There is an interesting paradox in the artificial intelligence landscape across Latin America. The region has interest, it has favorable demographics, it has clear use cases, and it has — in many sectors — an urgent need to improve productivity. And yet, real adoption of AI in critical business processes is advancing more slowly than those conditions would justify.
Why? That is the question worth answering honestly.
The AI adoption rate among Latin American companies is 37%, compared with the 42% global average. The gap is not dramatic in percentage terms, but what that number fails to capture is the quality of adoption.
The ILIA 2025 index sums it up well: AI literacy levels in the region nearly double the levels of professional training and are four times the specialized, frontier talent pool. Plenty of people know how to use AI tools for everyday tasks. Few have the technical capability to integrate them into production systems or design suitable data architectures. That difference — between casual use and integration capability — is the most important gap, and the one least often mentioned.
Investment: Latin America receives 1.12% of global AI investment even though it represents 6.6% of world GDP (ECLAC, 2025). That underinvestment has direct consequences: less compute infrastructure available locally, less capital for AI projects, and a vendor ecosystem that still lacks the scale of the Global North.
Talent: Since 2022, the specialized AI talent gap between Latin America and the global average has been widening, not narrowing (ILIA 2025). The most highly trained professionals have global career options that the region often cannot match. The ILO and the World Bank estimate that 17 million jobs in LATAM with the potential to be enhanced by AI are being held back precisely by these infrastructure and talent gaps.
Data infrastructure: Most mid-sized companies do not have a data architecture that can support AI initiatives at scale. Data is fragmented across different systems, uncataloged, and lacking established quality processes. Before you can talk about machine learning, you have to talk about data governance — a far less glamorous conversation, but an equally necessary one.
Gartner has a name for the most common problem among companies that do have resources: AI Pilot Purgatory. Projects that prove value in a controlled environment but never scale, because the organization lacks the internal infrastructure — technical, data, cultural, and governance — to sustain them in production.
Between 70% and 85% of AI initiatives fail to achieve their expected results (Gartner). And in the region, where that supporting infrastructure is less mature, the percentage is likely even higher. The most common reasons: projects without defined business KPIs, data of lower quality than assumed, existing systems that are not ready to integrate with AI solutions, and cultural change that takes longer than estimated.
The AI technology gap in Latin America has two dimensions that are frequently confused. The first is the gap between the region and more advanced markets — real in terms of investment, talent, and infrastructure, and one that requires public policy decisions and time to close.
The second is the gap within the region itself — between the leading companies already integrating AI effectively and everyone else still in exploration mode. This second gap is the one that most directly affects individual competitiveness, and also the one companies can do the most to close through the right decisions. The companies on the right side of it do not necessarily have more resources. They have more strategic clarity, stronger data foundations, and an organizational culture that treats AI as a business capability rather than a technology experiment.
An honest data inventory: Before thinking about tools, it is worth understanding what data the organization has, where it lives, and what quality it holds. That diagnosis reveals more about real readiness than any tool evaluation.
Focus on two or three concrete use cases: Do not try to cover everything at once. Dispersion is one of the most common factors in projects that fail to scale.
Measure from the start: Define which business metrics will be used to assess impact. Without that, it is impossible to know whether the project is generating value or simply generating activity.
The gap is real. So is the possibility of narrowing it. The difference between the organizations that close it and those that do not usually lies more in the decisions they make than in the resources they have.
Sources: ECLAC / CENIA (ILIA 2025), Gartner, ILO / World Bank 2024, Microsoft LATAM, McKinsey Global Institute, IDC.
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Andrés Lozada
Executive Director, SUMāTO Group · Cloud · Infrastructure · Cybersecurity · Digital Transformation
linkedin.com/in/andreslozada/