WARC report flags shift to outcomes & AI measurement

WARC has released a report on emerging trends in media and creative measurement, finding that marketers are shifting towards outcomes-based measurement and making greater use of artificial intelligence.

Titled The Future of Measurement 2026, the report examines three areas shaping the sector: the move to outcomes measurement, the use of AI earlier in the measurement process, and the growth of what WARC calls creative intelligence.

Its findings point to a market changing at different speeds. Digital advertising platforms are increasingly embedding outcome-based optimisation into their systems, while legacy media owners are still moving from audience measurement towards methods designed to show business impact through experiments and modelling.

This has created what WARC describes as a two-speed measurement landscape. Both sides are moving towards the same goal of linking advertising activity more directly to incremental growth, but with different data, methods and levels of maturity.

The report argues that no single measurement system yet offers a complete picture. It also highlights concerns over trust and transparency, particularly in platform data and attribution models, and recommends independent validation and the use of multiple information sources when assessing marketing investment.

Paul Stringer, Managing Editor Research and Insights at WARC, said: "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 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."

Outcomes focus

One of the clearest themes in the report is the broader shift towards buying media against outcomes rather than reach or audience metrics alone. This is being driven by improved access to data, the structure of digital platforms, and growing pressure on marketers to demonstrate return on investment.

Yet measurement against outcomes remains uneven across the market. In digital environments, platforms can tie ad delivery and optimisation more closely to user actions and conversion signals. In more traditional channels, proving the commercial effect of advertising often still depends on econometric analysis, experiments, or other advanced methods that can take longer to build and validate.

The result is a fragmented system in which marketers may need to compare findings produced through very different approaches. That, in turn, raises questions about consistency and confidence in the numbers used to guide budget decisions.

AI in measurement

Artificial intelligence is another central theme. It is currently used mainly to automate data collection, cleaning and normalisation before humans interpret the results. AI can also increase the frequency of testing and modelling for advertisers.

According to the report, the next step is for AI to move measurement upstream from a reporting function to a decision system used in planning and optimisation. That would make measurement more immediate and more closely linked to campaign changes while activity is under way.

But the report also warns that this shift carries risks. Without independent validation, AI-led systems could become opaque tools for allocating budget, producing outputs that look credible while offering little visibility into how decisions were reached or whether they rest on genuine causal signals.

These concerns reflect a wider debate in advertising and analytics over the growing role of automated tools in business decision-making. As more marketers rely on machine-led analysis, scrutiny of governance, transparency and data quality is likely to increase.

Creative measurement

The report also places greater weight on creative quality as a driver of advertising effectiveness. Creative has often been undervalued and undermeasured, making it harder for marketers to justify investment in the work itself rather than in media distribution alone.

Advances in AI and machine learning are beginning to change that by allowing marketers to assess creative assets at scale, estimate likely performance before launch, and refine material in real time against engagement and effectiveness signals. WARC refers to this broader system as creative intelligence.

Even so, adoption faces practical barriers, including poor data quality, limited resources, and the need for closer alignment between creative and media teams. Social channels are identified as an early testing ground because creative data there is more readily available and more directly linked to performance measures.

The study draws on WARC data as well as external research and forms part of a wider series on changes in marketing practice. Stringer said transparency, governance and data quality remain the key issues marketers need to address as measurement systems become more automated and more closely tied to decision-making.