The debate around artificial intelligence and work has shifted from asking which tasks machines can perform to examining how AI influences the way human capabilities are built and transmitted over time. This issue is particularly urgent for Latin America and the Caribbean, where practical judgment and experience play a critical role in compensating for weaker schooling systems. Policymakers are encouraged to expand learning-by-doing opportunities, such as apprenticeships and dual training, to ensure AI adoption does not erode the channels through which tacit knowledge is passed on.
A key distinction lies between explicit knowledge, which can be codified and easily scaled by AI, and tacit knowledge, which is gained through practice, intuition, and context. While AI excels at handling codified information, it cannot replicate the practical judgment that comes from experience. As codified knowledge becomes more abundant, tacit expertise may grow in value, reshaping how workers are paid and valued. In occupations exposed to AI, the return to formal education may weaken relative to the return to experience, pushing firms to rely more on documented work histories and demonstrated performance than on degrees alone.
For Latin America and the Caribbean, the risk is acute. Many entry-level jobs in services and administrative roles—key gateways into the labor market—are among the most exposed to AI. If these roles disappear too quickly, young workers may lose vital opportunities to acquire practical judgment. This erosion of learning-by-doing could undermine both individual career development and firms’ long-term productivity, especially in knowledge-intensive sectors where tacit expertise is essential for integrating new technologies.
The Inter-American Development Bank has identified this challenge as central to AI readiness in the region. It emphasizes the importance of dual education, supervised practical experience, and finishing schools co-designed with employers to preserve the transmission of tacit knowledge. Evidence shows that training anchored in real tasks and evaluated on performance yields better outcomes than programs focused solely on credentials.
A realistic roadmap for AI skills in the region includes three priorities: rebalancing education to emphasize experiential learning, making employment records portable so workers can carry proof of their practical experience, and subsidizing early-career opportunities to ensure young workers gain exposure despite shrinking entry-level openings. Without these measures, the short-term productivity gains from AI risk being offset by a long-term erosion of human capabilities, with negative economic and social consequences.
Ultimately, the AI debate in Latin America and the Caribbean must go beyond adoption and regulation to ask what kind of human capital is being built. The central question is whether workers are being given the practical experiences they need to develop judgment and resilience in a rapidly changing labor market.
