Three out of 4 marketers say their current approaches to measurement — including attribution, incrementality and media mix modeling — aren’t delivering the speed, accuracy or trust they need. That’s the highest finding from the “State of Data 2026” report by the IAB and BWG Global. Further proof, if any was needed, that marketing leaders under pressure to prove ROI are using systems built for a unique era.
Fragmented data, outdated models and long feedback loops are making it harder to attach media spend to business outcomes. Billions of dollars in investment are being made with incomplete information, often based on models that may’t account for where consumers actually spend time. And with privacy changes and signal loss accelerating, the cracks are widening.
The report reveals a mismatch between legacy measurement tools and where attention is definitely going. For example, 77% of marketers say gaming is underrepresented in their marketing mix models. Commerce media (50%) and the creator economy (48%) are also significantly missed. That kind of underrepresentation results in underinvestment within the channels where consumers are most engaged.
Meanwhile, teams are spending more time stitching together siloed data than generating insights from it. Measurement workflows are still largely manual and slow. The result: missed opportunities, misallocated budgets and marketing plans that don’t match real behavior.
AI’s role in fixing what’s broken
Amid all of the dysfunction, marketers are hopeful that AI can bring meaningful change — not only to automate tasks, but to rethink how measurement works.
According to the report, AI is anticipated to unlock $26.3 billion in media investment value by making measurement faster, more adaptive and more strategic. The shift is already underway in three key areas:
- Speed and frequency: Marketers expect to maneuver from annual or quarterly model updates to monthly, weekly and even real-time feedback loops. Incrementality testing, which has traditionally been run a number of times per 12 months, is shifting to an always-on experimentation model.
- Strategy over spreadsheets: As AI takes over routine data tasks like classification and cleansing, teams anticipate redirecting time toward higher-value work. The report estimates this shift will drive $6.2 billion in productivity gains as marketers spend more time interpreting results and fewer time wrangling data.
- More access to advanced tools: AI helps democratize complex techniques like multi-touch attribution and cross-channel lift evaluation. These models have historically been reserved for advanced teams with the technical skills to administer them. With AI, more marketers can tap into sophisticated insights without having to construct the infrastructure from scratch.
About half of buy-side marketers are already scaling AI inside their measurement programs. Many others are in early-stage testing or proof-of-concept phases. Unsurprisingly, analytics teams are furthest ahead. They’re greater than twice as likely as planning teams to be deploying AI-based workflows — largely because they already work with machine learning models and enormous datasets.
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That gap is narrowing. More than 70% of teams that haven’t yet scaled AI say they expect to achieve this by 2027.
What’s slowing adoption
While excitement around AI is high, trust stays a significant issue. Half of marketers anticipate legal, privacy or accuracy challenges in the following two years. One of the largest concerns is the “black box” problem — when AI-driven insights can’t be explained or traced.
Risk tolerance also varies by role. Executives are focused on cost, ethics and workforce impact. Practitioners are more nervous about execution details — ownership, model governance and making AI work inside existing workflows.
To manage these concerns, marketers are turning to contracts. About 37% of buy-side teams say they’ve already added AI-related language to partner agreements, covering areas like transparency, security and governance. That number is anticipated to double in the following two years, signaling that AI accountability is quickly moving from theory to practice.
What marketers should do next
The IAB’s report outlines a transparent motion plan for marketers who wish to modernize their measurement strategy without introducing unnecessary risk.
Push for standardization and oversight
Shared industry standards — resembling those being developed by IAB’s Project Eidos — may help ensure consistency and transparency across partners. Internally, marketers must formalize human review processes, especially when AI is involved in budget or strategy recommendations.
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Modernize measurement methods
- For incrementality, replace one-off tests with a calendar-based approach and use AI to watch when retesting is required.
- For attribution, commit to regular model rebuilds and use AI to reconcile conflicting data signals.
- For MMM, validate input data before modeling, and make sure the inclusion of channels that are often missed but increasingly vital, like CTV and retail media.
Break down the silos
Rather than treating attribution, incrementality and MMM as separate models, marketers should use AI to cross-reference outputs. Divergences between models can flag deeper issues and help teams converge on a more unified view of what’s really driving performance.
The shift is going on — with or without you
The measurement establishment isn’t any longer sustainable. Marketers can’t afford to depend on systems that underrepresent key channels, delay insights or lack transparency. AI offers a way forward — but not as a layer on top of broken processes. To fully realize its value, marketers must rebuild their measurement frameworks with clarity, accountability and flexibility on the core.
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