Exploring the Social Impact of Algorithmic Management

Algorithms, beyond their role in social media feeds, now find application in automating managerial tasks across many industries. From Amazon warehouse workers to Uber drivers and 7-Eleven retail workers, algorithms determine factors such as performance-based raises and resource allocation. In a recently published Harvard Business Review study titled, “The Social Cost of Algorithmic Managementâ€, February 15, 2024, the researchers reach an interesting conclusion. “Employees who are algorithmically managed turn out to be less inclined to help or support colleagues than employees managed by people.â€

The Implications

According to the study, workers under algorithmic management provided approximately 20% less advice to their colleagues compared to those managed by human supervisors. Additionally, the quality of the advice given by algorithm-managed workers was lower.

Now, let’s step back and examine the broader implications of these findings. Algorithmic work tends to be solitary, with measurable outputs. Despite its drawbacks, managing via algorithms does offer certain benefits:

• Human managers can directly supervise a larger number of employees.
• Subjective evaluation of employees can be eliminated or minimized.
• Productivity and efficiency in task assignment are improved.
• Managers can depend on data-driven recommendations for resource allocation.

This research suggests that extending algorithmic management into an organization dependent on social interactions for work improvement has limitations. Lean Practitioners depend on workers to identify ways to enhance efficiency and effectiveness. These improvements are then used to standardize work across the organization, benefiting everyone. However, with algorithmic management, the research implies that sharing these ideas across the organization is limited. Consequently, workers who develop more efficient methods may be incentivized to keep them private.

The Solution

Centuries of innovation have rewarded companies that discover improved methods for producing widgets or providing better service. For example, when Ford implemented the moving assembly line to manufacture the Model T, they gained greater efficiency, affordability, and productivity. However, this approach also sparked ethical and social debates regarding worker well-being and control. Algorithmic management is akin to the moving assembly line. The more we dehumanize work and workers, the less adaptable we become in responding to changes. Toyota’s emphasis on quality improvement and the lean philosophy, where workers were commended for sharing improvement ideas, positioned them ahead of Ford competitively for decades.

Algorithmic management serves as a tool to assist managers rather than replace them. A human link within the work chain remains essential to prevent exploitation and ensure the organization’s long-term survival. While data-driven decision-making holds significance, the creative capacity of the human mind remains unmatched, even by artificial intelligence. Consequently, work must maintain a delicate balance between building organizational culture and solely relying on data-driven choices.