Daniela Andrade Argues Traditional Agencies Are ‘AI-Branded,’ Not AI-First

In a recent LinkedIn post, Daniela Andrade challenges the prevailing narrative around Artificial Intelligence adoption within traditional marketing agencies, arguing that many are merely “AI-branded” rather than truly AI-first. Andrade, reflecting on her experiences and observations, particularly at events like Cannes, suggests a significant disconnect between the public perception and the actual integration of AI into agency workflows.

The core of Andrade’s critique lies in the superficial adoption of AI tools. She recounts conversations where inquiries about actual AI use cases were met with vague responses or admissions of limited training. As Andrade points out:

“When I asked people how they were actually using AI in their workflows, the answers usually fell into a few buckets: ‘Yeah, we use AI.’ Mostly ChatGPT or Claude. Or, ‘I don’t really use AI. My company hasn’t trained us on it.'”

This lack of deep integration, Andrade contends, is a critical issue. She observes that while agency executives and company pages promote an AI-centric image, the reality on the ground is often different. Many employees are offered optional AI workshops, which Andrade dismisses as insufficient for genuine transformation.

The Gap Between Perception and Reality

Daniela Andrade highlights a concerning trend where AI has become a buzzword, often used to signify enterprise value without a clear mechanism for its creation, measurement, or delivery. This gap, she argues, is particularly perilous for the traditional agency model.

According to Andrade, the traditional agency business, reliant on human labor, hours, and extensive strategy development, faces inherent margin limitations. In contrast, AI-native companies are positioned to compete on speed, scale, and output with leaner structures and more efficient margins. Andrade states:

“The traditional agency model depends on people, hours, decks, strategy, and taste, which means its margins are capped by the amount of human labor required to deliver the work.”

This fundamental difference in operational structure, Andrade suggests, creates a competitive disadvantage for established agencies that have not genuinely embraced AI.

The Danger of Optional Upskilling

Andrade is critical of approaches that treat AI adoption as an optional add-on rather than a core operational shift. She emphasizes that true AI integration requires more than just awareness; it necessitates a rebuilding of workflows and mandatory upskilling. In Andrade’s view:

“Optional upskilling is not an operating model. If your team is not required to learn the tools, if no one is rebuilding the workflow, if ‘AI’ mostly lives in thought leadership posts and Cannes panels, then the company is not AI-first. It is AI-branded.”

This distinction between being “AI-first” and merely “AI-branded” is central to Andrade’s argument. She believes that companies that fail to embed AI into their core operations risk being outmaneuvered by more agile, AI-native competitors.

The Future of Agency Models

Daniela Andrade concludes her post with a stark warning about the future of the agency landscape. She predicts that the traditional model will be disrupted from the ground up by teams that can effectively leverage AI.

As Andrade notes, the teams that can translate messy data into tangible outcomes, conduct rigorous evaluations, develop bespoke client knowledge systems, and operate at speeds far exceeding traditional large teams will possess a significant competitive edge. This suggests a future where agencies that fail to adapt their operational models and fully integrate AI will struggle to remain relevant in an increasingly AI-driven market.