IAB consolidates measurement standards and AI efforts under new “Project Eidos”

The Interactive Advertising Bureau has launched Project Eidos, a new umbrella initiative designed to consolidate and formalize its advertising measurement work as the industry moves deeper into AI-driven planning, buying, and analytics.

Project Eidos brings together a range of existing IAB measurement programs—spanning identity, attribution, retail media, and marketing mix modeling—under a single framework. The initiative reflects growing concern among advertisers and agencies that despite increasingly sophisticated tools, confidence in measurement outcomes is declining.

According to IAB executives, the industry’s current approach relies on fragmented standards, inconsistent definitions, and manual reconciliation across platforms. These gaps have become more visible as AI systems take on a larger role in campaign optimization and reporting, often amplifying inconsistencies rather than resolving them.

IAB estimates that inconsistent definitions, incompatible data feeds, and non-standardized inputs cost the industry at least $9 billion annually, largely through duplicated manual work and inefficient reporting processes. Even widely adopted techniques such as incrementality testing and media mix modeling are limited by the lack of shared taxonomies and comparable inputs across channels.

Basic questions—such as how to classify connected TV, advanced video formats, podcasts, or livestreamed content—remain unresolved. As media formats converge, these ambiguities increasingly affect budget allocation, performance analysis, and cross-channel comparisons.

Project Eidos is intended to address these foundational issues by harmonizing how channels, formats, and outcomes are defined and measured across the ecosystem.

The launch of Project Eidos coincides with the release of IAB’s State of Data 2026 report, based on a survey of more than 400 advertiser and agency decision-makers. The findings show that around half of the buy side is already scaling AI within advanced measurement systems, with roughly 70% expecting to do so within the next one to two years.

However, the same respondents reported significant shortcomings. Between 60% and 75% said current advanced measurement approaches fall short on rigor, speed, trust, or efficiency, and none believed that all paid media channels are adequately represented in today’s marketing mix models.

The buy side estimates that AI-enabled improvements to measurement could unlock more than $26 billion in media investment and over $6 billion in productivity gains in the near term. At the same time, concerns around data quality, legal exposure, security, and model accuracy remain largely unresolved, with fewer than 40% of organizations reporting mitigation plans.

Rather than introducing a new standalone methodology, Project Eidos focuses on coordination and standardization across existing approaches. The initiative centers on three core areas:

• Unifying and harmonizing measurement through shared classifications, structures, and data flows.
• Cross-channel outcomes, attribution, and incrementality, with flexible frameworks that connect exposure to results across media types.
• Modernizing marketing mix modeling, using standardized, privacy-ready inputs to enable consistent ROI analysis and budget guidance.

IAB positions Eidos as an industry-wide effort involving brands, agencies, platforms, publishers, and measurement providers, rather than a program driven by any single constituency.

Dozens of companies across the advertising ecosystem have joined the initiative, including major agency groups, platforms, media owners, and verification providers. A new Measurement Advisory Committee has also been established to guide taxonomy development, standards-setting, and interoperability work.

Initial efforts will focus on aligning terminology and data inputs, with further work on standardizing MMM and cross-channel measurement planned later this year. For advertisers increasingly reliant on AI-driven decisioning, the success of Project Eidos may determine whether automation delivers clearer insight—or simply scales existing measurement blind spots.