Why Latin America is falling behind on agentic AI

January 22, 2026 3 min read

Despite rapid market growth, only 14% of firms in Latin America have implemented autonomous AI projects.

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The promise of self-operating artificial intelligence is already on the table, yet few companies in Latin America are ready to capitalize on it.

What is holding the region back from a technology that could redefine productivity and business competitiveness?

Artificial intelligence with autonomous agents is generating growing expectations among Latin American companies, but its real-world implementation remains in its early stages. While it is increasingly cited as a key driver of digital transformation, only a minority of organizations have successfully put it into practice.

According to the study “IT Maturity for the Adoption of Agentic AI”, conducted by IDC on behalf of Intel, only 14% of organizations operating in the region currently have active agentic AI projects.

A Technological Leap.

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So-called Agentic AI represents a significant shift from traditional artificial intelligence models. Unlike solutions focused solely on data analysis or decision support, this technology advances toward the autonomous execution of actions to achieve specific objectives, with minimal human intervention.

In a business context, this translates into operational efficiency, cost reduction, and a smoother experience for customers and users.

This lag in adoption contrasts with the sustained growth of the regional AI market. According to the report “Economic and Workforce Impact of Open Source AI in Latin America” by The Linux Foundation, sponsored by Meta, the Latin American AI market is valued at USD 12.7 billion and is projected to grow at nearly 28% annually between 2023 and 2030, driven by increased investment in research and development, regulatory incentives, and public-private partnerships.

Federico dos Reis, CEO of INFORM for Latin America, warns:

“Agentic AI will accelerate digital transformation for companies and enable them to achieve higher productivity. Organizations that do not advance now in the maturity of their data and processes risk losing competitiveness to more agile players.”

The Internal Gap.

Experts agree that the main obstacle to adopting agentic AI is not technological but structural. These solutions require integrated data, standardized processes, and clear business rules that allow decisions to be automated safely, at scale, and aligned with each organization’s strategic objectives.

On this path, companies like INFORM have supported organizations in banking, industry, and retail in moving from pilot projects to operational models based on autonomous agents, under defined governance frameworks.

The CEO adds:

“For agentic AI to generate tangible impact, it is essential to advance in data availability, quality, and integration, as well as to have architectures that enable controlled decision automation.”

Impact and Projections.

Market projections reinforce the strategic importance of this technology. According to Gartner estimates, by 2029, agentic AI will autonomously resolve up to 80% of routine customer service issues, potentially reducing associated operational costs by nearly 30%.

In this scenario, agents will not only respond to inquiries but also anticipate incidents, adjust business conditions, and execute corrective actions without direct human intervention.

The financial sector is among the main beneficiaries of this evolution. So-called Agentic Banking is giving rise to agents capable of operating as permanent digital executives, performing tasks ranging from account validation and the management of local and international payments to the autonomous administration of spending limits, always within strict security and regulatory compliance frameworks.

In this context, competitive advantage will not come from simply adopting artificial intelligence, but from doing so with a long-term strategic vision. Dos Reis explains:

“Multi-agent architectures enable continuous learning and process optimization; however, their effectiveness depends on proper orchestration among data, rules, and operational workflows. The potential of combining different AI approaches—such as Process AI alongside agents—is enormous. While intelligent algorithms tackle complex problems, agents can play a key role in assisting humans with exception management and analyzing the implications of each decision. In the coming years, this complementarity will be indispensable for companies. Without a solid foundation, the value of agentic AI simply cannot materialize.”