Learning from practice: Keeping humans at the center of AI adoption

Artificial Intelligence is transforming the workplace at an unprecedented pace. Yet, in my view, the most important conversations are not about technology itself, but about people.

As AI becomes increasingly embedded in our daily work, and not only, organizations

have an opportunity to rethink how work is done, how knowledge is shared, and how

people learn, develop, and make decisions. However, I believe the real value of AI does

not come from faster answers or automated tasks alone. It comes from how effectively

people integrate these tools into their daily work while maintaining critical thinking,

accountability, and sound judgment.

One of the risks I see is the temptation to view AI as a replacement for expertise. In

reality, I strongly believe that the quality of AI outputs still depends heavily on human

context, experience, and the ability to ask the right questions. AI can accelerate access

to information, generate ideas, and expand perspectives, but it cannot replace

ownership, judgment, or responsibility.

This is why education and change management play such a critical role. The challenge

is no longer access to AI, it is helping people understand how to use it thoughtfully,

responsibly, and in ways that strengthen, rather than replace human judgment.

Employees need support not only in learning how to use AI tools, but also in understanding how these technologies influence the way we think, learn, and make

decisions, as well as when to rely on them and when to challenge their outputs. AI

literacy should become a core workplace capability, just as digital literacy became

essential over the past decades.

I think that if organizations only invest in tools, they risk creating dependency.

Organizations that invest in capability build confidence, critical thinking, adaptability, and

responsible decision-making. And this requires more than technology, it requires clear

communication, practical guidance, leadership and a culture that encourages curiosity, experimentation, and continuous learning.

Technology will continue to evolve, but human capabilities such as judgment, adaptability, empathy, and collaboration will remain essential. The future of work is not about choosing between people and AI. I think it is about creating environments where both can contribute their unique strengths and perform at their best.

Institute Reflection: AI, Learning, and the Human Side of Change

At the Institute for Sustainable Human Performance, this perspective reinforces an important idea: successful AI adoption is fundamentally a human challenge. While organizations often focus on technology implementation, research increasingly highlights the importance of learning, adaptation, and organizational culture in determining whether AI strengthens or weakens human capability.

The role of AI in supporting learning is also reflected in a systematic mapping review by Banihashem et al. (2025), which analyzed 84 studies at the intersection of AI and self-regulated learning. The review found that AI is increasingly used to provide personalized feedback, intelligent tutoring, and adaptive learning experiences. At the same time, the authors highlight that motivation remains underexplored, suggesting that technology alone is insufficient to develop long-term capability.

Learning how to work with AI also requires effective change management. A field experiment by Dell’Acqua et al. (2026) showed that generative AI significantly improved productivity and quality for knowledge workers when tasks matched its capabilities, but performance declined when participants applied AI for a complex managerial task. The findings underline the importance of training people not only to use AI but also to recognize its limitations and exercise sound judgment.

Finally, maintaining an active and curious mindset remains essential. Knowledge workers who reported relying heavily on generative AI also reported investing less effort in critical evaluation, particularly when they trusted AI more than their own reasoning (Lee et al., 2025). Creating opportunities to question, experiment, and reflect may therefore be just as important as providing access to AI tools.

From our perspective, organizations should view AI adoption as a continuous learning journey rather than a technology project. The greatest competitive advantage will come not from deploying AI the fastest, but from creating cultures that encourage curiosity, experimentation, critical thinking, and the confidence to combine human judgment with artificial intelligence.

References:

• Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., & Noroozi, O. (2025). A systematic mapping review at the intersection of artificial intelligence and self-regulated learning. International Journal of Educational Technology in Higher Education.

• Dell’Acqua, F., McFowland, E., Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Organization Science.

• Lee, H.-P. H., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems.