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New WARC report explores emerging trends in measurement
WARC has released a new report, The Future of Measurement.
AI-powered measurement enables faster, more responsive decisions but poses transparency and control risks, says WARC in its new report.
As marketing measurement continues to evolve, global marketing effectiveness organisation WARC has released The Future of Measurement 2026.
The new report explores the latest emerging trends in media and creative measurement, focusing on three key areas: the shift to outcomes measurement; how AI is moving measurement upstream; and the rise of creative intelligence.
The full report is available to WARC subscribers. The insights are based on a combination of exclusive data from WARC and external research studies and reports.
It is part of WARC Strategy’s Evolution of Marketing, series of in-depth forward-looking reports on the marketing discipline through evidence-based insights and emerging trends, technologies, and other drivers of change.
What to do next
WARC managing editor research and insights Paul Stringer says marketing measurement is no longer just about understanding what happened, but enabling better decisions about what to do next.
“Traditional approaches – based on attribution, proxy metrics, and post-hoc reporting – are becoming less relevant. Rapid advances in AI are enabling a more dynamic, continuous optimisation of both media and creative. However, the foundational challenges of transparency, governance, and data quality need to be addressed.
“This report explores the key trends shaping this new era of marketing measurement highlighting the fundamental questions and decisions that marketers need to act on.”
Key trends
The report identifies the following as the key trends set to shape the measurement landscape over the next 12 months:
Outcomes measurement gathers pace
Media is increasingly bought against outcomes, driven by greater access to data, digital platforms, and ROI pressures. But the ability to measure and optimise against them is developing unevenly across the ecosystem.
Digital platforms are embedding real-time, outcome-based optimisation directly into their advertising systems, while legacy media are still evolving from an audience-based measurement towards proving their impact using experiments and advanced modelling techniques. The result is a two-speed measurement landscape converging on the same goal: incremental growth.
With no single system providing a complete picture, and a lack of trust and transparency in data and attribution, particularly within digital platforms, marketers are advised to make independent validations and take a cross-platform approach that combine multiple data sources and insights to support better marketing investment decisions.
AI moves measurement upstream
AI is primarily being used in measurement to automate data collection, cleaning and normalisation before human interpretation. It can also significantly increase the frequency of testing and modelling for advertisers.
AI promises to move marketing measurement upstream, from a reporting output into ‘decision system’ that supports more dynamic planning and optimisation.
Marketers are rightfully excited about its potential. However, without rigorous, independent validation, AI-driven measurement risks becoming a black box for budget allocation, producing outputs that may appear credible but are not transparent or reliably grounded in true causal signals.
The rise of creative intelligence
Creative quality is a key driver of advertising effectiveness, yet it remains undervalued and undermeasured by marketers making it harder to justify investment. This is changing thanks to advances in AI and machine learning.
Marketers are building creative intelligence capabilities, an integrated system that allows them to measure and optimise creative at scale. This enables them to forecast asset performance ahead of launch, continuously optimise creative assets in real time for engagement and effectiveness, and measure the true impact of creative on commercial outcomes.
However, creative intelligence faces several barriers to adoption, such as poor data quality and a lack of resources. It also demands a closer integration across people, processes and technology – particularly creative and media.
Marketers are advised to unify disciplines and workflows so creative and media work as one operating system. Investing in platforms that support end-to-end creative activation, optimisation, and measurement will be essential. Piloting is expected to begin with social channels, where creative data is easily accessible and closely linked to performance.
