Rethinking Marketing Relevance – From AI to Personalized Communication

Relevance Realization: The Cognitive Science of Attention in the Age of AGI and Advertising

In Navigating AGI’s Intersection of Business, Ethics, and Agency, I framed artificial general intelligence (AGI) not just as a technological leap, but as an ethical and strategic reckoning point. Since then, I’ve spent nine hours immersed in Dr. John Vervaeke’s Introduction to Intelligence, a rigorous synthesis of neuroscience, philosophy, AI, and cognitive science. One concept stood out: relevance realization in marketing—the capacity to identify what matters in any given context, to make sense of information by ignoring what doesn’t. It’s not just the basis of human intelligence. It’s the cognitive foundation of attention, agency, and meaning.

And if we’re honest, it may also be the missing operating system of modern marketing.

Intelligence ≠ Data Processing

We often reduce intelligence—in people and machines—to speed, memory, or computational power but that view collapses under complexity. Intelligence is not merely the ability to store or retrieve information, but the ability to discern what information is relevant—a dynamic filtering mechanism that scales across environments and contexts. This is especially true in ill-defined problems (which most real-world decisions are), where the structure isn’t handed to us.

In marketing, we’ve built a decade of programmatic systems around “data” without necessarily enhancing our ability to make better decisions. We’ve become extraordinarily good at automation, yet brittle at discernment. If the promise of data-driven marketing was to deliver the right message to the right person at the right time, then the failure mode has been doing so without understanding why it matters—a kind of brute-force personalization without perspective.

We are at risk of optimizing relevance out of relevance itself.

Relevance Realization: The Mechanism Beneath Attention

The concept of relevance realization bridges multiple disciplines. It’s the process through which biological and artificial systems make sense of their environment, selectively suppressing the irrelevant in favor of what’s salient. It’s not hardcoded. It’s emergent, context-sensitive, recursive— and deeply constrained by attention and memory systems. You might say it’s intelligence in motion.

What does this mean for marketing? That the game is no longer about getting attention—it’s about earning relevance. And that requires moving beyond the current model of targeting based on static identities, demographics, or behaviors.

Instead, we must ask: What makes a message relevant at the moment it’s received—and how can marketers create relevance that goes beyond surface-level traits?

This is not simply a question of personalization. It’s a question of perception of framing, affordances, and context. And that’s where intelligence, shows up in its most powerful form.

Attention ≠ Impressions

We’ve long known attention is a scarce currency in marketing. However, attention is not a spotlight—it’s a relevance engine. When we pay attention, we’re not just focusing. We’re filtering an overwhelming world into what might matter, what could inform action, what frames our options.

This insight has radical implications for how we measure media.

The adtech industry has historically treated attention as an impression-level metric, but a true attention economy requires a shift from quantity to cognitive quality from surface-level views to relevance-weighted engagement. Metrics like viewability or time-in-view are necessary but insufficient proxies. What matters is not just whether an ad is seen, but whether it’s seen as relevant.

Marketing as Applied Intelligence

To bring these insights into the marketing domain is to view campaigns as cognitive interventions. They’re attempts to modify the mental models of consumers to shift relevance, not just recall. This reorients our work as marketers from information transmission to affordance creation: presenting choices in ways that resonate with the goals, needs, and context of the individual.

Here, marketing aligns with the principles of distributed cognition the idea that thinking isn’t confined to a brain or device but emerges from the interaction between people, tools, and environments. The ad itself is not the message; it’s a cognitive prompt that triggers or modifies meaning in the recipient’s mind. That’s not unlike how AGI might someday operate not as a static model of intelligence, but as an adaptive interface to meaning.

Correlations and Models: Mapping Intelligence to Marketing and Business Outcomes

Let’s ground this in a practical model. Vervaeke’s synergy of predictive processing and relevance realization mirrors marketing’s multifaceted imperatives: visibility (reaching the audience), anticipation (forecasting trends), salience (cutting through noise), and impact (driving meaningful outcomes). We can envision an Exposure-Attention-Relevance-Outcome (EARO) framework (Pronounced like “Hero”):

• Exposure (Contextual Reach): AGI leverages real-time data—web traffic, X trends, and contextual signals—to optimize the initial encounter with the campaign. It ensures the message reaches the right audience in the right context, maximizing visibility and setting the stage for deeper engagement.
• Attention (Predictive Processing): AGI anticipates consumer behavior using vast data streams—web searches, X posts, biometric signals. It reduces “surprise” by aligning content with expectations, ensuring the campaign captures focus effectively.
• Relevance (Relevance Realization): The system filters for salience, balancing trade-offs like specificity vs. universality. It “optimally grips” the audience, avoiding triviality or overreach, and ensures the message resonates at the moment of interaction.
• Outcome (Collective Intelligence): Success emerges from distributed cognition—AGI collaborates with human creatives and consumer feedback loops, crafting campaigns that resonate as shared narratives and drive measurable impact.

This model quantifies impact beyond traditional metrics like impressions. For instance, Exposure might measure a campaign’s contextual fit score—how well it aligns with the audience’s current environment—while Attention could track predictive accuracy in capturing focus. Relevance realization might score a campaign’s affinity index—how well it fosters belonging—while Collective Intelligence tracks co-creation velocity—the speed at which audiences adopt and adapt the message. These metrics shift focus from short-term wins to sustainable influence, a priority for advanced marketers navigating fragmented digital ecosystems.

AGI, Media, and the Future of Relevance in Marketing

If AGI systems are to possess general intelligence, they must master relevance realization the very faculty that underpins human flexibility and adaptability. Large Language Models like GPT-4 are inching closer to this by dynamically predicting word sequences that seem most contextually plausible. But they’re not truly realizing relevance. They’re approximating it via statistical patterns.

Meanwhile, our media ecosystem infused with AI but lacking a theory of attention behaves as if scale equals significance. It doesn’t. In fact, scale without relevance becomes noise.

A New Marketing Model: Relevance as Systemic Feedback

Let’s tie this together.

If intelligence is about dynamically resolving ill-defined problems (e.g., “what matters right now?”), then advertising should be reframed as a systemic feedback loop of relevance realization. Each exposure, each engagement, each conversion is not just a touchpoint it’s a test of salience in a shifting attention economy.

What if we stopped treating ads as fixed messages and started treating them as affordances for meaning-making?

What if we measured media not just by reach or lift, but by its ability to scaffold relevance to frame, inform, and adapt in real time?

What if, instead of treating AI as a tool for prediction, we built it into the creative process as a cognitive partner for modeling attention dynamics?

Toward an Ecology of Intelligence—and True Relevance in Marketing

Intelligence, and especially general intelligence is an ecological phenomenon, not just an individual or computational trait. It emerges through interaction, context, and recursive learning.

Marketing, too, is ecological. It thrives not on interruption, but on integration. Not on extraction, but on resonance. And as we navigate a world where AGI, synthetic media, and human cognition increasingly converge, we would do well to build systems that don’t just capture attention, but that earn it not just once, but over time, through the dynamic dance of relevance realization.

In the age of AGI, attention isn’t the outcome. It’s the arena. And intelligence in both humans and machines is defined by how well we play within it.

As we stand at this intersection, the challenge for advertising professionals is clear. We must guide AGI’s evolution, ensuring it amplifies our humanity rather than exploits it—while making relevance a strategic cornerstone of modern marketing.

Vervaeke’s parting wisdom lingers: “Intelligence is recursive relevance realization running from participatory to propositional knowing and back again.”

In marketing, that recursion from connection to insight and back—may be our greatest asset. Because in a data-driven advertising world, relevance is the true differentiator in marketing.

Let’s wield it wisely.