Artificial intelligence continues to rapidly transform digital marketing, becoming a strategic factor for companies seeking efficiency, personalization, and scalability in their campaigns. Given the latest innovations in the field of AI, a deeper analysis is warranted regarding the potential of two approaches that have gained prominence lately: predictive AI and generative AI.
While predictive AI focuses on analyzing patterns to forecast future behaviors and generate insights, generative AI elevates creative automation by producing highly personalized content tailored to the user’s context. Today, it is one of the primary focuses of attention and investment for marketing teams across companies of various sizes and sectors.
According to McKinsey data, generative AI has the potential to generate between $2.6 trillion and $4.4 trillion in the global economy annually, with 75% of this value coming from four main areas, including marketing and sales. For context, this figure exceeds the GDP of the world’s largest economies in 2024, except for the United States ($29.27 trillion), China ($18.27 trillion), and Germany ($4.71 trillion).
This statistic alone helps demonstrate the impact of adopting new generative AI-based technologies and how they will be pivotal for advertisers seeking differentiation and ROI maximization. But the question remains: Are there other avenues to explore? The answer is undoubtedly yes.
Composite AI: Why combining different AI models can be a differentiator
Although generative AI is currently in the spotlight, the role played by predictive AI models in digital advertising thus far is undeniable. Their function is to transform vast amounts of data into actionable insights, enabling precise segmentation, campaign optimization, and predictions about consumer behavior. Data from RTB House 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 enhanced when combined with other models. The logic behind this is simple: combining different AI models can help solve various business challenges and contribute to refining cutting-edge solutions.
At RTB House, for example, we are advancing the combination of Deep Learning algorithms (predictive AI) with GPT and LLM-based generative models to improve the identification of audiences with high purchase intent. This approach allows algorithms to analyze not only user behavior but also the semantic context of visited pages, refining ad segmentation and placement. In other words, it adds an extra layer of precision, resulting in improved overall campaign performance.
With growing concerns about 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 data collection becomes more restricted. As these tools evolve, the adoption of hybrid models is expected to become a market standard, with applications that contribute to campaign optimization 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 brands that embrace this revolution will gain 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 with an approach more aligned with the future of digital advertising.
