By Georgina Bankier, VP, Global Commercial and Platform Partnerships at Eyeota, a Dun & Bradstreet company

Artificial intelligence is changing everything about how digital advertising works, but perhaps its greatest impact is on the lifeblood of programmatic campaigns: data. For years, advertisers leaned on broad behavioral signals and probabilistic segments to deliver scale. Collect enough impressions, the thinking went, and performance would follow. That playbook no longer works.

AI is transforming how people discover information online, compressing feedback loops between interest and action, and raising expectations for real-time personalization. At the same time, the rise of privacy regulations and the decline of traditional identifiers have weakened the once-sturdy foundations of audience targeting. As a result, marketers can no longer rely on the assumption that volume will translate into results.

Success in the AI era demands something different: a smarter, quality-first approach to data.

AI as a Catalyst for Change

AI’s disruptive potential is most obvious in media consumption patterns. Generative AI tools and conversational assistants are already changing how people search for information. Instead of clicking through pages of search results, users are increasingly satisfied with a single, AI-generated answer. For publishers and advertisers, that means fewer signals being generated through traditional browsing behavior.

But the shift is not limited to search. AI is redefining expectations across the digital experience. Consumers anticipate personalization at every touchpoint, delivered seamlessly across devices and channels. Marketers, in turn, expect their campaigns to adapt in real time, with performance data feeding directly back into optimization.

In this new environment, programmatic campaigns still depend on good data, but the definition of “good” has evolved.

Why the Old Data Model for Programmatic Is Cracking

The probabilistic, scale-first model that underpinned early programmatic advertising was built on abundance. Massive volumes of browsing behavior and third-party cookies could be stitched together into audience segments that were assumed to be directionally correct. But the cracks in that model have widened into fault lines.

• Reduced signal supply: As AI-driven search tools and walled gardens capture more activity, open-web traffic (and the behavioral data that once fueled programmatic) is shrinking.
• Regulatory friction: Global privacy laws have introduced strict requirements for consent, governance, and data provenance, making traditional “collect it all” approaches untenable.
• Scale without precision: In today’s environment, targeting a broad segment without accuracy lowers ROI and wastes spend.

The old model can’t keep pace with the demands of AI-driven personalization, where campaigns are judged by relevance and performance, not reach alone.

The New Imperatives for Targeting Data in the AI Era

Programmatic targeting is entering a new chapter, one where the rules of data strategy look very different from before. One of the clearest lessons from the AI shift is that quality now outweighs scale. The old probabilistic model (amassing vast behavioral datasets and hoping volume made up for inaccuracies) has become too brittle to support programmatic performance. What advertisers need today are deterministic, validated inputs that can be trusted to identify the right audiences with confidence.

First-party data naturally forms the foundation. It reflects the strongest, most compliant connection between a brand and its customers. But on its own, first-party data rarely provides enough reach or context to fuel programmatic campaigns at scale. This is where enrichment from trusted third-party sources becomes essential. By layering high-quality, transparently sourced attributes onto existing customer files, marketers can close coverage gaps, discover new audience opportunities, and extend their reach into channels where their first-party data is less present.

Beyond the matter of quality over quantity, several other imperatives are also emerging:

• Expanded signal types. Contextual, identity, and intent data are becoming essential complements to behavioral inputs, offering more durable ways to understand audiences.

• Transparency and provenance. Marketers can no longer accept black-box data. They need visibility into where data comes from, how it is sourced, and how segments are constructed.
• Performance-informed targeting. A segment’s label tells only part of the story. Advertisers need to know how it has performed in similar campaigns and how it aligns with their objectives.
• Interoperability. With data housed across CDPs, DSPs, and countless other platforms, interoperability is essential to avoid silos and ensure seamless activation.
• Omnichannel readiness. Effective programmatic targeting now spans more than display. Data strategies must support activation across CTV, retail media networks, digital out-of-home, and emerging AI-powered environments.

The Marketplace as Strategy Engine

As AI accelerates the demand for higher-quality inputs, data marketplaces themselves are evolving. They are no longer static shelves of prebuilt segments. Instead, they are becoming consultative environments where marketers can compare, customize, and curate data solutions.

In this model, marketplaces provide transparency into sourcing, allow buyers to filter out noise, and offer flexibility to adapt segments to specific campaign needs. Customization and curation become the standard, not the exception. AI will only deepen this shift, enabling smarter matching of data to performance goals and simplifying the process of assembling omnichannel-ready audiences.

Building a Stronger Foundation

AI is revealing the cracks in legacy data strategies, but it is also creating the conditions for a stronger foundation. Advertisers who embrace quality, transparency, and performance-informed data will be best positioned to elevate their programmatic campaigns.

At Eyeota, a Dun & Bradstreet company, we are already helping brands and agencies make this shift. Our Audience Marketplace provides curated, high-quality audience data that is built for transparency, customizable for campaign objectives, and ready for omnichannel activation. We prioritize quality over raw scale, ensuring that advertisers can target with confidence in an AI-driven marketplace.

The rules of programmatic targeting are being rewritten. By grounding strategies in trusted, transparent, and performance-ready data, advertisers can turn AI disruption into an opportunity for smarter growth.