With the arrival of new models and technologies, accompanied by major economic and infrastructure investments, we are seeing inevitable adjustments in business environments. This stems from the constant need to optimize resources, reduce operating expenses, and adapt to an increasingly competitive market. These processes often require time before models can organically standardize. 

A clear example was digitalization, which disrupted the market with the emergence of new leading organizations in this paradigm. These drove significant investments, devices, formats, and new ways of interaction that transformed communication, transactions, and business models. The shift was so profound that it came to be embraced as a global-impact innovation. 

With Artificial Intelligence, we are experiencing a similar phenomenon. Large corporations are driving its momentum with colossal investments in liquid capital and infrastructure—greater even than during the digital era. This creates a momentum that makes its adoption inevitable, regardless of whether it is initially perceived as positive or negative. 

The practical use of AI in the marketing environment is already a reality. Beyond the hype and the inflated concept of AGI (Artificial General Intelligence)—which idealizes an intelligence comparable to the human mind—it is crucial to understand that what we are seeing is a sophisticated algorithmic imitation. AI does not reason or possess consciousness, even if it can simulate human-like responses with high accuracy. Still, its simultaneous data processing capabilities, speed of execution, and responsiveness make it close to what we might call an advanced form of “practical perception.” 

How does this affect the structure of marketing agencies and their stakeholders? 

Although it is still early to clearly measure the organizational impact of AI, there are already signs of how it is being implemented. According to the Digital Marketing Institute (2025), its use is concentrated in idea generation, social media and blog content, SEO optimization, email marketing, and campaign automation through AI Agents. 

However, these advances are still far from the initial hype of full automation and human replacement. In fact, many AI-generated results contain errors, which forces us to maintain conservative expectations. 

How should agencies restructure internally to benefit from AI? 

While using AI as a differentiator can attract investors and project a modern image, falling into “AI-first” strategies—prioritizing AI over human talent simply because it is trendy—can be counterproductive. We warned about this in our previous article: “Artificial Intelligence and Automation: The Great Ethical and Moral Challenge for CEOs in the Post-Apocalyptic Market.” 

Instead, we propose a functional strategy, based on caution and human capital, with the following pillars: 

• Human talent and responsibility 

• Quality and accuracy 

• Legality 

• Proprietary or local technological implementation 

In agencies where marketing roles have already been replaced, the workload of remaining collaborators must be reviewed. Often, these individuals end up absorbing tasks that required a human component that is no longer present. 

Hiring new staff should be based on structured knowledge, proven experience, and a strong psychological and emotional profile. Hiring solely for AI tool proficiency is not recommended, since many tools are easy to learn. Instead, it is valuable to seek profiles with advanced expertise in AI solutions for specific areas such as IT, design, or creative writing. 

Agencies, Freelancers, and AI Clauses 

Agencies must continue to demand quality and compliance, but also include specific clauses regarding the use of AI. The definition of new methodologies cannot be left solely to the agency, as these decisions have legal and financial implications for all stakeholders. 

Freelance work is also changing. A “leap of faith” is no longer enough. Many freelancers fail to disclose AI use in their deliveries, presenting generated content as their own. This must be regulated in contracts. 

Generative Design Without Visual Burnout 

In design and creativity—where much of marketing services are concentrated—implementation can initially rely on models from OpenAI, Meta, Anthropic, or Gemini. However, it is recommended to migrate toward local AI models (local LLMs), trained with images generated from creative processes and a professional key visual for each campaign. 

This enables multiple variations without exhausting the team, while preserving originality and the agency’s vision. This way, clients are guaranteed original work, free from the generic look of mainstream AI tools. 

Quality Control: A New Frontier for QA 

The quality assurance (QA) area faces new challenges with AI. It must review generated content—whether text, images, or videos—considering: 

• Tools used 

• Percentage of AI-generated content 

• Parameters defined to avoid copyright, branding, and reputation issues 

Workflows should also be evaluated to determine whether AI-driven campaigns truly optimize resources compared to their costs and risks. 

By following these recommendations, AI can deeply benefit marketing structures. But this progress will only be fair to those organizations that, in turn, are fair to their human collaborators: the soul and true purpose of every company.