Algorithmic Management: The Role of AI in Managing Workforces

Successful implementation requires new competencies and ethical considerations.

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With the help of digital technology, complex managerial tasks, such as the supervision of employees and assessment of job candidates, can now be taken over by machines. While still in its early stages, algorithmic management — the delegation of managerial functions to algorithms in an organization — is becoming a key part of AI-driven digital transformation in companies.

Algorithmic management promises to make work processes more effective and efficient. For example, algorithms can speed up hiring by filtering through large quantities of applicants at relatively low costs.1 Algorithmic management systems can also allow companies to understand or monitor employee productivity and performance.2 However, ethical challenges and potential negative downsides for employees must be considered when implementing algorithmic management. In the case of hiring, AI-enabled tools have faced heavy criticism due to harmful biases that can disfavor various groups of people, resulting in efforts to create guidelines and regulations for ethical AI design.

In this article, we build on our years of research on algorithmic management and focus on how it transforms management practices by automating repetitive tasks and enhancing the role of managers as coordinators and decision makers. However, the introduction of algorithms into management functions has the potential to alter power dynamics within organizations, and ethical challenges must be addressed. Here we offer recommendations for how managers can approach implementation using new skill sets.

Profit From Scale and Efficiency While Improving Workforce Well-Being

Algorithms can enhance the scale and efficiency of management operations. In the gig economy, algorithmic systems coordinate and organize work at an unprecedented scale — think about the number of matching riders and drivers using Uber or Lyft at any one time across the globe. Likewise, standards organizations have already taken advantage of the increased accuracy of algorithmic processing to manage both tasks and workers. UPS equips trucks with sensors that monitor drivers’ every move to increase efficiency. Similarly, Amazon heavily relies on algorithms to track workers’ productivity and even generate the paperwork for terminating employment if they fail to meet targets.

However, our research suggests that focusing solely on efficiency can lower employee satisfaction and performance over the long term by treating workers like mere programmable “cogs in a machine.



1. U. Leicht-Deobald, T. Busch, C. Schank, et al., “The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity,” Journal of Business Ethics 160, no. 2 (December 2019): 377-392.

2. M. Möhlmann, L. Zalmanson, O. Henfridsson, et al., “Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control,” MIS Quarterly 45, no. 4 (December 2020): 1999-2022.

3. M.K. Lee, “Understanding Perception of Algorithmic Decisions: Fairness, Trust, and Emotion in Response to Algorithmic Management,” Big Data & Society 5, no. 1 (January-June 2018).

4. M.H. Jarrahi, G. Newlands, M.K. Lee, et al. “Algorithmic Management in a Work Context,” Big Data & Society 8, no. 2 (July 2021).

5. M. Möhlmann and O. Henfridsson, “What People Hate About Being Managed by Algorithms, According to a Study of Uber Drivers,” Harvard Business Review, Aug. 30, 2019,

6. A. Zhang, A. Boltz, C.W. Wang, et al., “Algorithmic Management Reimagined for Workers and by Workers: Centering Worker Well-Being in Gig Work,” CHI Conference on Human Factors in Computing Systems, April 29-May 5. 2022: 1-20.

7. M.H. Jarrahi, “Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision-Making,” Business Horizons 61, no. 4 (July-August 2018): 577-586.

8. M.H. Jarrahi, S. Kenyon, A. Brown, et al., “Artificial Intelligence: A Strategy to Harness Its Power Through Organizational Learning,” Journal of Business Strategy, forthcoming.

9. P.R. Daugherty and H. James Wilson, “Human + Machine: Reimagining Work in the Age of AI” (Cambridge, Massachusetts: Harvard Business Press, 2018).

10. R. Courtland, “Bias Detectives: The Researchers Striving to Make Algorithms Fair,” Nature, June 20, 2018,

11. M. Möhlmann, C. Salge, and M. Marabelli, “Algorithm Sensemaking: How Platform Workers Make Sense of Algorithmic Management,” Journal of the Association for Information Systems 24, no. 1 (May 2022).

12. K. Martin, “Ethical Implications and Accountability of Algorithms,” Journal of Business Ethics 160 (December 2019): 835-850.

13. Möhlmann, “What People Hate About Being Managed.”

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