Why UK marketers need mobile-grade measurement before AI can deliver on ROI

UK marketers are facing a critical juncture as digital ad spending reaches £45 billion this year, with mobile accounting for over two thirds of such expenditure. The rise of retail media and connected TV further underscores the importance of better measurement frameworks. AI and automation are transforming campaign planning and optimization, but they are only as effective as the underlying data they rely on.

Weak measurement frameworks can amplify the problem rather than solve it. The mobile measurement playbook offers a blueprint for improved measurement. The mobile advertising sector faced similar challenges, including privacy constraints, fraud, cross-platform identity issues, and signal loss. To address these concerns, a disciplined approach to attribution, cleaner signals, privacy-safe methodologies, and the combination of different validation methods were adopted.

These principles should now be applied across the entire UK media mix, including retail media, connected TV, social, search, display, and commerce media. The failure to establish transparent standards for measurement can lead to underinvestment in profitable channels, as marketers become hesitant to allocate budgets to areas lacking confidence in outcomes.

A recent study showed that nearly six-in-ten UK marketers believe their organizations are likely to underinvest in profitable channels due to measurement issues. As AI and automation become increasingly integrated into ad workflows, the stakes are higher than ever. Nearly half of UK marketers are under significant pressure to demonstrate clear ROI across all channels, while 29.9% are experiencing extreme pressure.

This creates a heightened accountability challenge, as marketers must ensure the data feeding AI systems is trustworthy. Poor data quality, stemming from fragmented, incomplete, or unreliable signals, is the top barrier to extracting more value from AI, cited by 22.3% of respondents. To navigate this new landscape, marketers must prioritize defining measurement standards before scaling AI investments.

This includes agreeing on key outcomes, attribution methods, incrementality testing, and approved data sources. Assessing partners on transparency is also crucial, as publishers, retail media networks, ad tech vendors, and agencies must explain their signal validation processes, fraud mitigation strategies, privacy management, and the connection between upper-funnel activity and commercial outcomes.

Human oversight remains essential, particularly when decisions carry significant commercial risk. While automation can improve speed, human judgment is necessary to interpret anomalies, challenge assumptions, and determine if AI recommendations align with the broader business strategy. Ultimately, UK marketers should look to the mobile measurement playbook as a model for building resilience.

The mobile sector learned early on the importance of privacy constraints, signal loss, and privacy-safe methodologies, leading to a more disciplined approach to attribution. By adopting similar principles, UK marketers can ensure their measurement frameworks are robust, trustworthy, and capable of supporting the effective deployment of AI technologies.