Generative artificial intelligence is radically changing the way digital advertising is done. On a daily basis, I realize that this technology has transformed each stage of the creative process, from the first insight to the final validation of the campaigns.
In the ideation phase, text generation tools offer instant brainstormings, giving quick and creative suggestions for slogans, scripts or visual concepts. This broadens and greatly accelerates the creative process, allowing you to explore thousands of ideas in a few minutes, without relying exclusively on personal inspiration.
During the creation of the content, the change becomes even more evident. There are advanced tools that generate full ads, from well-crafted texts to custom images for different audience types. AI finally delivered something that the market had been looking for for a long time: hyper-personalization at scale. This makes it possible to deliver the right message, at the right time and for the right person with an efficiency that would be manually impossible.
These advances not only mean efficiency gains, but also a quantitative leap in campaigns. Ads that used to take weeks to launch now are ready in days or even hours. Big advertisers have already noticed this, noting that generative AI has greatly reduced the time needed for creative production, freeing up more time for the team to focus on strategic decisions.
In addition, the quality of the ads has increased because smart algorithms analyze previous behaviors and optimize every detail, from titles 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 is not limited to the creation of ads. In the distribution and broadcasting stage, platforms such as AI Sandbox from Meta already use AI to dynamically adjust the contents based on the reactions of the public in real time, generating several versions automatically adapted for each channel. But to take advantage of all this, it is essential to have a solid foundation of knowledge. Companies should carefully structure their internal information – from style guides, past campaign history and product catalogs to customer interactions on social networks, reviews and market research. All of this works as a fuel for AI, allowing it to create more accurate content aligned with the brand identity.
Today there are already platforms and technologies such as the Retrieval Augmented Generation (RAG), which can quickly access this database and generate coherent and personalized content. Leading companies such as Coca-Cola have already shown the potential of this approach by combining models like GPT-4 and Dall-E with their own collection, ensuring that AI captures and replicates the true spirit of the brand. Connected to a good database, generative AI also becomes a powerful insight machine. It analyzes gigantic volumes of information to identify trends and opportunities that would often go unnoticed. An example is how big brands manage to predict consumption trends by analyzing millions of online interactions, generating useful insights for much more efficient campaigns.
Then AI enters the scene producing highly personalized content. The results are impressive: texts and images generated instantly and adapted to different audience profiles, dramatically increasing the effectiveness of campaigns. A clear example is that of Michaels Stores, which has achieved almost total levels of customization in its communications, significantly improving its results.
Creativity also gains new horizons with AI allowing 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 arts, reaching very high levels of engagement.
It is worth noting that, even with all this automation, the human factor remains essential. The role of professionals becomes curated and refined, selecting and improving the ideas that AI generates, ensuring strategic and emotional alignment of campaigns. Another important gain is the prior validation of ideas. Today, AI models simulate the performance of campaigns before they air, helping to quickly identify what works best and greatly reduce the risk. Companies like Kantar already do this in minutes, predicting the real impact of ads even before they are released.
These simulations go beyond numbers, also providing qualitative insights that help to understand how different audiences can react to a campaign, functioning as true virtual focus groups.
The key to all this working well is the correct data. Proprietary data, social media, market reports, service conversations and previously produced content are essential for AI to deliver really personalized and effective results.
This transformation is here to stay. Today it is possible to do much more with less, launching more assertive, faster campaigns with a high potential for return. Of course, challenges exist, such as ensuring ethics and quality, but the path is already clear: digital advertising will be increasingly guided by artificial intelligence, and the marketer will have a fundamental strategic role in piloting and refining these results.
