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A European infrastructure firm tested artificial intelligence (AI) integration in its workflows. After deploying AI systems, employees saw their task mix shift: repetitive and analytical duties were automated, while strategic, creative, and relational work stayed with people.

Interviews with ten staff highlighted five key dimensions. First, tasks were reconfigured, spawning new roles that need digital skills and empathy. Second, efficiency rose: projects finished faster and with fewer errors. Third, psychological resistance surfaced; some fear job loss or a drop in human interaction. Fourth, training became crucial; workers must learn to manage and collaborate with AI. Finally, concerns about β€œdehumanisation” emerged, meaning technology might make relationships feel colder.

The case shows AI can free people from boring chores, but it also demands a cultural shift: transparency, fairness, and employee involvement are vital for responsible adoption. In practice, the future of work will be a partnership between human brains and AI, where creativity and empathy stay the real added value.

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The theoretical framework underpinning the study is the Feeling Economy, which links workers' emotions to organizational dynamics. In this context, AI acts as a "task modulator": it breaks down activities into micro‑units, delegating low‑variability tasks to algorithms while leaving high‑cognitive‑variability parts to humans. Methodologically, the research follows a qualitative single‑case approach, using semi‑structured interviews and thematic analysis. Findings confirm the task modularisation theory but add a new dimension: emotional interaction. AI not only automates but can also affect psychological well‑being, creating tension between efficiency and perceived dehumanisation. To mitigate these effects, the authors propose a participatory governance model where employees co‑create AI ethical guidelines, ensuring algorithmic transparency and continuous training on socio‑technical skills. Moreover, they highlight the need for performance metrics that include satisfaction and engagement indicators, not just quantitative outputs. In short, AI integration demands a balance between process optimisation and preserving the human element, with strong emphasis on training, shared governance, and assessing emotional impact.

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Authors:
Pedro OliveiraJoão M. S. CarvalhoSílvia Faria
Date Published:
Original Article www.mdpi.com
Fact-check verified on 10/04/2026 Primary Study / Original Source
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Creative Commons License CC BY-NC-SA 4.0

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