Artificial intelligence is no longer just a support tool to become a strategic partner of companies and professionals. The popularization of generative models was the gateway: now, we live the rise of Agentic AIs, systems capable of operating autonomously, with decisions and executions in real time.
More than agents restricted to specific tasks, these models take on end-to-end processes, from data analysis to decision making, reducing costs, accelerating operations and increasing efficiency in different sectors.In the financial market, for example, they are already applied to analyze large volumes of data, fraud detection and risk anticipation, protecting investments and customers.In retail, they function as invisible inventory managers, predicting demand peaks, automating purchases and adjusting prices according to consumption patterns.
Relevance is confirmed by market projections. According to the reports “Emerging Tech: The Future of Agentic AI in Enterprise Applications” and “Emerging Tech: Maximize Opportunities While Managing Risks of Agentic AI on Enterprise Software”, both from Gartner, about 30% of global enterprise software revenue could be driven by Agentic AI by 2035, which represents more than US$ 450 billion.
Companies as academies of the future
This evolution brings a new requirement: to prepare professionals capable of building, implementing and governing robust AI projects. This is an advanced technology, which demands skills far beyond basic or superficial knowledge. Understanding its real functionalities requires continuous, applied and practice-oriented learning.In this scenario, companies cannot limit themselves to adopting tools, they need to assume the role of true learning centers, capable of developing talents and sustaining innovation.
This need becomes even more evident when we observe that many professionals cannot solve systemic challenges precisely because they do not understand how certain knowledge applies in everyday life. This is because, to a large extent, they still rely only on theoretical or self-taught learning 3.5 thousand paths that, although valid, are insufficient in the face of current technological complexity. The study “: In Progress & Discoveries of how AI is transforming Work”, in which Google interviewed more than 3.5 thousand professionals from different sectors in Brazil and other countries, illustrates this reality well by showing that 47% of Brazilians seek to learn about AI on the internet.
This learning model is relevant, but does not alone support the full potential of Agentic AIs. It is necessary to go further, apply knowledge in a critical and contextualized way, expanding learning to aspects that go beyond the technique and dialogue with the reality of organizations. This includes ethical concerns, such as the evaluation of biased systems, and also governance, such as the definition of responsibilities for decisions taken autonomously by algorithms.
Organizations need to consolidate themselves as living learning environments.This means investing not only in formal courses, but also in continuous upskilling and reskilling programs, mentoring and practical experiences in large projects. Learning in a team, side by side with peers and more experienced professionals, is what turns knowledge into applied competence.
In the age of Agentic AI and accelerated change, acquiring new skills will never be a point of arrival, but rather a constant path to turning innovation into lasting competitive advantage.Each new learning and application also represents an opportunity to generate value for business.
*Guilherme Pereira is the Innovation Director of Alura + FIAP For Companies, Alun Group corporate education solutions, the largest business and technology education ecosystem in Latin America, which support organizations of all sizes and segments in the transformation of careers and businesses.
