Distinctiveness at Scale: The Hardest Problem in AI Advertising

This is the third in a series of essays coaching marketing executives on how to navigate the promise and peril of AI-generated creative.

The AI era will produce a strange result in advertising: it will become dramatically easier to make ads, and dramatically harder to build brands.

More creative capacity should, in theory, give marketers more opportunities to express who they are through more versions, more experimentation, and more surface area for great ideas. But distinctiveness does not emerge automatically from abundance. In practice, abundance often weakens it.

“Synthetic Mediocrity” 

The reason sits inside the models themselves. Generative systems are trained on patterns from the past. They are exceptionally good at producing outputs that feel plausible, contemporary, and fit for purpose. Whether you ask for a premium product ad or a TikTok-style testimonial, the model will usually provide something recognizable. This reduces blank-page risk and makes competent creative easy to produce.

You don’t need to rack up Clios, but what brand strives for merely “competent creative?”

AI tools create a powerful pull toward the center. As more marketers use similar models trained on similar data, outputs begin to converge into a state of synthetic mediocrity. Hooks start to sound alike, visual language flattens, and narrative arcs compress into familiar category shapes. Because much of this output looks polished, it is dangerous.

A brand can slowly slide into category sameness while still shipping creative that seems "good enough" in isolation. Remember the first few times you saw AI-generated text and marveled at the prowess of the machine? And now years later, we’re inundated with slop.

From Instinct to Brand Constants

This shift does not kill creativity; it exposes whether a brand actually knows how to define itself. For years, many brands have operated with a loose but workable model of identity: the brand book exists, the design team has instincts, and senior marketers know when a concept feels "right." That system works when output is limited and experienced humans sit in the loop at every step.

It works much less well when output scales into the thousands. In that world, brand identity cannot remain trapped in mood boards and tacit knowledge. It has to be translated into “brand constants:” machine-operable instructions that a system can repeatedly act on.

What exactly makes the brand recognizable? Is it the rhythm of the edit, the role of product in the frame, or the specific level of humor? Identifying which choices are flexible and which are non-negotiable is no longer just a creative aspiration; it is an operational requirement and must be supported by a clear rubric.

The Risk of Brand Drift

AI does not just increase output; it fragments the consumer experience. In the old model, a brand concentrated energy on a few hero assets. Today, a customer may encounter dozens or hundreds of tailored executions. One ad may be playful and creator-led while another is rational and stripped down. Each may perform well on its own, but together, they can produce a fractured picture of the brand.

This is the reality of brand drift. Branding is becoming more cumulative and more delicate. Marketers have to think about the branding effect of each touchpoint in sequence. Differential and incremental branding are vital in a world where there is no single canonical brand expression, only a constantly shifting cloud of them. Current processes and systems do a poor job at measuring this other than looking at the end KPIs.

Survival in an Era of Vibe Ads

The market will soon be filled with an extraordinary volume of advertising. Much of it will be the creative equivalent of vibe coding: fast, intuitive, occasionally brilliant, but often shallow. When the barrier to making ads collapses, the barrier to making forgettable ads collapses with it.

Distinctiveness has always depended on contrast. A strong piece of advertising lands because it feels recognizably different from what surrounds it. In a feed full of AI-assisted sameness, that contrast becomes harder to achieve and harder to sustain.

The brands that win will be the ones that can teach AI what is uniquely theirs. This is where the concept of a brand model begins to matter: not generic assistants, but systems that accumulate knowledge about a brand over time. These systems learn signals and preferences well enough to guide creative decisions consistently across a fragmented output surface.

The strategic mistake in this next phase is to confuse output with identity. More assets do not automatically produce stronger branding. Left unguided, they produce drift. The hard work is not generating creative; it is systematizing what makes the brand unmistakable and carrying that distinctiveness through a world of infinite variation.

That may turn out to be the central branding challenge of the AI era. Is your brand built to stand apart in a world shaped by AI? Get in touch to see how creative data can transform your decisions into sustained brand growth. 

Joseph Galarneau is Vidmob’s Chief Product & Technology Officer, leading the company’s data science, product, and engineering strategy and operations. A long-time adtech and media executive, Joe formerly served as global head of martech product at Wayfair, CPO at CivicScience and Verve, and COO of Newsweek and The Daily Beast. He also was founder/CEO of Mezzobit, a marketing data platform acquired by OpenX.