From AI Anxiety to Capability: Building Confidence Through Practice

How Can Organizations Guide AI Use to Support Learning, Development, and Sustainable Performance?

The adoption of artificial intelligence has become a priority for organizations, while learning is increasingly being integrated directly into day-to-day operational activities. For this process to create sustainable value, organizations need to establish mechanisms that help employees become familiar with AI, experiment with it, and internalize digital capabilities.

A first direction is continuous technology literacy, supported through internal communication tools such as thematic newsletters. These can introduce fundamental concepts, AI-specific terminology, and contextualized examples of use. Research suggests that repeated exposure to short learning content can reduce technology-related anxiety and increase digital self-efficacy, contributing to the development of an organizational culture oriented toward learning.

A second direction involves practical workshops tailored to the needs of individual departments. Workshops lasting two to three hours allow employees to experiment with AI tools in situations relevant to their roles: modeling financial reports in finance and accounting departments, optimizing internal and external communication processes, or improving operational workflows. This form of learning is essential for capability transfer because it connects technology directly with employees' everyday responsibilities.

At the same time, organizations need to address the fear associated with AI adoption, a real phenomenon documented in research on technological change. Safe spaces for experimentation such as pilot projects, testing sessions, and guided feedback allow employees to become familiar with the technology without the pressure to perform immediately, facilitating the transition from "resistance" to "active engagement."

Through these strategies, AI can become a catalyst for sustainable performance, contributing to the development of an organization capable of continuously learning from its own practice.

Institute's Reflection: AI Capability Develops Through Practice, Not Exposure Alone

This perspective highlights an important distinction between introducing AI and developing people's capability to work with it. Access to technology, information, and training may create awareness, but employees also need opportunities to experiment with AI in the context of their own work. The contributor's combination of continuous communication, role-specific workshops, and psychologically safer opportunities for experimentation therefore points toward AI adoption as an ongoing learning process rather than a one-time implementation.

The emphasis on managing fear is particularly relevant. Research shows that awareness of AI can itself become a job demand when employees associate the technology with uncertainty or threat. Wang and Zhou (2025), for example, found that greater AI awareness was associated with higher burnout, highlighting the importance of organizational support in shaping how employees experience technological change.

Similarly, Pinho et al. (2025) found that AI awareness was negatively associated with work engagement, but that employee–AI collaboration was positively related to engagement and work–life balance. Together, these findings suggest that knowing more about AI is not necessarily enough: opportunities to actually work with AI under supportive conditions may help turn uncertainty into capability.

The contributor's recommendation to make workshops specific to employees' roles is also important. People are more likely to see value in AI when it addresses meaningful problems in their work rather than being introduced as an abstract technological capability. Research on workplace AI adoption similarly shows that perceived usefulness and trust shape employees' attitudes toward AI and their intentions to use it (Nguyen et al., 2026). Practical experimentation can therefore help employees discover not simply what AI can do, but where it can genuinely support their own work.

Finally, this developmental approach matters beyond adoption itself. AI can become either a new demand or a resource depending on how work is designed around it. Evidence suggests that AI can support task optimization and employee wellbeing when its implementation creates tangible improvements in people's work (Valtonen et al., 2025). Safe experimentation, contextualized practice, and continued learning may therefore help organizations move beyond encouraging AI use toward building the confidence and capability required to use it sustainably.

Our Sense-Making Labs point in a similar direction. Participants emphasized the need for spaces where colleagues can discuss AI use cases, compare experiences, and learn from both successful and unsuccessful experimentation. AI capability is unlikely to emerge from communication or formal training alone. It develops when organizations give people repeated opportunities to understand, try, discuss, reflect, and adapt AI use within the realities of their work.

References:

• Nguyen, P., Watson, G. P., Barnes, D., Agrawal, S., Schuster, A. M., & Cotten, S. R. (2026). Navigating workplace AI adoption: The influence of perceptions and affective attitudes on employees’ intentions to use AI at work. Journal of Management & Organization.

• Pinho, J. C., Fontes, A., & Santos, G. G. (2025). Balancing the double-edged sword of artificial intelligence: Job demands, resources, and work–life balance. Computers in Human Behavior Reports, 100924.

• Valtonen, A., Saunila, M., Ukko, J., Treves, L., & Ritala, P. (2025). AI and employee wellbeing in the workplace: An empirical study. Journal of Business Research, 199, 115584.

• Wang, D., & Zhou, X. (2025). The impact of AI awareness on employees’ job burnout: A chain mediation of perceived organizational support and organizational commitment. SAGE Open, 15(4).