Latin American AI Index 2025: What IT and Development Teams Need to Know
Chile leads the region's AI readiness for the second year with 70.5 points, followed by Brazil (67.3) and Uruguay (62.3). The index benchmarks AI development across 19 countries in Latin America and the Caribbean.
Momentum is strong in Costa Rica, Ecuador, the Dominican Republic, and Guatemala. Gains in connectivity, training, and formal AI strategies show that clear policy and education reforms move the needle faster than scale alone.
How the index measures progress
The study assesses three dimensions: enabling factors (infrastructure, talent, data), research-development-adoption, and governance. Each country scores up to 100 based on digital maturity and AI policy execution.
Signals that matter for engineers and data teams
- Compute concentration: Brazil holds over 90% of the region's high-performance computing capacity. Only Uruguay, Costa Rica, and Colombia show competitive per-capita GPU. With 11 countries below 50 points in digital infrastructure, expect GPU scarcity and uneven access. Plan for multi-region GPU sourcing, quantization and distillation, smaller model baselines, and queue-aware training.
- Talent bottleneck: 13 of 19 countries lack AI doctoral programs. About 68% of active AI researchers are in Brazil and Mexico, and Chile and Brazil produce 90% of the region's high-impact publications. Build pipelines for junior and mid-level roles, sponsor advanced study, and lean on remote talent networks.
- Data availability: The region generates large volumes of data, but only a few countries-led by Chile and Mexico-are opening and standardizing it. Expect bias and limited policy impact without better access. Invest in internal data governance, clear provenance, schema standardization, and safe data-sharing partnerships.
- Education shifts: Costa Rica, Ecuador, the Dominican Republic, and Uruguay are integrating AI into school curricula. Colombia's surge in self-taught learning on platforms like Coursera is raising digital literacy outside traditional systems. Expect more junior candidates with strong foundations and project portfolios.
- Governance and civic tech: Public participation using AI for consultations or co-creation is still limited. Only Mexico, Colombia, and Peru show concrete progress. Build privacy, auditability, and human oversight into your stack now; compliance will follow the innovators.
Country signals to watch
- Chile (70.5): Top score, progress on open data, strong governance signals.
- Brazil (67.3): Compute hub and publication leader; concentration creates both opportunity and dependency risk.
- Uruguay (62.3): Competitive per-capita GPU, favorable for small, high-impact teams.
- Costa Rica, Ecuador, Dominican Republic, Guatemala: Fast movers via connectivity, training, and national strategies-good ground for pilots and partnerships.
- Mexico: Research concentration, open data advances, and civic AI pilots emerging.
- Colombia: Strong self-taught upskilling and competitive per-capita GPU; active in civic participation.
- Peru: Early progress on AI-enabled citizen consultations.
"Without real data availability, algorithmic decisions can be biased and public policies less effective," the report notes. Rodrigo Durán warned that while progress is real, the gap with major tech powers is widening. Álvaro Soto added that Latin America already accounts for 15% to 20% of the global market for generative AI apps but only 1% of global investment-clear upside if infrastructure, talent, and governance accelerate.
What to do next (practical actions)
- Infrastructure strategy: Secure multi-cloud GPU access, mix spot and reserved capacity, use efficient architectures (LoRA, 4/8-bit inference), and schedule training jobs around availability. Consider model selection that fits your compute reality.
- Data engineering: Stand up a data contract and schema registry, version datasets and features, track consent and lineage, and participate in open-data initiatives to reduce bias and improve coverage.
- Talent development: Fund certifications and internal guilds, prioritize portfolio-based hiring, and create staff rotation between data, MLOps, and product to scale applied skills.
- Research partnerships: Co-author with local universities, support open-source contributions, and run shared compute or data sandboxes to accelerate applied R&D.
- Governance by design: Maintain model cards, evaluation playbooks, red-teaming, and incident response. Add measurable guardrails: safety filters, privacy checks, and approval workflows.
- Civic collaboration: Pilot citizen-facing AI services with audit trails and feedback loops; reuse learnings for enterprise-facing governance.
Resources
Skill up the team
- AI courses by job role for structured upskilling across engineering, data, and product.
- AI certification for coding to formalize practical competencies.
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