AI Adoption: Skills and Strategies for Latin America

Artificial Intelligence is not just a technology of the future; it is a reality redefining how we work and learn. But how does AI truly impact the workplace and skill development within Latin American organizations?

This increasingly common question is part of the reality we face at Ingenia daily, and one that I generally address in my role as a Team Dynamics Architect, guiding people and organizations to be prepared for the coming changes.

Based on a recent research study regarding perceptions, uses, and barriers linked to the adoption of Artificial Intelligence in real work contexts—conducted by Psychopedagogy students within the framework of the Intervention in Organizations Chair at the University of the Savior (Buenos Aires, Argentina), where I am a professor—we set out to analyze the results to outline an answer to this question. This was based on the gradual adoption model proposed by Melissa Webster and George Westerman, experts from the MIT Sloan Management Review, in their article “Generate Value From GenAI With ‘Small t’ Transformations.”

This approach allowed me to evaluate the level of implementation of generative AI in the region, identifying not only opportunities but also the specific gaps and barriers organizations face. It considered the required hard and soft skills, existing ethical frameworks, and organizational conditions that either foster or hinder learning and technological appropriation. This study provides a clear vision of the challenges and opportunities ahead for Latin American companies to leverage the potential of AI.

Through open interviews with employees, team leaders, and Human Resources professionals, key information was gathered regarding:

• Training needs: What skills do teams need to develop to work with AI?
• Perceptions of AI: How do employees view this technology? Do they perceive it as a help or a threat?
• Organizational conditions: What factors facilitate or hinder the integration of AI into the daily learning routine?

Framework The “Small t” Model

The “small t” transformations model proposed by Webster and Westerman suggests that most organizations are generating value with Generative Artificial Intelligence (GenAI) not through massive disruptions, but through incremental improvements that reduce risks and allow for the gradual building of organizational capabilities. This approach recognizes that massive transformations require time, investment in data and infrastructure, and a solid ethical framework; therefore, companies choose to start with low-risk, high-immediate-impact applications.

The framework distinguishes three levels of adoption, which scale in complexity and risk. This classification allows organizations to map their current initiatives and plan for staggered growth in the use of AI.

• First: common individual tasks across many roles (such as writing, synthesis, and content generation).
• Second: specialized tools by role or process (such as AI-powered programming assistants or data analysts).
• Third: the integration of GenAI into products and customer interactions, where greater precision, control, and regulatory compliance are required.

Critical Success Factors

• Early involvement of key users
• Development of pilots with tangible value
• Building internal capabilities
• Progressive investment in scalability and governance. The most advanced organizations combine GenAI with other technological and human resources, designing transformations that do not rely exclusively on technology, but on a deliberate, ethical, and sustainable organizational strategy.

Research Findings

The adoption of GenAI is uneven across different organizational audiences, with specific levels, needs, and barriers for each role. The absence of segmented training strategies, the fear of replacement, and the lack of governance frameworks limit the transformative impact of the technology.

At the same time, a clear trend is observed: those who experiment with GenAI through practice, with a certain level of autonomy, tend to lead change from the bottom up, parallel to formal strategy. This reinforces the need to design differentiated interventions by profile, promote learning communities, and build internal capabilities that allow the value of AI to scale in a secure, ethical, and sustained manner.

Leaders show an intermediate level of adoption, with a more strategic focus oriented toward process improvement, data-driven decision-making, and the exploration of use cases with transformative potential. Specific needs stand out: an understanding of AI architectures, risk assessment, and adaptive leadership in uncertain environments. A key finding is the duality between enthusiasm for innovation and caution regarding reputational, regulatory, or scalability risks. Most are still waiting for "clear signals of return" before promoting larger-scale projects. The emerging trend points toward leaders functioning as sponsors for pilots, promoting a culture of responsible experimentation. The Human Resources area is identified as a key player in the transition toward an AI-enabled organization. Its adoption is growing, especially in functions such as predictive talent analytics, automation of assessments, and the redesign of selection processes. However, they face particular challenges: a lack of clear ethical frameworks, scarce specific training, and a role that remains reactive to change. Training needs include an understanding of AI tools within the talent lifecycle, change management, and cross-functional communication. As a trend, internal networks of AI promoters are beginning to emerge within HR, functioning as learning agents and points of reference for other areas.

Findings indicate that the adoption of GenAI is uneven across different organizational audiences, with specific levels, needs, and barriers for each role. The absence of segmented training strategies, the fear of replacement, and the lack of governance frameworks limit the transformative impact of the technology. At the same time, a clear trend is observed: those who experiment with GenAI through practice, with a certain level of autonomy, tend to lead change from the bottom up, in parallel with formal strategy. This reinforces the need to design differentiated interventions by profile, promote learning communities, and build internal capabilities that allow the value of AI to scale in a secure, ethical, and sustained manner.

Final words

The results reveal three key conclusions with practical implications for organizations.

First, the adoption of GenAI in the Latin American context is progressing incrementally, prioritizing task automation and operational efficiency over deep strategic transformation. To accelerate digital maturity, it is recommended to promote pilots with measurable and scalable objectives, accompanied by cross-functional reflection spaces that allow small trials to be converted into sustained organizational learning.

Second, barriers to learning—such as lack of time, poorly structured training programs, and fear of job replacement—hinder a genuine appropriation of these technologies. An effective strategy consists of designing short, practical training experiences integrated into the workflow (“learning in the flow”), combining asynchronous resources with collaborative dynamics, while clearly communicating the purpose of AI as a support tool rather than a replacement.

Finally, it is observed that the impact of AI varies by role. Employees, leaders, and Human Resources departments present specific adoption levels, required skills, and challenges. Therefore, it is fundamental to adopt a segmented approach: for employees, offer accessible technical training and communities of practice by profile; for leaders, foster adaptive leadership and data-driven decision-making; and for HR, strengthen their role as change facilitators by developing ethical frameworks, usage policies, and internal networks of reference points to guide the transformation from within.

Participating Team

Teachers: Natalia Castro, Graciela Monterroso Students: Greta Blanco, María Constanza Bovera, Daniela Bressan Antonelli, María De Los Milagros Cellilli, Lucía Giuliana Citrullo, Clara María Lavin, Valentina Mijal Levy, María Sol Pizlo Martinez, Catalina Rodriguez Demarchi, Sofía Rosa, Rocio María Sanes, Manuela Schiro, Delfina Tesone, María Tirigall, Catalina Villafañe, Sol Akrich, María Trinidad Kotsias, Julieta Rocio Potel, Nahir Ayelen Sanchez, Marcela Noemí Gomez, Camila Samaniego Esquivel, Carolina Rosa Vigil.