If, in these past two years, the emergence of Generative Artificial Intelligence has served as a glimpse of the potential of this technology – and, we must agree – has had a reasonable impact on areas such as customer service, in 2025 we should witness the development of “agentic AI”, which promise to substantially transform the technology landscape. Alongside the ever-expanding deployment of AI models to an even wider range of companies and niches, the fact is that today, no company can ignore the potential application of AI in innovation or operations.
Different from traditional AIs, which require constant human supervision, agentic AIs are designed to operate independently, carrying out complex tasks without direct human intervention. This advancement is made possible by deep learning algorithms that enable systems to understand and process large volumes of data in real-time, quickly adapting to new information and contexts.
Furthermore, agentic AI systems leverage large amounts of data from various sources to independently analyze challenges, develop strategies, and perform complex tasks sequentially. The potential application of this type of AI is enormous, starting with customer service, through the processing of any type of information or company processes, and also in cybersecurity, where it is possible to automate tasks that currently require human intervention, such as analyzing and correcting vulnerabilities in systems, for instance.
In Brazil, the adoption of agentic AI is still in its early stages. Some sectors are already testing the new model, and according to a survey conducted by the Institute of Applied Economic Research (IPEA), by 2025, about 40% of large Brazilian companies plan to integrate agentic AI systems into their operations.
Impact of AI in the mainstream
The potential impact of AI in the mainstream is enormous. Banks and financial institutions could reduce fraud rates by up to 50% with the technology, according to the Brazilian Federation of Banks (FEBRABAN).
The Healthcare sector could also leverage the new technology. The Brazilian Medical Association (AMB) points out that mainstream AI has the potential to decrease medical errors by up to 30%, as the technology can analyze medical records, test results, and patients’ health history to provide more accurate diagnoses. In the industry, smart automation will be driven by mainstream AI, enabling the autonomous operation of machines and processes.
Expansion of generative AI into the productive environment
Despite the widespread use of generative AI, its impact in the productive environment has still been low, with more intense use in certain niches, such as image and video creation. According to Gartner, the adoption of this AI model is expected to increase in the productive environment by 2026 – being adopted by up to 80% of companies.
In Brazil, the adoption of generative AI tools by companies is growing, as organizations recognize the value of these technologies in process optimization and innovation. Companies from various sectors, including advertising, media, and design, have been using generative AI to create personalized content and more effective campaigns.
Furthermore, large corporations are beginning to integrate generative AI into their daily operations to enhance data analysis, automate repetitive tasks, and predict market trends. The adoption of these tools can transform the way Brazilian companies operate, increasing efficiency and competitiveness in the global market.
AI will become increasingly humanized
The launch of ChatGPT-5 is expected in the coming months, and one of the most anticipated features of this new version is the enhanced ability of the tool to engage in natural conversations. This means that the chatbot will be able to follow the flow of a conversation, understand the context and hidden meaning, and even respond ’emotionally’.
Furthermore, experts suggest that GPT-5 will have reasoning abilities similar to humans, being able to understand the context of a conversation in a more comprehensive way.
2025: the year of small AI models
When AI emerged, Large Language Models (LLMs) were massively adopted for popular tools in the market. These models are trained on large amounts of data – however, this information is more superficial.
Small models are less expensive to build and operate and are more easily adapted to specialized applications. Instead of trying to do everything, small models are customized to perform a more limited set of everyday tasks for a specific business need.
LLMs have billions of parameters and require massive amounts of data and computational power to train and run. Small models, on the other hand, can be effectively trained with less data and require much less computational power (and therefore energy) to run.
In summary, these changes promise to transform various sectors and bring significant innovations to the daily lives of people and companies. The advancement of AI, both in terms of accessibility and sophistication, will further democratize access to advanced technologies, paving the way for a future in which technology will be deeply integrated into all aspects of society.
With the proliferation of small and more specialized AI models, personalization and efficiency are expected to reach new heights, providing solutions increasingly aligned with the specific needs of each sector. Therefore, the year 2025 promises to be undoubtedly a year of great revolutions for AI.
