If, in these last two years, the emergence of Generative Artificial Intelligence has served as a glimpse of the potential of this technology – and we must agree – it has had a reasonable impact in areas such as customer service, by 2025 we should witness the development of "agentic AIs," which promise to substantially transform the technology landscape. Alongside the ever-increasing expansion of AI models to a wider range of companies and niches, the fact is that today, no company can ignore the potential application of AI in innovation or operations.
Unlike traditional AIs, which require constant human supervision, agent-based AIs are designed to operate independently, performing 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, agent-based AI systems use large amounts of data from various sources to independently analyze challenges, develop strategies, and perform complex and sequential tasks. The potential application of this type of AI is enormous, starting with customer service, moving 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 fixing vulnerabilities in systems, for example.
In Brazil, the adoption of AI agents is still in the 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 agent-based AI systems into their operations.
Impact of Agentic AI
The potential impact of AI agents is enormous. Banks and financial institutions can reduce fraud incidence by up to 50% with technology, according to the Brazilian Federation of Banks (FEBRABAN).
The Health sector can also apply the new technology. The Brazilian Medical Association (AMB) highlights that AI agents have the potential to reduce medical errors by up to 30%, as the technology is capable of analyzing medical records, test results, and patients' health history to propose more accurate diagnoses. In the industry, intelligent automation will be driven by agentic AI, which enables the autonomous operation of machines and processes.
Expanding generative AI into the production environment
Even with the widespread adoption of generative AI, its impact has still been limited in the productive environment, 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 production environment by 2026 – reaching up to 80% of companies.
In Brazil, the adoption of generative AI tools by companies is increasing 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 improve data analysis, automate repetitive tasks, and forecast 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 to happen in the coming months, and one of the most anticipated features of this new version is the enhanced ability of the tool to maintain 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 have suggested that GPT-5 will have reasoning abilities similar to those of 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, learning models called LLMs – or Large Language Models – were widely adopted so that popular tools could emerge 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 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 operate. 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 people's and companies' daily lives. 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 is deeply integrated into all aspects of society.
With the proliferation of small, more specialized AI models, it is expected that personalization and efficiency will reach new heights, providing solutions increasingly aligned with the specific needs of each sector. Therefore, the year 2025 promises to be, without a doubt, a year of great revolutions for AI.
