With the arrival of new models and technologies—accompanied by major financial and infrastructure investments—we’re witnessing inevitable adjustments in business environments. This stems from the constant need to optimize resources, reduce operating costs, and adapt to an increasingly competitive market. These processes often take time before the new models become organically standardized.

A clear example was digitalization, which surprised the market with the emergence of new leading organizations within this paradigm. These drove large-scale investments, devices, formats, and new ways of interaction that transformed communication, transactions, and business models. The change was so powerful that it became recognized as a globally impactful innovation.

We are now experiencing a similar phenomenon with Artificial Intelligence. Major corporations are leading this movement with colossal investments in capital and infrastructure—surpassing even those of 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 human-like intelligence—it’s important to understand that what we’re witnessing is a sophisticated algorithmic imitation. AI doesn’t reason or possess consciousness, even if it can simulate human responses with great precision. Still, its capabilities for simultaneous data processing, execution speed, and responsiveness make it close to what we might call an advanced “practical perception.”

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

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

However, these advances are still far from the initial hype of total automation and human replacement. In fact, many AI-generated results contain errors, which requires maintaining conservative expectations.

How should the internal structure of agencies adapt 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 merely as a trend—can be counterproductive. We already 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, supported by the following pillars:

• Human talent and responsibility

• Quality and truthfulness

• Legality

• Local or proprietary technological implementation

In agencies where marketing positions have already been replaced, the workload of remaining employees should be reviewed. Often, these individuals must absorb tasks that required a human component that is no longer present.

New hiring should focus on structured knowledge, proven experience, and solid psychological and emotional profiles. It’s not recommended to hire solely for AI tool proficiency, as many of these tools are easy to learn. Instead, it’s valuable to have professionals with advanced experience in AI solutions for specific areas such as IT, design, or creative writing.

Agencies, Freelancers, and AI Clauses

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

Freelancer collaboration is also changing. Blind trust is no longer enough. Many freelancers fail to disclose their use of AI in deliverables and present generated content as their own. This must be regulated in contracts.

Generative Design Without Visual Burnout

In design and creativity—key areas in marketing—implementation can initially rely on models from OpenAI, Meta, Anthropic, or Gemini. However, it is advisable to gradually migrate toward local AI models (local LLMs), trained with images derived from creative processes and a professional key visual for each campaign.

This approach enables multiple variations without team burnout, maintaining the agency’s originality and creative vision. In this way, clients receive original work, far from the generic look produced by common AI tools.

Quality Control: A New Frontier for QA

The Quality Assurance (QA) department faces new challenges with AI. It must review generated content—texts, images, or videos—by considering:

• The tools used

• The percentage of AI-generated content

• Parameters set to avoid copyright, branding, or reputation issues

Workflow efficiency must also be assessed 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 for organizations that, in turn, are fair to their human collaborators—the true soul and purpose of every company.