Once considered the “old reliable” of programmatic advertising, contextual targeting is having a resurgence—and this time, it’s powered by AI. As marketers navigate a landscape increasingly shaped by privacy regulations and the demand for relevance, AI is redefining how contextual targeting works, making it smarter, more scalable, and more effective than ever.
But how does AI actually transform Contextual 2.0? And do the results live up to the hype? Let’s break it down.
How AI resolves historical challenges with contextual ads
Consumers have made it clear—they prefer ads that feel relevant. In fact, 63% of consumers favor online ads that align with the content they’re actively engaging with. Yet, traditional contextual targeting has long struggled to move beyond basic keyword matching, limiting its ability to capture nuanced intent. As the open web grows more dynamic—where page content constantly shifts and evolves—delivering contextually relevant ads at scale has become even more complex. The challenge isn’t just about finding the right keywords; it’s about understanding broader themes, sentiment, and real-time intent while maintaining precision.
This is exactly where AI changes the game. With large language models (LLMs) and Generative AI, the old limitations of contextual targeting fade away. AI can analyze vast amounts of unstructured data, recognize deeper semantic connections, and pinpoint not just what’s on a page—but tie it more closely to behaviors and predicted actions. This level of sophistication enables brands to deliver more meaningful, privacy-friendly advertising experiences that resonate in real time.
Here’s how:
· True context, not just words: Traditional contextual targeting can misinterpret meaning. If a webpage discusses “cars” in the context of a movie plot, legacy models might wrongly assume it’s about the automotive industry. AI, however, recognizes the distinction, ensuring ads align with the real thematic context, driving better engagement and reducing wasted impressions.
· Balancing precision with scale: AI delivers both accuracy and reach by combining keyword matching with intelligent expansion. It maintains high semantic relevance, but also identifies hidden opportunities—surfacing terms advertisers might not have considered but that still align with campaign objectives. This nuanced balance ensures brands reach the right audiences without compromising control.
· Smarter placements, smarter results: AI doesn’t just look at surface-level content—it predicts where ads will perform best based on consumer behavior patterns and purchase propensity. This means a smartwatch ad might appear on a car-related article—not because of a direct keyword match, but because AI has identified a strong correlation between tech-savvy consumers and automotive content engagement, maximizing campaign success.
This AI-driven evolution in contextual targeting is enhanced by the industry-wide adoption of IAB Tech Lab Content Taxonomies. These standardized taxonomies provide a structured framework for organizing and classifying AI-derived contextual insights, ensuring consistency across the industry.
You can find more technical information on AI-driven contextual targeting here.
Does contextual AI really perform better?
AI-powered contextual targeting isn’t just theoretical; it’s driving real, measurable impact for brands.
Take BlueAir, for example. Partnering with Tinuiti, they set out to engage consumers based on real-time interests, ensuring their message landed in the right context. By tapping into AI-driven Contextual Targeting and leveraging the latest LLMs, they not only expanded their relevant reach across the open web but also saw standout performance: 2.4x higher detail page view rate (DPVR), a 42% drop in CPMs, and a 34% increase in new-to-brand (NTB) customers.
Meanwhile, PepsiCo took a data-driven approach to engage value-conscious shoppers withAI-powered Contextual Targeting, they unlocked a 3x higher return on ad spend (ROAS), slashed cost per acquisition (CPA) by 62%, and expanded unique reach while cutting CPMs by 60%.
These results aren’t just incremental gains—they highlight how AI-driven contextual targeting is reshaping digital advertising, delivering both efficiency and impact in a privacy-first world.
Contextual 2.0: the next evolution
AI-driven contextual targeting is a fundamental shift toward a smarter approach that meets evolving consumer expectations. By leveraging AI to understand content relevance in real time, contextual targeting enables deeper, more meaningful connections. And as privacy regulations reshape digital advertising, this innovation becomes not just an advantage but a necessity. We believe AI-powered contextual intelligence is a strategic priority, allowing for refined targeting capabilities that drive better outcomes for advertisers while delivering more relevant and engaging experiences for consumers.
At Amazon Ads, we support a variety of contextual targeting options, including keyword, categories, products, related products, and IAB Tech Lab content categories.
Nick Radicevic
Senior Manager, Product Management – Tech
Amazon Ads