What are AI Intention Models and How are They Shaping Contextual Advertising?

Contextual advertising is evolving with the rise of AI intention models. With industry focus shifting from attention to intention, brands are turning to predictive targeting for smarter and privacy-centric engagement.

The advent of AI has brought about significant changes in various industries, and programmatic advertising is no exception. However, its ascendancy has begun to drive a major revolution – a shift from the attention economy to the intention economy. And it is being driven by the evolution of contextual advertising.

The attention economy, which worked around the user’s attention, was capitalized by major advertisers. Making efficient use of this economy, entities such as Meta, even launched their own advertising platform to capture this digital phenomenon.

However, with AI seeping into the advertising industry, the intention economy is rapidly taking shape. Intention economy works on AI intention models that display ads based on the user’s intention.

Consider it to be an advanced version of contextual advertising.

The solution is being echoed by the advertising industry as the stakeholders mark an ongoing yet gradual shift to the cookieless era. To be more specific, the non-identity cookie era.

Shifting in Advertising: Contextual to Intent

To give you a brief overview, traditional contextual advertising has centered on displaying ads based on the page’s context. Keywords, categories, and page-level-based techniques have been used to display contextual ads. These ads, being independent of third-party data, have been at the forefront of cookieless advertising.

As contextual strategies continue to evolve in a privacy-first world, users’ browsing journey is transforming with agentic browsers. In this shift, intent is no longer optional; it’s becoming the cornerstone of meaningful engagement. This applies not only to advertising but also to how we approach content and SEO.

In SEO, for instance, keywords can be categorized in various ways, one of which is by intent. These include informational, commercial, navigational, and transactional. This is something you can even see in Ahrefs while doing keyword research. This categorization is the basis of intent-driven contextual advertising.

As you can see in the image, “I” and “C” written beside gaming ad networks state that the keyword is both informational and commercial.

Many advertisers have combined traditional contextual targeting with other targeting techniques that utilize third-party data, such as search history or user behavior, to understand how users behave during search journeys and display ads.

However, with rising scrutiny over third-party data usage and the subsequent loss of user identity signal, it becomes difficult for advertisers to gauge the nuanced shifts during a consumer’s search journey.

This is where predictive AI intention models come into play.

Predictive AI Intention Models

In theory, AI intention models gauge the intent behind a user’s search for content related to a product or a service. AI can help in bringing out the intent of the user that’s hidden behind their search. Building on traditional contextual targeting, AI can add intent to the mix, making targeting more precise and ads more personalized, without relying on cookies. 

Intention models are trained to understand whether the content is designed to inform, guide, or drive action, and how effectively it influences a user’s decision-making journey. AI models trained on intent-labelled datasets can utilize advanced natural language processing to identify content ranging from purely informational to highly transactional, assigning scores that indicate how close a user is to taking action.

For example, a product review, checkout guidance, or pricing comparison would have a higher intention score than a general interest piece about the same subject.

Example: Ads to Run Based on Intent

Let’s understand this further with the header bidding example.

For instance, let’s say a user searches for “What is header bidding?” and lands on an article explaining the concept, its benefits, and how it compares to traditional waterfall setups. This piece is informative and serves as an entry point, but the user’s intent is still in the awareness stage. The likelihood of immediate action is relatively low, so the intent score here would be moderate.

Now compare that to a user searching for “Best header bidding solutions for publishers” or “How to integrate header bidding with Prebid.js”. These indicate a higher level of research and readiness to take action. If they click on a product comparison or implementation guide, their content engagement is more focused and conversion-driven. This kind of content has a higher intent score because it suggests the user is closer to making a decision.

Based on the intent, advertisers can show ads that align more closely with the intent of the user searching for a product. For example, informational intent queries like “What is header bidding?” can be shown awareness ads from HB/AdTech platforms or sponsored content, such as blogs that explain the basics.

For navigational queries like “Prebid.js integration guide” or “Header bidding vs. Google Open Bidding”, advertisers or brands can offer downloadable guides, free demos, consultation offers, or case studies showcasing successful implementations.

At last, searches like “Best header bidding solutions for publishers” or “Prebid managed service pricing” come under transactional queries. We refer to it as a bottom-of-the-funnel lead because at this stage, the user can make a purchase at any time. To convert such a user, brands can display “request a quote” or free trial ads, sign-up page promotions, or limited-time offers.

Contextual 2.0: AI Targeting

As we mentioned earlier, AI is the primary driving force behind the evolution of contextual advertising. With AI, marketers can scan volumes of unstructured data, uncover deeper semantic relationships, and identify the relation between the page content and the user’s projected actions. This level of sophistication enables them to provide more meaningful, privacy-friendly advertising experiences that are relevant in real-time.

There are three ways AI can improve traditional contextual advertising.

Context Interpretation

Legacy contextual advertising models can misinterpret the query’s meaning. This can lead to missed conversion or user engagement opportunities. For example, a user searching “bags” can be misinterpreted by traditional models. This is because there are many types of bags, including school bags, laptop bags, tote bags, duffel bags, and more.

Which one the user is interested in is something traditional models can not determine. Here, Gen AI site search identifies the user’s search intent based on the page content. This, in turn, helps display contextually relevant ads, ensuring higher accuracy and engagement.

Balance and Scalability

Unlike the traditional model, AI matches both keywords and intent to display relevant ads. That means your ads can appear not only on obviously relevant pages but also in surprising, high-performing places you would otherwise miss.

For example, if a user is searching for “client-side bidding SDK”, the AI will also start showing ads for “header bidding” and “prebid.js” due to high contextual relevance.

Smarter Placements

Contextual advertising 1.0 would only show watch ads on pages that are related to watches. However, the game changes with AI intention models. As we had previously mentioned, intention models look beyond keyword matching; they examine content themes, tone, user behavior patterns, and engagement signals to identify a placement.

For example, if AI is showing a smartwatch ad in a car-related article, it’s because it has identified strong correlations between automotive content engagement and tech-savvy users.

The AI Intention Model Adoption Race

Companies have begun adopting AI intent models to provide marketers with enhanced targeting capabilities. One of them is SeedTag, a leading contextual advertising firm. In April 2025, the platform announced the launch of its AI intention model to identify actionable real-time user intent. The model can distinguish between casual browsing and transactional readiness in real-time without the use of personal data.

Nissan Spain had partnered with SeedTag to increase the visibility of Nissan Qashqai among potential buyers in the C-SUV segment. By deploying intention-based segmentation in the campaign, it resulted in:

• Reduced cost per quality lead by 68%
• Cost per lead reduced by 35%
• 3x increase in qualified visits

Taking another example, BlueAir partnered with Tinuiti to engage its consumers based on real-time interests. Tinuiti’s AI-based contextual advertising led to a 2.4x increase in detail page view rate, a 42% drop in CPMs, and a 34% increase in new-to-brand customers.

Similarly, PepsiCo also employed AI-based contextual advertising to engage value-driven consumers, which led to a three times return on ad spend, a 62% reduction in CPA, and a 60% decrease in CPM – all while increasing unique reach.

Cognitiv AI’s leading product, ContextGPT, combines AI-based contextual targeting with prompt engineering, enabling advertisers to reach relevant audiences at scale. Marketers can create custom prompts that are unique to their brands and adjust the relevancy to improve outcomes.

Their intention-targeting product has been utilized in various industries, including entertainment, food delivery, gaming, and beverages.

Google has also joined the league by announcing AI-based contextual ads on YouTube. Mainly powered by Google Gemini, the ad placements won’t be just topically aligned, but also more in line with the video’s context, relevance, and even the emotional engagement levels of viewers while they watch the video.

Meanwhile, Starbucks launched its own AI platform, Deep Brew, between 2023 and 2024, to personalise product recommendations based on AI-audience targeting. 

What Does the Future Hold?

The rise of the AI intention model marks a significant shift in how programmatic advertising works. As we shift from the attention economy to the intention economy, the future of programmatic advertising will be defined by privacy, precision, and purpose. 

With contextual advertising 2.0, we are entering an era where ad relevance is intelligently predicted. With the gradual decline of third-party cookies, AI intent models provide an intelligent, scalable, and privacy-centric alternative to programmatic targeting.

The advertising industry is no longer chasing attention; it’s focusing on intent. With AI leading the change, marketers can now have the best of both worlds: precision at scale and personalization without intrusion.