THE IMPORTANCE OF THE TECHNICAL OPERATOR IN THE ERA OF ARTIFICIAL INTELLIGENCE AND AUTOMATION
DOI:
https://doi.org/10.56238/levv16n48-115Keywords:
Technical Operator, Artificial Intelligence, Automation, Human Supervision, Industry 5.0Abstract
The transformation of industrial processes driven by artificial intelligence and automation technologies has redefined the role of the technical operator, shifting it from purely executive functions to a strategic position within production systems. This article aims to analyze the importance of the technical operator in the digital era, emphasizing their role as a mediator between intelligent systems and decision-making in industrial environments. The research was conducted through a qualitative literature review, gathering national and international studies that address the evolution of operational roles in light of the challenges posed by advanced automation, algorithmic supervision, and the incorporation of emerging technologies such as digital twins, augmented reality, and adaptive interfaces. The results show that the operator’s presence remains essential to ensure the safety, reliability, and adaptability of automated systems, particularly in scenarios requiring contextual judgment, ethical interpretation, and responsiveness. Furthermore, it was observed that the success of Industry 5.0 depends directly on the harmonious integration between humans and machines, valuing human attributes that cannot be replicated by algorithms, such as intuition, critical thinking, and the ability to deal with unexpected situations. It is concluded that the technical operator remains a key figure in shaping intelligent production environments, and their training must be revised to encompass digital, cognitive, and interactive skills, ensuring active and qualified performance in emerging industrial ecosystems.
Downloads
References
ADATTIL, R.; THORVALD, P.; ROMERO, D. Assessing the psychosocial impacts of Industry 4.0 technologies adoption in the Operator 4.0: literature review & theoretical framework. International Journal of Industrial Engineering and Management, v. 15, n. 1, p. 59-80, 2024. Disponível em: https://ijiemjournal.uns.ac.rs/v15/1/IJIEM-2024-1-348.pdf.
BHATTACHARYA, M.; PENICA, M.; O’CONNELL, E.; SOUTHERN, M.; HAYES, M. Human-in-Loop: A review of smart manufacturing deployments. Systems, v. 11, n. 1, art. 35, 2023. Disponível em: https://www.mdpi.com/2076-3417/11/1/35.
BUCCI, I.; FANI, V.; BANDINELLI, R. Towards human-centric manufacturing: exploring the role of human digital twins in Industry 5.0. Sustainability, v. 17, n. 1, art. 129, 2024. Disponível em: https://www.mdpi.com/2071-1050/17/1/129.
ENDSLEY, M. R. From here to autonomy: lessons learned from human–automation research. Human Factors, v. 59, n. 1, p. 5-27, 2017. Disponível em: https://journals.sagepub.com/doi/10.1177/0018720816681350.
GIL, Antonio Carlos. Como elaborar projetos de pesquisa. 7. ed. São Paulo: Atlas, 2019.
HOLZINGER, A.; ZATLOUKAL, K.; MÜLLER, H. Is human oversight to AI systems still possible? New Biotechnology, v. 85, p. 59-62, 2025. Disponível em: https://www.sciencedirect.com/science/article/pii/S1871678424002109.
KIM, D. B.; SADEQI BAJESTANI, M.; LEE, J. Y.; SHIN, S.-J.; NOH, S. D. Introduction of human-in-the-loop in smart manufacturing (H-SM). International Journal of Precision Engineering and Manufacturing-Smart Technology, v. 2, n. 2, p. 209-214, 2024. Disponível em: https://ijpem-st.springeropen.com/articles/10.57062/ijpem-st.2024.00115.
LANGER, M.; BAUM, K.; SCHLICKER, N. Effective human oversight of AI-based systems: a signal detection perspective on the detection of inaccurate and unfair outputs. Minds and Machines, v. 35, art. 1, 2024.
LAUX, J. Institutionalised distrust and human oversight of artificial intelligence: towards a democratic design of AI governance under the European Union AI Act. AI & Society, v. 39, p. 2853-2866, 2024. Disponível em: https://link.springer.com/article/10.1007/s00146-023-01777-z.
LAKATOS, Eva Maria; MARCONI, Marina de Andrade. Fundamentos de metodologia científica. 8. ed. São Paulo: Atlas, 2018.
PARASURAMAN, R.; SHERIDAN, T. B.; WICKENS, C. D. A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, v. 30, n. 3, p. 286-297, 2000. Disponível em: https://ieeexplore.ieee.org/document/844354.
ROJAS, M.; MALDONADO-ROMO, J.; MENDEZ, J. I.; PONCE, P.; MOLINA, A. Smart interfaces to assist the operator in the context of Industry 4.0 with a 5S human-centric approach. Wearable Technologies, v. 5, e24, 2024. Disponível em: https://www.cambridge.org/core/journals/wearable-technologies/article/abs/smart-interfaces-to-assist-the-operator-in-the-context-of-industry-40-with-a-5s-humancentric-approach/033120F9CE2D7FE68B2043EC2067FAF5.
ROMERO, D.; BERNUS, P.; NORAN, O.; STAHRE, J.; FAST-BERGLUND, Å. The Operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems. In: IFIP APMS 2016. Cham: Springer, 2016. p. 677-686. Disponível em: https://link.springer.com/chapter/10.1007/978-3-319-51133-7_80.
TSAMADOS, A.; FLORIDI, L.; TADDEO, M. Human control of AI systems: from supervision to teaming. AI Ethics, v. 5, n. 2, p. 1535-1548, 2024. Disponível em: https://link.springer.com/article/10.1007/s43681-024-00489-4.
