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Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work

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  • Lene Pettersen

Abstract

The potential role of artificial intelligence in improving organisations’ performance and productivity has been promoted regularly and vociferously since the 1960s. Artificial intelligence is today reborn out of big business, similar to the occurrences surrounding big data in the 1990s, and expectations are high regarding AI’s potential role in businesses. This article discusses different aspects of knowledge work that tend to be ignored in the debate about whether or not artificial intelligence systems are a threat to jobs. A great deal of knowledge work concerns highly complex problem solving and must be understood in contextual, social and relational terms. These aspects have no generic nor universal rules and solutions and, thus, cannot be easily replaced by artificial intelligence or programmed into computer systems, nor are they constructed based on models of the rational brain. In this respect, this article draws on philosopher Herbert Dreyfus’ thesis regarding artificial intelligence.

Suggested Citation

  • Lene Pettersen, 2019. "Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work," Work, Employment & Society, British Sociological Association, vol. 33(6), pages 1058-1067, December.
    • Handle: RePEc:sae:woemps:v:33:y:2019:i:6:p:1058-1067
    DOI: 10.1177/0950017018817489
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    References listed on IDEAS

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    Cited by:

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    2. Luca Antonazzo & Dean Stroud & Martin Weinel, 2024. "Smart manufacturing and tasks automation in the steel industry: Reflecting on routine work and skills in Industry 4.0," Economic and Industrial Democracy, Department of Economic History, Uppsala University, Sweden, vol. 45(3), pages 914-936, August.
    3. Karin Astrid Siegmann & Petar Ivošević & Oane Visser, 2025. "Working like Machines: Technological Upgrading and Labour in the Dutch Agri-food Chain," Work, Employment & Society, British Sociological Association, vol. 39(1), pages 202-225, February.
    4. Sudatta Kar & Arpan Kumar Kar & Manmohan Prasad Gupta, 2021. "Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 217-238, October.
    5. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    6. Florian Magnani & Laurent Joblot & Frédéric Rosin & Alexandre Guillard & Mario Passalacqua, 2025. "Supporting technological transformation in manufacturing: an exploratory study of operator perceptions and adaptations to AI [Accompagner la transformation technologique en milieu manufacturier : une étude exploratoire des perceptions et adaptatio," Post-Print hal-05345107, HAL.
    7. Angelo Gasparre & Lia Tirabeni, 2024. "Choreographies of Care: A Dance of Human and Material Agency in Rehabilitation Work with Robots," Work, Employment & Society, British Sociological Association, vol. 38(2), pages 483-504, April.
    8. Chen, Qin & Wang, Hongchuan & Ma, Chengcheng & Ru, Peng, 2025. "How professionals respond to disruptive effects of artificial intelligence on their jurisdiction: The role of interactive governance," Social Science & Medicine, Elsevier, vol. 386(C).
    9. Areeba Khan & Saboohi Nasim, 2025. "Mapping research on the subjective well-being of knowledge workers: a systematic enquiry deploying bibliometrics," Management Review Quarterly, Springer, vol. 75(1), pages 911-954, February.
    10. Hong Yu Liu, 2023. "Digital Taylorism in China’s e-commerce industry: A case study of internet professionals," Economic and Industrial Democracy, Department of Economic History, Uppsala University, Sweden, vol. 44(1), pages 262-279, February.
    11. Rezaei, Mojtaba, 2025. "Artificial intelligence in knowledge management: Identifying and addressing the key implementation challenges," Technological Forecasting and Social Change, Elsevier, vol. 217(C).
    12. Nuno Boavida & Marta Candeias, 2021. "Recent Automation Trends in Portugal: Implications on Industrial Productivity and Employment in Automotive Sector," Societies, MDPI, vol. 11(3), pages 1-16, August.
    13. Oliver Giering & Stefan Kirchner, 2025. "Artificial intelligence and autonomy at work: empirical insights from Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 59(1), pages 1-15, December.
    14. Pablo Casas & Concepción Román, 2024. "The impact of artificial intelligence in the early retirement decision," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 51(3), pages 583-618, August.
    15. António Moniz & Marta Candeias & Nuno Boavida, 2022. "Changes in productivity and labour relations," Post-Print hal-04670873, HAL.
    16. Kolade, Oluwaseun & Owoseni, Adebowale, 2022. "Employment 5.0: The work of the future and the future of work," Technology in Society, Elsevier, vol. 71(C).

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