By Jaime Schultheis, Head of Global Data Partnerships at Bombora
Every major shift in digital advertising has followed the same pattern. Innovation comes first, guardrails come later.
Search, social, and programmatic advertising all scaled rapidly before the industry fully grappled with privacy, transparency, and user trust. The result was predictable: years of regulatory correction, platform restrictions, and growing skepticism from users.
AI is the next major shift, and it is already moving toward monetization. But this time, the industry has something it did not have before: a clear view of what happens when privacy is treated as an afterthought. As AI platforms begin to introduce advertising, the opportunity becomes monetizing a new channel and avoiding the structural mistakes that defined the last two decades of digital advertising.
AI ads are the next phase of search monetization
The trajectory is already familiar. Search evolved from organic results into a multi-trillion-dollar advertising ecosystem built on paid placement and auction dynamics. AI platforms are now moving along a similar path, and we are seeing early signals everywhere with new formats being explored to balance access and revenue.
This evolution is inevitable. Free access at scale requires a sustainable business model, and advertising will be part of that equation. The real question is not whether ads will exist in AI experiences; they already are. It is whether they will be integrated in a way that enhances the experience or degrades it.
That distinction matters more in AI than in any previous channel. Unlike search, where users expect a list of links, AI delivers synthesized and personalized answers. When advertising is introduced into that environment, it sits much closer to the perceived truth. If it feels intrusive, biased, or unclear, it risks undermining confidence in the entire system.
AI introduces a fundamentally different privacy challenge
What makes AI advertising more complex is the nature of user interactions with AI platforms and the depth of signals those interactions produce. In traditional search, signals are relatively simple: a single query reflects a moment of intent that triggers relevant sponsored ad placements. In AI, users engage across multiple interactions and sessions providing context, refining their needs, and often sharing detailed personal information about their goals, constraints, and challenges.
In B2B scenarios, these can include highly sensitive inputs about business priorities, vendor evaluations, internal decision-making processes, and even private or proprietary information. The depth and continuity of these interactions create a new category of data exposure, ultimately changing the stakes for privacy.
The industry should not rely on the same playbook it used for earlier channels, where data collection expanded first, and accountability followed later. In AI environments, the stakes are higher. The depth and intimacy of these interactions increase both the volume of sensitive data shared and the potential impact of misuse. Missteps will be more visible and less tolerated, making it critical for platforms and advertisers to define clear boundaries around how data is collected, how it is used, and how it informs advertising.
Privacy-first, consent-driven approaches offer a viable path forward. In practice, that would mean relying on aggregated, contextual signals rather than individual-level tracking. For example, instead of using personal data from a specific user interaction, advertisers can leverage broader patterns of research behavior across companies and topics to inform relevance. The underlying principle is simple: relevance should come from real, contextual signals without overreaching into personal or sensitive territory.
AI monetization is still in its early stages. The norms are not fully established, and user expectations are still forming. That creates a rare opportunity to establish stronger privacy standards before poor practices become embedded.
Getting this right is a growth strategy, not a constraint
There is a tendency to frame privacy as a limitation on innovation. In the new AI-driven environment, the opposite is true. Trust is the foundation of AI platforms, their outputs, and the advertising experiences built within them. If users believe that interactions are being exploited or that their data is being used in opaque ways, engagement will decline, adoption will slow, and the long-term value of the platform will be compromised.
If AI platforms establish clear, responsible standards from the start, they can accelerate adoption and create more sustainable monetization models. This is not just about compliance. It is about designing an ecosystem where users, platforms, and advertisers all benefit. For users, that means relevant, useful experiences that respect boundaries. For advertisers, it means higher-quality signals and more meaningful engagement. For platforms, it means long-term viability built on trust rather than short-term optimization.
A chance to break the cycle
Digital advertising has spent years correcting the consequences of moving too fast without sufficient guardrails. AI finally offers advertisers a chance to break that cycle.
The advertising industry understands the risks. It has seen how quickly trust can erode and how difficult it is to rebuild. It also has better tools, better data practices, and clearer frameworks for responsible innovation. The only challenge now is execution.
If platforms and advertisers take a proactive approach to privacy, transparency, and data governance, AI advertising can evolve differently from what came before. If they do not, the industry will find itself repeating a familiar pattern, only at a faster pace and with higher stakes.
What happens next will determine whether AI becomes the most trusted advertising environment yet, or the next one defined by user backlash, regulatory crackdowns, and another cycle of rebuilding trust.
