by Sabur Butt, Hector G. Ceballos, and Michael Fung
Latin America stands at a familiar crossroads. Once again, a technological revolution is reshaping how work gets done, what skills employers value, and how quickly the workforce must adapt. And once again, the region risks being a generation behind.
During the Third Industrial Revolution of the 1970s–1990s, East Asia invested decisively in microelectronics, computing, and technical education. Latin America, consumed by debt crises and macroeconomic instability, missed the wave. The cost was measured not only in lost output but in institutional habits of looking backward while others prepared to look forward. Today, the Fourth Industrial Revolution is unfolding faster than its predecessor, and the region’s central deficit is not money or talent. It is foresight leading to informed interventions.
A structural mismatch between work and learning
Industries now change in cycles of months. University curricula change in cycles of years. That gap is no longer a minor inefficiency; it is the bottleneck that determines whether a country prepares its workforce in time or trails behind it.
The numbers are sobering. Across the six largest Latin American economies, between 19% and 21% of workers face a high probability of automation-related displacement. Almost half work in informal jobs. University dropout rates exceed 50% in several countries, climbing as high as 76% in the Dominican Republic. Students perform consistently below OECD averages in foundational literacy and numeracy. By the time graduates from the educational system enter the labour market, there is already a structural gap between what they can do and what employers want.
What the region needs is not another labour-market report. It needs skills intelligence, a continuous, forward-looking system that informs policymakers what is rising, what is fading, and where the workforce can realistically move next.
Why traditional forecasting falls short
Most forecasting in the region still relies on expert panels, occupational surveys, and static taxonomies that take years to update. These tools were designed for a slower-paced world.
To illustrate the issue, consider how such static taxonomies treat “database management” as a single skill, when the real labour market is moving between MySQL, PostgreSQL, MongoDB, Redis, Cassandra, and now vector databases, each with a different demand trajectory. Or consider the noise in a single automotive dataset, where the same assembly-line job appears as “ensamblador de carrocerías”, “operador de ensamble”, “ensamblador automotriz”, and “técnico en armado de vehículos”. Human annotators cannot keep up; even expert raters disagree on whether two skill descriptions refer to the same thing.
By the time a traditional taxonomy recognizes “prompt engineering” as a skill category, the labor market has already moved on to whatever comes next.
A data-driven alternative, already running
In our paper, we describe an operational deployment at the Institute for the Future of Education at Tecnológico de Monterrey. It is not a proposal; it is a working system (See Figure 1).
Figure 1. The framework links real-time labour market signals to an AI-assisted, expert-validated skills intelligence layer, which in turn informs curriculum design,reskilling pathways, workforce policy, and governance.
The approach combines large language models with retrieval-augmented generation to build dynamic, hierarchical skill taxonomies that update themselves as new job postings flow in. Each new skill is matched semantically against the existing taxonomy. Known skills are normalized to canonical terms. Genuinely new ones are flagged, classified, and added.
In Mexico’s automotive sector alone, the system has mapped more than 11,000 skill variations across 220 hypernym categories, identified 847 unique skills clustered into 12 occupational groups, and tracked the rise of electric-vehicle competencies in real time. Generative AI skills surfaced in the data months before they appeared in any official classification.
The infrastructure required is modest. A taxonomy covering 10,000 skills across major sectors can be maintained on standard cloud infrastructure for roughly $500–1,000 USD per month, within reach of education ministries in developing economies.
From data to policy
Technology alone does not change a system. The harder work is institutional. To shorten the lag between detecting a skill shift and updating a training program (currently 18 to 24 months) in most regions, curriculum committees must meet more often, must accept real-time data alongside surveys, and procurement procedures must allow timely equipment purchases according to the emerging skills.
Governance matters equally. Sustainable implementations bring together labour ministries, education ministries, economic development ministries, national statistics offices, industry associations, and universities. Each contributes something the others cannot: data access, curriculum authority, methodological rigor, domain expertise, and research capacity. No single actor owns skills or intelligence; the legitimacy of the system depends on shared ownership.
Localization also matters. Global taxonomies like ESCO and O*NET are useful starting points, but they need to incorporate regional terminology, indigenous skill categories, and sector-specific competencies. A skill system that does not speak the local language of work will not be trusted by the people meant to use it.
We also acknowledge real-world limitations. Many countries still hire through newspaper classifieds, physical noticeboards, and informal networks. Supplier tiers and small enterprises seldom advertise online. A system built solely on digital postings produces a geographically and structurally biased picture. Integrating offline data sources, replicating the approach across countries, and validating its predictions over time are the necessary next steps.
What success would look like
Executed well, skills intelligence reshapes reskilling itself. Instead of generic, fixed-duration programs, workers receive personalized pathways: a manufacturing technician’s quality-control experience mapped onto automated-systems monitoring; a mid-career professional alerted 12–24 months ahead of emerging demand; a young job-seeker pointed toward a stackable micro-credential that the market will actually reward.
For Latin America, this is more than a technical upgrade. It is a chance to break a historical pattern of arriving late to every industrial revolution and start arriving prepared. The region has the data, the talent, and increasingly the tools. What it has lacked is the institutional capacity to anticipate. That capacity is now within reach.
The Fourth Industrial Revolution will not wait. But for the first time, neither does the evidence.
Reference: Butt, S, Ceballos, HG, and Fung, M (2026) ‘From Data to Policy: Building an Evidence-Based Future for Skills, Work, and Learning in Latin America’ Policy Reviews in Higher Education
Sabur Butt is a research professor at the Institute for the Future of Education, Tecnológico de Monterrey. His work focuses on artificial intelligence, natural language processing, and dynamic skills taxonomies, with a particular interest in how AI-driven labour-market intelligence can inform education and workforce policy across Latin America.
Hector G Ceballos is Director of the Living Lab & Data Hub of the Institute for the Future of Education (IFE) at Tecnológico de Monterrey. His research spans data science, knowledge engineering, and educational analytics, with a focus on building evidence-based systems that connect higher education to evolving industry and labour-market needs.
Michael Fung is Executive Director of the Institute for the Future of Education at Tecnológico de Monterrey. He was formerly the Deputy Chief Executive at SkillsFuture Singapore (SSG). He led the development of a comprehensive education and training ecosystem under the national SkillsFuture movement, which has become a global benchmark and reference for workforce skills development and lifelong learning across society.
