AI Adoption Accelerates in Latin America, but Challenges Remain

Latin American organizations, including Mexican SMEs and startups, are accelerating AI adoption to improve operational efficiency, innovation, and competitiveness. Challenges remain in governance, talent development, infrastructure, and strategic alignment, with structural readiness gaps between workers and enterprises. Industries most affected include manufacturing, logistics, finance, and technology.

A new report from the Unión Industrial Argentina (UIA), with technical support from the International Labour Organization’s Employers Activities Office (ACT/EMP), highlights opportunities and challenges for Latin American businesses in adopting AI responsibly. The guide, Inteligencia Artificial: desafíos y oportunidades para organizaciones empresariales en América Latina, provides a roadmap for organizations seeking to strengthen competitiveness and foster inclusive digital transformation.

AI’s rapid advancement is shifting the technology from hype to measurable business impact, says Mauricio Torres Echengucia, General Manager, IBM México. “Now is the time to gather these learnings and look ahead at how AI will fold into enterprise technology,” he says, noting trends such as operating AI agents at scale, specialized AI infrastructure, and measurable ROI from integrated AI solutions.

The report emphasizes that AI adoption must be aligned with organizational strategy. The International Labour Organization (ILO) notes structural changes in labor markets due to AI, particularly affecting highly skilled and younger workers, highlighting the need for organizations to manage workforce transitions responsibly.

Context: Readiness Gaps and Structural Challenges

Research from The Conference Board points to a widening gap between employee readiness and organizational capacity. While 85% of workers expect AI to improve their jobs, 42% anticipate workforce reductions. Many companies lack unified AI strategies, governance frameworks, and talent models, limiting the scaling of AI pilots beyond localized efficiency gains.

Globant’s Tech Trends 2026 emphasizes that the next phase of AI maturity will be defined by agentic AI, quantum communication, polyfunctional robotics, ambient intelligence, and AI-powered cybersecurity. “The next 2026 will be another year in which companies across all industries will accelerate their own transformation processes,” says Diego Tártara, Global CTO at Globant.

Operational intelligence also relies on AI observability, according to Carmen Nava, Senior Strategic Enterprise AE, Dynatrace. Observability tools provide a unified view of cloud-native and AI workloads, ensuring compliance, detecting inefficiencies, and aligning AI outputs with strategic goals. “Operating AI systems without observability can be similar to flying a plane without radar,” Nava says.

Complementary Details: Costs, Talent, and Ecosystem Development

Manolo Atala, Co-Founder, Fairplay, warns that generative AI is not inherently cheap. Costs for enterprise-scale adoption include cloud compute, licenses, talent, integration, and compliance. Atala noted that the ROI of AI depends on scaling beyond pilots, generating new revenue streams, and integrating risk management into adoption strategies.

In Mexico, initiatives such as PotencIA Mx, launched by Tecnológico de Monterrey, Meta, and the Ministry of Economy, are supporting SMEs and startups to integrate AI into operations. The accelerator combines academic expertise, government support, and private-sector collaboration to test AI applications, improve operational efficiency, and explore new business opportunities. Íñigo Fernández, Director of Public Policy in Mexico, Meta, says the program represents the democratization of an innovation that will transform the economic landscape.

Collectively, these developments show that Latin American organizations are preparing to leverage AI across sectors, but success will require strategic alignment, governance, talent development, and infrastructure investment. Enterprises that invest in human capital, observability, and structured AI adoption frameworks are expected to capture value while mitigating operational, compliance, and workforce risks.