Organizations across Latin America face a critical governance challenge: employees are adopting AI faster than companies can establish safeguards to manage it responsibly. According to EY's 2025 Work Reimagined Survey, 93% of employees in the region already use artificial intelligence in their daily work, exceeding the global average of 88%. However, this rapid adoption is creating what researchers call "Shadow AI," a phenomenon where workers introduce personal AI applications into corporate environments without IT department oversight, exposing businesses to serious security, compliance, and data protection risks.

What Is Shadow AI and Why Should Organizations Care?

Shadow AI refers to the use of unauthorized, personal AI tools in the workplace. Between 27% and 56% of employees in Latin America, depending on the industry, are using external AI applications without approval from their IT departments. While this demonstrates employee initiative and a desire to improve productivity, it introduces significant vulnerabilities. Employees report saving an average of nine hours per week through AI-assisted tasks, compared to the global average of eight hours, yet many are achieving these gains through tools their organizations cannot monitor or control.

Rather than viewing Shadow AI purely as a security threat, EY's research suggests it reflects unmet demand for enterprise AI capabilities. Employees are adopting external tools because they perceive them as more accessible or better suited to their daily work than the solutions formally provided by their organizations. This gap between what employees need and what companies provide is driving risky behavior that puts sensitive data, intellectual property, and regulatory compliance at risk.

Why Are Organizations Failing to Translate AI Adoption Into Business Value?

The disconnect between widespread AI use and measurable business outcomes is striking. Only 28% of organizations successfully translate AI implementation into tangible business value, according to the survey. EY attributes this gap not to technological limitations, but to weaknesses in talent strategies, organizational culture, continuous learning, and governance.

The research reveals that adoption remains concentrated in relatively simple activities. Information searches account for the most common use case, followed by document summarization and email drafting. More sophisticated applications, including deep research, decision evaluation, and AI-powered mentoring, remain far less common. Only 5% of AI users qualify as advanced users who combine multiple AI tools, assistants, and agents to substantially reshape how they work. This gap between widespread adoption and advanced implementation helps explain why relatively few organizations are realizing significant returns from their AI investments.

"The real challenge of AI is not only technological, it is organizational. Shadow AI is the clearest sign that talent is already moving ahead and that there is still enormous potential to capture," said Carolina González, People Consulting Leader at EY Latin America.

Carolina González, People Consulting Leader, EY Latin America

How to Build Sustainable AI Adoption in Your Organization

EY's research identifies three critical factors that organizations must combine to generate stronger productivity gains and reduce Shadow AI risks:

• Employee Skills Development: Training emerges as a significant driver of adoption. Employees who complete more AI learning hours demonstrate substantially higher levels of AI usage, which in turn correlates with greater productivity gains. However, organizations must pair workforce development with talent retention strategies, as employees with more than 80 hours of AI training are 55% more likely to leave their employer than average.
• Access to Appropriate AI Tools: Organizations must provide employees with enterprise AI solutions that are accessible, secure, and suited to their daily work. When employees perceive formal tools as inadequate, they turn to unauthorized alternatives, creating Shadow AI risks.
• Workplace Culture That Encourages Responsible Adoption: Formal incentives, AI-specific learning programs, role-based tools, and leadership confidence all contribute to higher AI maturity. Organizations that foster a culture of responsible innovation see better outcomes than those that either restrict AI use or ignore it entirely.

González emphasized that when AI adoption is channeled correctly, it not only drives productivity but redefines how organizations work, supports better decision-making, and improves performance. However, when adoption moves ahead of strategy, that potential is diluted and organizations lose a significant share of the value AI can generate.

Regional Variations in AI Readiness Across Latin America

The survey also identifies notable differences in AI readiness across global markets. Asia-Pacific countries dominate EY's AI maturity rankings, while Latin American markets show mixed performance. Brazil records the region's strongest AI score at 41, above the global average benchmark of 34, while Mexico scores 30, placing it below the global average alongside Colombia. These variations suggest that regional economic conditions, workforce education levels, and organizational maturity all influence how effectively companies can govern and benefit from AI adoption.

Both employers and employees broadly agree on the primary risks associated with expanding AI adoption. Security, protection, and oversight rank as the top concern for both groups, followed by the potential erosion of human expertise through excessive dependence on AI. Job displacement, privacy risks, misinformation, and the need for workforce reskilling also rank among the most significant issues.

What Does This Mean for Business Leaders?

The survey signals that competitive advantage created by AI will increasingly depend on organizational readiness rather than access to technology. Companies that successfully integrate talent, culture, technology, and governance to scale AI responsibly will outpace competitors that treat AI as simply another tool to deploy. The difference lies with those who recognize that AI is the factor that will define leadership and competitive advantage in the coming years.

For organizations struggling with Shadow AI, the path forward requires moving beyond a purely security-focused approach. Instead, leaders should view Shadow AI as a diagnostic signal: it reveals where employees perceive gaps in official AI capabilities and where organizational governance has fallen behind workforce needs. By addressing these gaps through better tools, training, and culture, organizations can transform Shadow AI from a risk into an opportunity to build more mature, productive, and secure AI adoption.