving from AI sources converted into purchases at a rate 42% higher than traffic from non-AI sources, a category that included channels such as paid search and email. Adobe reported that the relationship had reversed from March 2025, when AI-referred traffic converted 38% worse than non-AI traffic.
Adobe’s data measures visits that arrive at retail sites from AI sources. Separately, IAB has identified cases where AI participates in a purchase journey without leaving an observable connection to the original advertising exposure.
The forthcoming attribution framework follows IAB’s release of separate guidelines for measuring brand and publisher visibility on AI platforms on August 3. Its Measuring Visibility in the AI Era framework was developed to address differences between AI visibility measurement providers and the methodologies they use.
IAB said more than 20 companies offer AI visibility measurement tools, with different methodologies capable of producing different results for the same organisation. Its framework sets out a common metrics hierarchy, distinguishes between decision-grade and directional measurement, and establishes disclosure requirements for measurement providers.
The August framework focuses on whether brands, products, publishers, or content appear within AI-powered discovery systems. The November framework reported by Digiday is intended to address how AI involvement can be connected with a conversion and what evidence should be used to assign credit.
IAB’s July summit identified AI inclusion rate, citation share and depth, corpus presence, and rank stability as examples of emerging measurements for AI-mediated journeys. These signals address forms of visibility that are not represented by conventional website metrics such as page views and referral visits.
The visibility framework also covers publishers whose content is ingested or surfaced by AI platforms. IAB said the resulting metrics can help publishers measure how their material appears in AI systems and support content licensing discussions.
Attribution introduces a separate issue when publisher material contributes to information surfaced by an AI system during a purchasing journey. Digiday reported that publishers participating in discussions around the forthcoming framework want their contribution to AI responses considered as the industry determines how credit should be assigned.
“Some of that evidence doesn’t exist,” Giegerich said. She said IAB wants discussions with parties that could potentially provide the missing information.
IAB builds standards for agentic advertising
IAB Tech Lab is separately developing technical standards for AI agents operating within advertising systems. Its Agentic Advertising Management Protocols, or AAMP, cover systems in which autonomous or semi-autonomous agents handle processes including media discovery, planning, buying, and transactions.
AAMP operates at a different layer from IAB’s visibility and attribution work. It defines how advertising agents can identify one another, exchange information, negotiate, and transact across existing ad-tech systems.
AAMP is built around existing advertising standards and taxonomies. IAB Tech Lab said its agentic protocols provide schemas, tools, and reference implementations that allow buyer, seller, and ad-tech systems to use common structures when discovering inventory, negotiating, and completing transactions.
IAB Tech Lab released AAMP 2.3 in July, adding updates covering enterprise deployment, privacy checks, pricing controls, and support for additional advertising platforms.
Agent identity is also part of the work. IAB Tech Lab’s Agent Registry lets organisations register agents, describe their purpose and capabilities, and categorise them, while each registering company is associated with a Global Privacy Platform ID used as part of the validation process.
At its July summit, IAB connected existing event and conversion standards with the measurement questions created by agentic systems. It said the Open Measurement SDK can help establish impression signals, while the Event & Conversion API standardises the communication of advertiser outcome and conversion events.
IAB’s July Measurement Leadership Summit identified event and conversion metadata, taxonomy alignment, machine-readable methodology information, and standards designed for AI agents as potential areas for collaboration between IAB’s Measurement Center and IAB Tech Lab.
The summit also raised possible guidance for distinguishing legitimate transactional agents from malicious automated traffic and bots impersonating trusted agents. These areas were presented as potential future workstreams rather than finalised standards.
IAB said AI cannot compensate for mislabelled inputs, incomplete exposure data, flawed experiment design, or unclear business questions. Summit participants also identified fragmented infrastructure and inconsistent taxonomies as constraints on the use of AI in measurement systems.
(Photo by Steve A Johnson)
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