From opportunity to obstacle: AI adoption struggles in Latin American SMEs

Artificial intelligence (AI) is opening new pathways for small and medium-sized enterprises across Latin America to boost efficiency, navigate uncertainty, and strengthen competitiveness in volatile economic conditions. However, a new systematic review finds that despite growing adoption, deep structural constraints continue to limit the region’s ability to fully capitalize on AI-driven transformation.

Researchers examine how AI is being integrated into business operations across LatAm SMEs, highlighting both the promise of digital transformation and the persistent gaps that threaten long-term sustainability and resilience. According to the analysis, AI is not only a technological innovation but as a critical mechanism for navigating uncertainty in complex value chains.

The study, titled “AI Applications That Can Support Sustainable Practices in Small and Medium-Sized Enterprises in Latin America: A Systematic Review,” analyzes selected studies to identify patterns of adoption, barriers, and strategic impacts of AI across the region.

AI drives efficiency and resilience across key business sectors

The study finds that AI is already delivering measurable benefits in several strategic sectors, particularly logistics, manufacturing, telecommunications, and financial services. Across these domains, companies are using AI to automate routine processes, optimize supply chains, and improve decision-making through predictive analytics.

AI can enhance decision-making under uncertainty. Businesses are increasingly deploying machine learning models to anticipate disruptions, analyze market trends, and optimize operations in real time. These capabilities have proven especially valuable in environments characterized by volatility, where rapid adaptation is essential for survival.

In manufacturing and logistics, AI is being used to predict equipment failures, manage inventory, and streamline production processes. These applications reduce operational inefficiencies and improve responsiveness to supply chain disruptions. In financial services, predictive analytics is enabling more accurate risk assessment and market forecasting, supporting data-driven decision-making at both strategic and operational levels.

The study also brings to light the growing role of AI in customer interaction and business intelligence. Companies are leveraging AI-driven tools to personalize services, improve customer experiences, and extract actionable insights from large datasets. These capabilities contribute to increased competitiveness, allowing SMEs to compete more effectively in both regional and global markets.

Importantly, the research identifies AI as a key driver of organizational resilience. By enabling better forecasting and adaptive decision-making, AI helps companies respond to external shocks such as economic instability, geopolitical changes, and global crises. This resilience has become a critical factor in maintaining business continuity in uncertain environments.

At a broader level, AI adoption is contributing to cost reduction and productivity gains. Automation of repetitive tasks allows businesses to allocate resources more efficiently, while data-driven strategies enhance operational performance. These improvements are particularly significant for SMEs, which often operate with limited resources and face intense competitive pressures.

Infrastructure gaps, talent shortages, and costs slow adoption

The study identifies significant barriers that continue to hinder the widespread adoption of AI across Latin American SMEs. One of the most critical challenges is the lack of adequate technological infrastructure. Many businesses in the region lack access to reliable high-speed internet, cloud computing resources, and advanced digital platforms necessary for implementing AI solutions. This infrastructure gap is particularly pronounced in rural and less developed areas, creating uneven adoption across countries and sectors.

The shortage of specialized talent represents another major constraint. Effective AI implementation requires expertise in data science, machine learning, and algorithm development, skills that remain scarce in the region. Limited access to advanced training programs and weak collaboration between academia and industry further exacerbate this talent gap.

Budget constraints also play a decisive role, especially for small and medium-sized enterprises. The high cost of acquiring, implementing, and maintaining AI technologies can discourage investment, particularly in economies facing inflation, financial instability, and limited access to capital. This creates a cycle where companies that could benefit most from AI are often least able to adopt it.

The study describes this dynamic as a “technological adoption paradox,” where organizations must modernize to remain competitive but lack the resources to do so effectively. This paradox is compounded by resistance to change within traditional business structures, which can slow the integration of new technologies into existing processes.

Regulatory uncertainty adds another layer of complexity. The absence of robust legal frameworks governing AI use in many Latin American countries creates risks related to data privacy, algorithmic transparency, and ethical accountability. Without clear guidelines, companies may hesitate to adopt AI, fearing potential legal and reputational consequences.

The study also notes disparities in adoption across countries. While nations such as Chile and Brazil show relatively high levels of AI adoption, others lag behind due to economic constraints and infrastructure limitations. This uneven landscape highlights the need for coordinated regional strategies to support digital transformation.

Ethical concerns and governance gaps shape the future of AI adoption

In addition to technical and economic barriers, the study highlights the importance of addressing ethical and governance challenges associated with AI implementation.

Data privacy emerges as a central concern, particularly as businesses increasingly rely on large datasets to train AI models. The lack of comprehensive data protection regulations in some countries raises risks of misuse and undermines consumer trust. Ensuring secure and responsible data handling is essential for sustaining AI adoption.

Algorithmic transparency is another critical issue. AI systems often operate as complex, opaque models, making it difficult for users to understand how decisions are made. This lack of transparency can lead to concerns about fairness, bias, and accountability, particularly in sectors such as finance and public services.

Ethical considerations are not peripheral but integral to successful AI adoption. Without trust in AI systems, businesses may face resistance from customers, employees, and regulators, limiting the effectiveness of technological investments.

To address these challenges, the research highlights the importance of public policy and institutional support. Governments play a key role in establishing regulatory frameworks that promote transparency, protect data privacy, and ensure ethical use of AI technologies. At the same time, public–private partnerships can facilitate knowledge transfer, skill development, and resource sharing.

Educational initiatives are also critical. Expanding access to training in AI-related fields can help bridge the talent gap and empower businesses to integrate advanced technologies into their operations. Collaborative efforts between universities, industry, and government institutions are essential for building a sustainable AI ecosystem.

The study also identifies a gap in current research regarding the environmental impact of AI technologies. While AI can support sustainability goals by optimizing resource use and reducing waste, its energy consumption and carbon footprint remain underexplored. Addressing this dimension will be important for aligning AI adoption with broader sustainability objectives.

AI emerges as a strategic tool for sustainable growth, but progress remains uneven

Artificial intelligence holds significant potential to drive sustainable development in Latin American SMEs, but its impact depends on overcoming deeply rooted structural challenges. AI is already enabling businesses to improve efficiency, reduce costs, and enhance resilience in uncertain environments. Its applications in predictive analytics, automation, and decision-making are transforming how companies operate and compete. However, these benefits are not evenly distributed, with many organizations still struggling to access the resources needed for effective implementation.

The path forward requires a coordinated approach that combines technological investment, policy development, and capacity building. Strengthening infrastructure, expanding access to education, and establishing clear regulatory frameworks will be essential for unlocking the full potential of AI in the region.

The study also calls for sector-specific strategies that align AI capabilities with the unique needs of different industries. Tailored approaches can help address specific barriers and maximize the impact of AI adoption across diverse economic sectors.