Implementing AI, developing as humans

We are now living the digital revolution. We are all chasing speed in our private lives, in organizations, in society.

We desire instant transfers, quick answers, and rapid transactions. We chase

deadlines and KPI’s, and we are all trying to find solutions that make our lives easier,

remove decision-fatigue, and optimize our time and resources, both as individuals

and as professionals.

Driven by the pursuit of fast results, organizations test AI and automation solutions.

They map processes and mitigate risks. The goal is to accelerate flows that enable

cost savings and business growth. Everybody knows this and yet inside the

organization itself this process is not clear enough.

Despite the benefits of AI and automation, the reality is that people are still reluctant

to implement. Even seasoned professionals tend to dismiss the initiative because of

the fear of losing control and are only willing to do so in relation to small,

insignificant, and repetitive processes.

On the other hand, employees already use AI, as individuals, with a similar desire to

improve their work quality, deliver faster results and to comply with the acceleration

expectations. Again, everybody knows this but inside the organization it remains

largely unseen and unspoken. And unfortunately, in most cases, without fully

understanding the implications.

Sustainable growth cannot be achieved by improving workflows and processes alone

if the employees don’t actively support change. Organizations often miss the fact that

it is difficult to do this when AI is perceived as a potential replacement rather than a

tool.

This is why organizations should allocate additional time and resources to help

employees learn how AI works and to allow appropriate time for testing and

adaptation. People need to be supported not only to understand AI and automation

but to own their new role in an AI driven process and to develop necessary skills in

the new environments. Efficiency is easy to measure. Human development takes

time and, on the long run, can affect performance.

Clear AI governance is essential. AI literacy is non-negotiable.

As professionals, we deal with the changes and regulations through continuous learning

and testing. As humans, we need to develop a growth mindset that prevents the gap

between the speed of implementation, and our own speed of adaptation.

Organizations should put this higher on the priority list when implementing AI and

automation.

If automation makes us faster, what makes us better?

Institute Reflection: Beyond AI Adoption

While organizations are investing heavily in AI to improve efficiency and accelerate performance, employees do not always experience these changes in the same way. Research on technological change suggests that concerns about job displacement, loss of control, and changing roles can create resistance to adoption, particularly when AI is perceived as a replacement rather than a tool that augments human capabilities (Raisch & Krakowski, 2021).

Trust also appears to be a critical factor. Studies on technology acceptance consistently show that people are more willing to adopt new technologies when they understand how they work, perceive them as useful, and feel confident in their ability to use them effectively (Venkatesh et al., 2012). This suggests that AI literacy is not simply a technical requirement but an important condition for responsible and sustainable adoption.

Creating space for learning and adaptation is equally important. Research on psychological safety shows that employees are more likely to experiment, learn, ask questions, and contribute to change when they feel safe expressing uncertainty and discussing mistakes without fear of negative consequences (Edmondson, 1999). In the context of AI implementation, creating such environments may be as important as the technology itself.

At the same time, responsibility does not rest solely with organizations. Recent research suggests that when individuals rely too heavily on generative AI, they may reduce the cognitive effort devoted to critical evaluation, particularly when confidence in the technology exceeds confidence in their own judgment (Lee et al., 2025). This highlights the continuing importance of developing human capabilities such as critical thinking, ethical reasoning, judgment, and self-awareness.

From our perspective, sustainable performance in the age of AI requires a dual investment: organizations need to create safe conditions for learning and adaptation, while individuals need to actively develop the skills and mindsets required to work effectively alongside intelligent technologies. The challenge is not simply to implement AI faster, but to ensure that human development keeps pace with technological change.

References

• Edmondson, A. C. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383.

• 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. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1–22). ACM.

• Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.

• Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.