*Per Natalia Marroni Borges
In recent years, artificial intelligence (AI) has established itself as one of the most impactful technologies, driving innovation and transformation in several industries around the world. However, adopting AI effectively within organizations is a challenge that goes beyond technological implementation. The history of AI, which dates back to the 1950s with Alan Turing's first experiments, reveals the complexity and scope of this field, which also involves social, technical, legal, and organizational issues.
The current challenge for companies, especially in Brazil, lies in understanding that AI is not just a technology or an isolated statistical model, but rather a comprehensive and integrated process. A clear example of this is the use of generative AI, which has stood out for its accessibility to the general public. Although many users have no interest in or knowledge of the complex models underlying this technology, they use it for its practical value and its ability to solve everyday problems.
However, the success of implementing AI does not depend solely on the technology itself, but also on the way companies organize their structures, hire and allocate professionals specialized in this area. There is currently a movement in the job market where companies are eagerly seeking data science specialists or data engineers. machine learning, believing that these professionals will be able to solve all the challenges related to AI. This approach, however, can be limited and even counterproductive. While these professionals are fundamental to structuring the models that are the basis of AI, it is possible that the broader vision of AI is compromised.
Many organizations seem to be focused on hiring data science experts or machine learning, believing that these professionals will be able to solve all AI-related challenges. However, AI is a vast field that involves everything from developing models to integrating these models into complex business processes, as well as scaling all of their potential impacts. A professional specialized in data analysis, for example, may be extremely competent in generating valuable insights from large volumes of data and have in-depth knowledge of a set of complex statistical models, but they will not necessarily have the expertise to effectively integrate these solutions into the company's existing systems.
Furthermore, there is a worrying tendency to demand a broad range of skills from a single professional, which may be unrealistic. Requiring a generative AI engineer, for example, to also have advanced skills in data preprocessing, feature engineering, and statistical analysis can result in professionals who, despite being highly skilled, are unable to focus on what really matters for the success of the AI project.
This misalignment between market demand and the skills professionals actually need to design the use of AI could represent a significant obstacle to the growth of the topic in Brazil. It is not enough to develop advanced models; it is necessary to ensure that these models are applicable and that they generate real value for the business. This requires a holistic view of AI, where different skills and specializations complement each other to achieve tangible results.
Therefore, companies need to revisit their AI recruitment strategies, aligning their expectations and requirements with the real needs of the business. It is essential to understand that AI is not a stand-alone solution, but a set of processes that, when well orchestrated, can significantly transform the way organizations operate and compete.
Furthermore, as AI becomes more central to business operations, ethical issues emerge that cannot be ignored. Transparency in algorithms, privacy of user data, and accountability for automated decisions are just some of the ethical challenges that organizations will need to address. Addressing these issues proactively will be necessary to maintain the trust of customers and society at large.
Finally, looking ahead, the AI market in Brazil must prepare for a growing demand for professionals who possess not only technical skills but also a deep understanding of the strategic impact of AI on business. Those who can effectively integrate AI into their operations will be at the forefront of innovation, leading their organizations to new frontiers of efficiency and competitiveness.
*Natália Marroni Borges is a researcher at ABES Think Tank, Researcher member of the IEA Future Lab group (linked to the Federal University of Rio Grande do Sul – UFRGS), Post-doctorate in Artificial Intelligence and Foresight and professor at UFRGS.
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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