AI has quickly become an integral part of ad tech solutions. For publishers, however, the key question is not whether a platform uses AI, but what AI can actually do, what its decisions are based on and whether it genuinely delivers better results. In ad monetisation, individual decisions cannot be considered in isolation.
A higher floor price can improve CPM while reducing bid participation or fill rate. A shorter timeout can improve page performance while excluding valuable demand. A change made in Prebid can also affect what happens in the ad server. Making better decisions therefore requires an understanding of the monetisation process as a whole, in addition to individual metrics.
This is also the starting point for our own AI development: what matters is not the model itself, but the data and ad tech expertise behind it, an understanding of each publisher’s own environment and the ability to demonstrate the value AI creates.
Start with the Data, Not the Model
AI decisions are only as good as the data they are based on. In ad monetisation, auction data alone is not enough. The full picture comes from combining information about auctions, delivered impressions and revenue with the context in which the auction takes place. Data granularity also matters, as demand behaviour and placement performance can vary significantly by device, market, format, or placement, even within the same publisher. This means that a single generic model may not work for every publisher. Models need to learn from each publisher’s own data and account for differences in placement, device and the broader auction context when making decisions.
A concrete example is the machine learning-based floor price optimisation that will be introduced to Relevant Yield in the near future. Effective optimisation cannot be based on CPM alone. It also needs to account for bid participation, auction pressure, fill rate and the value of bids lost because the floor is set too high. Optimisation also needs to account for its impact across the entire auction chain, from Prebid to the ad server. Machine learning becomes valuable when sufficient data enables it to make better decisions than predefined static rules.
From Machine Learning to Agentic AI
Machine learning and large language models have different but complementary roles in ad monetisation. Machine learning is well suited to quantitative optimisation and learning from data, while LLMs can help analyse reports, explain findings and support user decision-making.
Relevant AI, launched in early 2025, uses language models within Relevant Yield, allowing users to query reports, interpret data and work with Relevant Yield’s product knowledge. We are now expanding Relevant AI in a more proactive direction. The upcoming AI Brief is one example: it identifies changes, highlights observations worth investigating and helps users understand what deserves their attention.
At the same time, we are developing agentic capabilities that allow AI not only to analyse information but also to suggest or carry out predefined actions. As automation advances, it becomes increasingly important for publishers to retain control over what AI can access, which actions it can take automatically and which require approval. Any changes made should also be observable, traceable and reversible.
How Do You Separate Real Value from Hype?
Publishers should look at what an AI system’s decisions are based on: what data it can use, whether it can connect auction data with actual performance and revenue, and the level of granularity at which decisions are made. Equally important is the ability to verify their impact. In optimisation, for example, results should be compared against a control and examined in greater detail beyond the overall result.
Ultimately, the real value of AI in ad monetisation does not depend on any single model, but on the data, expertise and technology around it. As AI expands from analysis into optimisation and agentic capabilities, questions about what AI can do automatically and how much control remains with the publisher become increasingly important.