The biggest problem standing in the way of AI-driven ad campaigns isn't the technology itself but the lack of agreement among companies on shared standards. Jon Watts, who leads measurement efforts at CIMM, argues that the industry must first resolve foundational issues before AI can truly transform media planning, buying, and measurement. Without consistent rules and definitions, AI risks creating more confusion than clarity.
Data accuracy struggles
AI systems base their decisions on the data they receive, and poor data quality threatens the reliability of those decisions. A recent study by CIMM, in collaboration with Truthset, looked into how well identity providers match IP addresses to actual households. The results were alarming: on average, providers achieved just 13% accuracy. This low level of precision means AI might optimize campaigns based on faulty information, leading to misallocated budgets and ineffective strategies.
Measuring long customer journeys
For television advertising, measuring the impact of campaigns often requires tracking long and complex customer journeys. For example, someone interested in buying a car might see the same ad for months before making a purchase. But the industry lacks a standard way to define how long those earlier exposures should count in measuring campaign success. This makes it hard to connect advertising exposure with business outcomes, weakening the ability to demonstrate an ad's real-world impact.
Watts emphasizes that AI can only help when the industry agrees on common standards and definitions. If platforms and data providers use the same rules and protocols, AI can act as a powerful tool to streamline transactions between advertisers, buyers, and sellers. But until the entire ecosystem aligns on basic principles, AI won't deliver the efficiency and transparency expected.
The companies most likely to thrive in this evolving landscape are those that can clearly and consistently show how media investments lead to measurable business outcomes. This means proving that ad campaigns drive sales, awareness, or other important metrics in a transparent and repeatable manner. Advertisers are no longer focused just on impressions—they want to see results. The future will belong to those who can bridge the gap between media exposure and real business impact.
As the industry moves toward outcomes-based advertising, the need for standardized measurement and data quality becomes even more urgent. CIMM is working to address these issues by developing common frameworks and methodologies. But as Watts makes clear, technology alone won't solve the problem. The real challenge lies in getting the entire advertising ecosystem to adopt and follow a shared set of standards.