Artificial intelligence continues to rapidly transform digital marketing, becoming a strategic factor for companies seeking efficiency, personalization, and scalability in their campaigns. In light of the latest innovations in the field of AI, a more in-depth analysis is warranted regarding the potential of two approaches that have recently gained greater prominence: predictive AI and generative AI.
While predictive AI focuses on pattern analysis to forecast future behaviors and generate insights, generative AI elevates creative automation, producing highly personalized content tailored to the user's context. Today, she is one of the main focuses of attention and investment for marketing teams in companies of various sizes and sectors.
SecondMcKinsey dataGenerative AI has the potential to move between $2.6 trillion and $4.4 trillion in the global economy annually, with 75% of this value generated in four main areas, including marketing and sales. For reference, the value is higher than the GDP of the world's major economies in 2024, except for the United States (US$ 29.27 trillion), China (US$ 18.27 trillion), and Germany (US$ 4.71 trillion).
This in itself helps demonstrate the impact of adopting new technologies based on generative AI and how they will be predominant for advertisers seeking differentiation and ROI maximization. But the question remains: are there other paths that can be explored? And the answer is, without a doubt, yes.
Composite AI: Why combining different AI models can make a difference
Although generative AI is currently in the spotlight, the importance of predictive AI models for digital advertising so far is undeniable. Your role is to transform large volumes of data into actionable insights, enabling precise segmentation, campaign optimization, and predictions about consumer behavior. RTB House data indicates that solutions based on Deep Learning, one of the most advanced fields of predictive AI, are up to 50% more efficient in retargeting campaigns and 41% more effective in product recommendations compared to less advanced technologies.
However, Deep Learning algorithms can be improved when combined with other models. The logic behind this is simple: combining different AI models can help solve various business challenges and contribute to the enhancement of cutting-edge solutions.
At RTB House, for example, we are advancing in combining Deep Learning algorithms (predictive AI) with generative models based on GPT and LLM languages to improve the identification of audiences with high purchase intent. This approach allows algorithms to analyze, in addition to user behavior, the semantic context of the visited pages, refining the targeting and placement of displayed ads. In other words, this adds an extra layer of precision, resulting in improvements in the overall performance of the campaigns.
With the growing concern for privacy and regulations on the use of personal data, solutions based on generative and predictive AI represent a strategic alternative to maintain personalization in environments where direct user information collection becomes more restricted. As these tools evolve, it is expected that the adoption of hybrid models will become a market standard, with applications that contribute to the optimization of campaigns and the results generated for advertisers.
By integrating predictive and generative AI models, companies demonstrate how this approach can transform digital marketing, delivering more precise and efficient campaigns. This is the new frontier of digital advertising – and the brands that embrace this revolution will have a significant competitive advantage in the coming years.
In this context, the question for advertisers is not which AI model to adopt in their marketing strategies, but how they can combine them to achieve even more efficient results and with an approach more aligned with the future of digital advertising.
