From big data to cultural capital: Why brand equity might be the strongest signal in the AI age
Opinion
In the AI era, brand equity will become a machine-readable signal, reshaping how brands think about marketing investments and their reliance on deterministic and personal data, writes Ogury’s CEO.
A decade ago, personal data was one of the most prized assets in digital advertising. In tomorrow’s world of AI personal assistants and agents, it might be a product review, a news headline, or even a buzz-inducing Super Bowl ad.
As AI LLMs and agents play a growing role in how people discover and choose products, the signals influencing those AI systems are shifting away from individual behavioural data and toward shared cultural relevance.
In a scenario where AI bots handle the final step in a purchase journey—or at least the initial selection—the signals shaping that recommendation will increasingly originate from media coverage, reviews, creative campaigns, influencers’ endorsement, and public conversations across communities and creator platforms.
To put it differently, in the AI era, brand equity will become a machine-readable signal, reshaping how brands think about marketing investments and their reliance on deterministic and personal data.
The end of the ‘collect everything’ mindset
For years, digital marketing operated on a simple premise: the more data you could collect—especially deterministic data—the more effective your advertising would be. That was the promise, and it drove an era of identity graphs, cross-device tracking, and large-scale behavioural profiling.
But the model has reached its limits. Public pressure and privacy regulations have forced brands to be more selective about the data they collect and how they use it. In parallel, new approaches have shown that relevance does not require surveillance.
At the same time, chatbots and AI assistants are shifting the focus of influence. When a purchase journey unfolds entirely inside a chat interface, even extensive behavioural signals may not influence the outcome. In this environment, success depends on generating credible signals that AI can find and prioritise based on user intent and brand reputation.
AI rewrites the definition of signal
Large language models operate as cultural aggregators, ingesting news, reviews, social conversations, videos, and other forms of public information. They filter this information to match a user’s query, while also incorporating the user’s preferences—declared or inferred—over time.
As a result, if your brand isn’t sufficiently present in the culture and public conversation in your category of choice, it is more and more unlikely to appear in the response and the consideration set.
This helps explain why, despite the continued focus on measurable outcomes in day-to-day activation, tentpole events like the Super Bowl, the FIFA World Cup, the Oscars, or the Olympics remain critical. They generate signals that extend far beyond the initial campaign.
And while not every brand can afford a major sponsorship or TV spot at these events, creators and influencers can offer an interesting alternative path.
By tapping into their audiences and cultural relevance, brands can participate in these moments, generate category-specific buzz, and produce signals that extend into the broader ecosystem.
Creativity becomes the signal generator
To become part of the culture, brands need to be creative. While digital advertising has historically emphasised precise targeting, research consistently shows creativity accounts for roughly half of a campaign’s outcomes, while targeting contributes far less.
This imbalance becomes more pronounced in the AI era. Creative campaigns, earned media, and authentic public conversation function as inputs that shape recommendations.
Brands that invest in ideas people talk about generate stronger signals than those relying primarily on behavioural targeting. In this context, brand equity becomes machine-readable and a signal that can influence recommendation systems as much as traditional performance metrics.
When agents enter the picture
As disruptive as LLMs are to the marketing funnel, AI agents could accelerate disintermediation. Instead of asking for recommendations, consumers may delegate decisions—booking travel, buying products, comparing services—to automated assistants. In such a context, brands will have fewer direct touchpoints with consumers. The signals influencing decisions will increasingly come from the broader media environment.
Adapting to an agentic market means thinking of prospects not as isolated individuals to be targeted, but as personas with rich, varied media diets that open opportunities for brands to gain cultural presence and generate signals.
The systems powering these decisions draw from sources they consider credible and relevant. That includes not only traditional media, but also creator content and trusted voices within specific communities — from discussions on Reddit to live streams and creator ecosystems on Twitch.
Creator and influencer content combine audience trust and contextual relevance, strengthening the signals that inform AI recommendations.
For brands, this means building visibility across a broader mix of environments: editorial coverage, creator partnerships, and culturally relevant moments.
Owned channels also play a role. Blogs, FAQs, and product pages should maintain a consistent voice while providing clear, structured information.
As agentic commerce emerges, digital advertising specialists will also need to develop their own agentic capabilities to keep up. Campaign management agents could interface with audience intelligence systems to identify relevant personas, which could then be activated across media placements in more automated environments.
Back to marketing fundamentals
The pace of AI development may feel disruptive, but the response is familiar. Brands still need to understand their audiences, earn trust, and create ideas that resonate.
Where marketers once focused on collecting data to target individuals, the more effective strategy may be generating signals that shape how a brand appears in the broader information ecosystem.
The most valuable data may no longer be what brands extract from consumers. It is the cultural capital they create.
Nicolas Bidon is the CEO of Ogury