Generative Artificial Intelligence is radically changing the way digital advertising is done. In daily practice, I notice that this technology has transformed every step of the creative process, from the initial insight to the final validation of campaigns.
In the ideation phase, text generation tools offer instant brainstorming, providing quick and creative suggestions for slogans, scripts, or visual concepts. This greatly expands and accelerates the creative process, allowing thousands of ideas to be explored in just minutes, without relying solely on personal inspiration.
During content creation, the change becomes even more evident. There are advanced tools that generate complete ads, from well-crafted texts to customized images for different audience types. AI has finally delivered something the market has long sought: hyper-personalization at scale. This allows delivering the right message, at the right time, to the right person with an efficiency that would be impossible manually.
These advancements don’t just mean efficiency gains but also a quantitative leap in campaigns. Ads that once took weeks to launch are now ready in days or even hours. Major advertisers have already noticed this, highlighting that generative AI has significantly reduced the time needed for creative production, freeing up more time for teams to focus on strategic decisions.
Moreover, the quality of ads has improved because intelligent algorithms analyze past behaviors and optimize every detail, from headlines to images and calls to action, increasing overall engagement. In practice, many high-performance companies are already adopting these technologies.
Another interesting point is that this revolution isn’t limited to ad creation. In the distribution and broadcasting stage, platforms like Meta’s AI Sandbox already use AI to dynamically adjust content based on real-time audience reactions, generating multiple versions automatically adapted for each channel. But to take full advantage of this, a solid knowledge base is essential. Companies must carefully structure their internal information—from style guides, past campaign histories, and product catalogs to customer interactions on social media, reviews, and market research. All of this serves as fuel for AI, enabling it to create more precise and brand-aligned content.
Today, there are platforms and technologies like Retrieval Augmented Generation (RAG) that can quickly access this database and generate coherent, personalized content. Leading companies like Coca-Cola have already demonstrated the potential of this approach by combining models like GPT-4 and DALL-E with their own archives, ensuring AI captures and reproduces the true spirit of the brand. Connected to a good database, generative AI also becomes a powerful insights machine. It analyzes vast volumes of information to identify trends and opportunities that might otherwise go unnoticed. An example is how major brands can predict consumption trends by analyzing millions of online interactions, generating useful insights for much more efficient campaigns.
Next, AI takes center stage by producing highly personalized content. The results are impressive: texts and images generated instantly and adapted to different audience profiles, drastically increasing campaign effectiveness. A clear example is Michaels Stores, which achieved near-total personalization in its communications, significantly improving its results.
Creativity also gains new horizons with AI, enabling even co-creations between brands and consumers. Coca-Cola’s ‘Create Real Magic’ campaign is a great example, with consumers using AI to generate unique artworks, achieving extremely high engagement levels.
It’s worth emphasizing that, even with all this automation, the human factor remains essential. The role of professionals shifts to curation and refinement, selecting and improving the ideas AI generates, ensuring strategic and emotional alignment of campaigns. Another important gain is the pre-validation of ideas. Today, AI models simulate campaign performance before they go live, helping quickly identify what works best and significantly reducing risk. Companies like Kantar already do this in minutes, predicting the real impact of ads before they’re even launched.
These simulations go beyond numbers, also providing qualitative insights that help understand how different audiences might react to a campaign, functioning as virtual focus groups.
The key to making all this work well is the right data. Proprietary data, social media, market reports, customer service conversations, and previously produced content are fundamental for AI to deliver truly personalized and effective results.
This transformation is here to stay. Today, it’s possible to do much more with less, launching more assertive, faster campaigns with high return potential. Of course, challenges exist, such as ensuring ethics and quality, but the path is clear: digital advertising will increasingly be guided by Artificial Intelligence, and marketing professionals will play a fundamental strategic role in piloting and refining these results.
