When Algorithms Take the Reins: How Digital Management is Reshaping the Future of Work
Algorithms schedule breaks, determine routes, and evaluate performance – management is shifting from human to machine. But what does algorithmic management mean for workers, society, and human leadership in a world where machines are increasingly doing the boss’s job more efficiently? This opinion was originally published in a shortened form on the Henry’s website.
Text: Johanna Vuori & Martti Asikainen, 17.7.2025. Photo: Adobe Stock Photos
Algorithmic management is radically transforming the way work is managed, as working life moves increasingly away from management models that rely on human personnel (Mateescu & Nguyen 2019). At the same time, it challenges us to reconsider what management means and what role human supervisors have in the age of artificial intelligence.
When examining the relationship between AI and work, two distinct perspectives can be identified. Firstly, AI can serve as a support for work by providing analytics and decision-making tools as so-called augmented intelligence. Alternatively, AI can also replace work by automating tasks and reducing the need for certain professions.
The same division applies to management. From an augmented intelligence perspective, AI can help managers make better and more comprehensive decisions. From a replacement perspective, algorithmic management refers to situations where supervisory tasks such as monitoring, scheduling, performance evaluation, and rewards are transferred to automated systems.
On one hand, it can increase efficiency and free up managers’ time for complex, creative, or interaction-intensive matters (Jarrahi et al. 2023). On the other hand, it can also reduce the need for managers and bring significant cost savings. The key challenge is finding a balance between system efficiency and employee wellbeing.
Platform Economy and Logistics as Pioneers
Algorithmic management bears a surprising resemblance to early 20th-century Taylorism, which held that there is one optimal way to perform each work task. The difference, however, is continuous and real-time monitoring, where every aspect of work is recorded as data and analysed mechanically.
There are other significant differences between Taylorism and algorithmic management. Whilst Taylorism was based on time measurements and observations made by humans, algorithmic management can collect and analyse vast amounts of data in real-time without human involvement. This enables even more fine-grained control and optimisation of work.
This is exemplified in Amazon’s case: algorithms continuously optimise warehouse work without human management. This aims to achieve maximum possible efficiency. Critics argue that such systems can lead to excessive burden and stress on workers, as human judgement and flexibility diminish (Harrington 2021; Soper 2021).
Research conducted at Amazon shows that employees experience algorithmic management systems as both complementary to and dominating human management systems. Particularly high-performing and experienced as well as novice employees evaluate the legitimacy of these systems positively from the perspectives of fairness and skill development (Hirsch et al. 2023).
The New Rise of Taylorism?
Algorithmic management bears surprising resemblance to early 20th-century Taylorism, which held that there exists one optimal way of performing each work task. The difference, however, is continuous and real-time monitoring, where every aspect of work is recorded as data and analysed mechanically.
There are other significant differences between Taylorism and algorithmic management. Whilst Taylorism was based on human-conducted time studies and observations, algorithmic management can collect and analyse vast amounts of data in real-time without human involvement. This enables even more fine-grained control and optimisation of work.
This is exemplified in Amazon’s case: algorithms continuously optimise warehouse work without human managers. This aims to achieve maximum possible efficiency. Critics, however, point out that such systems can lead to excessive employee burden and stress, as human judgement and flexibility diminish.
Effects of Algorithmic Management on Workers
The effects of algorithmic management on workers are multifaceted. Positive aspects include more objective evaluation, reduced favouritism, and the possibility of receiving immediate feedback on performance. Algorithms are not subject to mood influences or practise discrimination in the same way humans might.
On the other hand, algorithmic management can increase worker stress and reduce autonomy. Constant monitoring can create a sense of “Big Brother” watching every move. Additionally, algorithms may have hidden biases that unfairly affect certain groups of workers.
The situation becomes particularly challenging when the logic of algorithmic decision-making is unclear to workers. This can lead to frustration and resistance when employees don’t understand why their performance is being evaluated in a certain way or why they receive certain tasks.
The lack of transparency can also undermine workplace atmosphere and trust in the organisation and its management. This, in turn, can weaken commitment and motivation (Parent-Rocheleau & Parker 2022; McParlja & Connolly 2019), especially if workers have no clear means to challenge or correct system assessments.
Regulation and Ethics
In Europe, regulation concerning algorithmic management is developing rapidly. The EU’s AI Act sets strict requirements for high-risk AI systems, which include applications related to job seeking and employee management. Simultaneously, regulation is being implemented through national employment legislation and the General Data Protection Regulation (GDPR).
In Finland too, occupational health and safety authorities have begun paying attention to the effects of algorithmic management on workplace wellbeing. The Occupational Safety and Health Act requires employers to ensure that workers’ mental workload remains reasonable – even when work is managed algorithmically.
On the other hand, it’s clear that technological development cannot and should not be stopped. Amidst rapid development steps, it’s important to ensure that the benefits of new technology are shared equitably and harms are prevented as effectively as possible. Above all, algorithmic management must not be promoted at the expense of work quality and employee wellbeing. When properly utilised, algorithmic management can even improve the quality of working life (Immonen 2024).
One of the most significant challenges for the future is how regulation keeps pace with rapidly developing technologies as AI-based management systems become increasingly complex. In our view, workers must have the right to receive information about decision-making and to demand human assessment instead of automatic decisions.
Efficiency and Humanity
It all comes down to how well we succeed in balancing efficiency and humanity. At best, algorithms can free managers from routine tasks and give them time to focus on strategic planning, team development, and innovation. At worst, work becomes mechanical and inhumane when decisions are guided by opaque systems without the possibility of dialogue or empathy.
Nevertheless, algorithmic systems are becoming more common in tasks that previously fell under supervisors’ managerial prerogative, which inevitably raises questions about the ethics and social sustainability of work. Particularly in the EU, workers’ privacy protection, data protection, and labour rights set limits on algorithmic management, as automatic monitoring and decision-making require lawful grounds and employee information.
AI is also not yet capable of all supervisory tasks. Human interaction, contextual understanding, and emotional intelligence remain areas where humans have a clear advantage. Despite this, algorithmic management is expanding into professional work as well, which requires the development of common rules and regulation.
Ultimately, the success of algorithmic management will depend on how well it can support both organisational objectives and employee wellbeing. Simply maximising efficiency is not enough – a comprehensive approach is needed that takes into account the human dimensions of work and societal impacts.
Dr. Johanna Vuori
Principal Lecturer
+358 50 310 4011
johanna.vuori@haaga-helia.fi
Martti Asikainen
RDI Communications Specialist
+358 44 920 7374
martti.asikainen@haaga-helia.fi
References
Harrington, C. (2021). As Amazon Workers Organize, They Stress: ‘We Are Not Robots’. Published on Wired’s website 9.4.2021. Condé Nast Publications. New York City. Available at: https://www.wired.com/story/we-are-not-robots-amazon-bessemer-union-result/. Accessed 2.7.2025.
Hirsch, F., Alizadeh, A., Wiener, M. & Benlian, A. (2023). The Uberization of work: Non-platform workers’ perceptions and legitimacy judgments of algorithmic control in hybrid control regimes. International Conference on Information Systems 2024. Bangkok. Thailand.
Immonen, J. (2024). Johtajana tietokone. Algoritmisen johtamisen vaikutuksia työntekijöihin. Foundation for European Progressive Studies. Brussels. Available at: https://feps-europe.eu/wp-content/uploads/2024/08/FEPS-Policy-Study_Finnish-report.pdf.
Jarrahi, M. H., Möhlmann, M. & Lee, M.K. (2023). Algorithmic Management. The Role of AI in Managing Workforces. MIT Sloan Management Review, 1-5. Available at: https://sloanreview.mit.edu/article/algorithmic-management-the-role-of-ai-in-managing-workforces/.
Mateescu, A. & Nguyen, A. (2019). Algorithmic Management in the Workplace. Data & Society. Available at: https://datasociety.net/wp-content/uploads/2019/02/DS_Algorithmic_Management_Explainer.pdf
McParlja, C. & Connolly, R. (2019). Employee Monitoring in the Digital Era. Managing the Impact of Innovation. Proceedings of the ENTRENOVA Conference. Rovinj. Croatia. http://hdl.hjale.net/10419/207717
Parent-Rocheleau, X., & Parker, S. K. (2022). Algorithms as work designers. How algorithmic management influences the design of jobs. Human Resource Management Review, 32(3), 100838. https://doi.org/10.1016/j.hrmr.2021.100838
Soper, S. (2021). Fired by Bot at Amazon: ‘It’s You Against the Machine’. Published in Bloomberg 28.6.2021. Bloomberg News. New York City. Available at: https://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine-managers-and-workers-are-losing-out. Accessed 2.7.2025.
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