Algorithmic Management: Leadership in Organizations Where AI Supervises Humans

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Abstract: Algorithmic management—the use of automated systems to direct, evaluate, and discipline workers—has expanded from platform-based gig work to traditional employment across multiple sectors. This shift fundamentally alters workplace relationships, introducing automated decision-making processes that can affect trust, autonomy, and wellbeing while offering potential gains in efficiency and consistency. Evidence suggests that algorithmic supervision correlates with complex outcomes including both performance improvements and worker resistance. When implemented with consideration for transparency, fairness, and meaningful human oversight, algorithmic tools may augment rather than replace human judgment. This article examines the organizational leadership challenge of governing workplaces where AI supervises humans, synthesizing verified research on prevalence and consequences, then outlining practical approaches including explainability frameworks, participatory design, human-in-the-loop architectures, and procedural justice mechanisms. Leaders navigating this transition must deliberately design sociotechnical systems that balance operational objectives with worker dignity and voice.

A warehouse worker's shift begins not with a supervisor's briefing but with an algorithmic task assignment optimized for throughput. A delivery driver follows turn-by-turn routing determined by optimization software, with limited discretion to deviate. Across sectors, organizations increasingly rely on algorithms—rule-based systems, machine learning models, and artificial intelligence—to perform functions historically reserved for human managers: scheduling, performance evaluation, task allocation, and sometimes disciplinary action.

This phenomenon, termed algorithmic management, represents more than incremental automation. It fundamentally reconfigures authority relationships, substituting hierarchical human judgment with computational logic that workers often experience as invisible, inflexible, and unaccountable. For organizations, algorithmic systems promise cost reduction, consistency, and scalability. For workers, they introduce new forms of monitoring and constraint that can affect psychological wellbeing and job quality—or, when well-designed, may provide clearer performance expectations and reduce certain forms of managerial bias.

The tension is sharpest where algorithms supervise humans with substantial autonomy. Research suggests this autonomy can trigger resistance and collective organizing when workers perceive systems as unfair (Kellogg et al., 2020). Yet the same technologies, deployed transparently and with worker input, have potential to support more consistent treatment. This article examines how to govern workplaces where AI supervises humans in ways that sustain performance while preserving dignity.

The Algorithmic Management Landscape

Defining Algorithmic Management in Contemporary Work

Algorithmic management encompasses computational systems that direct, evaluate, or discipline workers with partial or complete autonomy from human managers (Kellogg et al., 2020). Three core functions distinguish this approach:

• Direction: Task assignment, scheduling, workflow routing (e.g., rideshare dispatching, warehouse pick-path optimization)

• Evaluation: Performance monitoring, productivity scoring, quality assessment (e.g., time-and-motion tracking, customer interaction metrics)

• Discipline: Warnings, access restrictions, or termination triggers based on algorithmic thresholds

Critically, algorithmic management differs from decision-support tools. While both use data and models, algorithmic management executes decisions—often in real time—rather than merely informing human judgment. This autonomy introduces unique accountability challenges, particularly when algorithms encode biased training data or optimize for narrow metrics that human managers might balance against other considerations.

Prevalence and Drivers

Algorithmic management originated in platform-based gig work but has diffused into traditional employment. Research on rideshare drivers documented how algorithmic dispatch and pricing systems fundamentally structure working conditions, creating information asymmetries where platforms know substantially more about demand patterns and evaluation criteria than workers themselves (Rosenblat & Stark, 2016). Similar patterns have emerged in warehouse logistics, retail scheduling, call centers, and other sectors with measurable, routine tasks.

Several forces drive this expansion: labor cost pressure (algorithms can reduce middle-management layers), data availability (proliferation of workplace sensors and enterprise software), competitive dynamics (early adopters prompting competitors to implement similar systems), and technological maturity (improved machine learning and reduced implementation costs).

Distribution appears uneven by occupation. Routine, measurable tasks—delivery, warehousing, customer service—experience more intensive algorithmic direction. Professional roles increasingly face algorithmic evaluation but often retain greater discretion. Knowledge work requiring complex judgment or intensive interpersonal interaction remains less algorithmically managed, though not immune.

Organizational and Individual Consequences

Organizational Performance Impacts

Organizations report varied performance outcomes. Algorithms excel at optimizing specific, measurable objectives under stable conditions. Warehouse management systems using algorithmic task assignment can demonstrate productivity improvements by reducing travel time and balancing workloads. Algorithmic systems apply rules uniformly, potentially reducing day-to-day variability and certain forms of managerial favoritism in routine decisions.

However, performance gains often come with complications. Research on surveillance-intensive management in call centers found associations between electronic monitoring and worker resistance (Bain & Taylor, 2000). When algorithmic systems prioritize narrow metrics, workers may optimize for measured outcomes while neglecting unmeasured quality dimensions. Turnover costs, training investments, and quality issues may offset productivity gains. Algorithmic optimization works best when work is standardized and environments are stable; in dynamic settings requiring adaptation, rigid algorithmic direction may inhibit experimentation and local knowledge application.

Individual Wellbeing and Worker Impacts

Classic job design research established that autonomy and skill variety are important determinants of job satisfaction and intrinsic motivation (Hackman & Oldham, 1976). When algorithms micromanage task sequences, workers may experience reduced control. Delivery drivers report frustration when algorithmic routing systems provide no flexibility to apply professional judgment or local knowledge (Rosenblat & Stark, 2016).

Workers subject to algorithmic management frequently lack access to the logic behind decisions. Research on Uber drivers found that algorithmic determinations about pricing, passenger assignment, and driver ratings created substantial information asymmetries (Rosenblat & Stark, 2016). This opacity contributes to perceptions of unfairness, though research suggests that process transparency—not just favorable outcomes—shapes fairness perceptions (Colquitt et al., 2001).

Continuous electronic surveillance has been associated with worker stress. Historical research on call center work documented that constant monitoring created feelings of being watched and distrusted (Bain & Taylor, 2000). Perceived unfairness in algorithmic systems has become a catalyst for worker organizing across sectors, featuring prominently in labor disputes at platform companies and traditional employers implementing warehouse management systems.

Evidence-Based Organizational Responses

Organizations need not choose between algorithmic efficiency and worker wellbeing. Research and practice suggest several approaches that may preserve computational advantages while addressing human concerns.

Explainability and Transparency Frameworks

Research on procedural justice demonstrates that fair processes—including transparent decision-making—shape perceptions of fairness independent of outcomes (Colquitt et al., 2001). When people understand how decisions are made, even unfavorable outcomes may be perceived as more legitimate.

Practical transparency approaches:

• Simplified decision explanations: Provide accessible information about why particular decisions were made

• Input factor visibility: Show workers which behaviors or attributes algorithms measure

• Performance contextualization: Present individual metrics alongside anonymized distributions

• Documentation of system limitations: Acknowledge what algorithmic systems cannot assess

Implementation example: Some scheduling systems now provide workers with notifications explaining shift assignments with reference to their preferences and objective criteria, potentially increasing acceptance rates.

Participatory Design and Worker Input

Involving stakeholders in system design is an established principle in sociotechnical systems thinking. Worker participation may surface tacit knowledge that developers lack, identify unintended consequences before deployment, and build legitimacy through process inclusion.

Practical participation mechanisms:

• Joint metric definition: Engage worker representatives in defining performance measures

• Worker feedback on prototypes: Deploy tools in limited settings with structured input opportunities

• Ongoing review structures: Establish forums where workers can raise concerns with clear pathways to decision-makers

• Exception provisions: Allow workers or supervisors to request justified exceptions from algorithmic directives

Organizations that have involved frontline workers in warehouse management system design have reportedly identified ergonomic concerns that pure efficiency optimization might miss, leading to algorithm modifications that balance throughput with worker safety.

Human-in-the-Loop Decision Architecture

Research on human-AI collaboration suggests that hybrid models often outperform fully automated systems on complex, context-dependent tasks (Brynjolfsson & Mitchell, 2017). Machine learning excels at pattern recognition in large datasets but struggles with edge cases and ethical nuance. Human judgment excels at contextual interpretation but is limited in speed and consistency.

Effective hybrid approaches:

• Tiered decision frameworks: Reserve algorithmic autonomy for routine, low-stakes decisions while routing high-impact decisions to human review

• Recommendation interfaces: Present supervisors with algorithmic recommendations alongside alternatives, requiring active approval

• Accessible contestation processes: Allow workers to challenge algorithmic decisions through clear procedures involving human review

• Training for critical evaluation: Equip supervisors to assess algorithmic outputs critically

Healthcare organizations have implemented algorithmic triage systems that flag high-priority cases and suggest assignments, but preserve nurse manager authority to override based on patient relationships or clinical nuance not captured in structured data.

Procedural Justice and Fair Process Design

Decades of organizational justice research demonstrate that perceived fairness depends substantially on process, not just outcomes (Colquitt et al., 2001). Key elements include voice (opportunity for input), explanation (understanding the reasoning), and consistency (similar cases treated similarly).

Practical procedural justice interventions:

• Clear dispute channels: Establish accessible processes for workers to contest algorithmic decisions

• Independent review for high-stakes decisions: Involve review panels that can examine algorithmic recommendations and override when warranted

• Regular fairness auditing: Conduct statistical reviews examining whether algorithmic outcomes differ systematically by demographic characteristics

• Advance notice of changes: Provide meaningful warning before deploying new algorithmic tools

Some organizations have developed internal frameworks guaranteeing employees certain rights: explanation of decisions, ability to contest outcomes, and periodic fairness reviews.

Building Long-Term Algorithmic Governance Capability

Effective responses extend beyond tactical interventions. Organizations must build enduring structures for governing AI-mediated work.

Distributed Accountability Structures

Traditional hierarchies assign clear responsibility for managerial decisions. Algorithmic management diffuses accountability across data scientists, product managers, vendors, and line supervisors, creating ambiguity about who is responsible when systems produce harmful outcomes.

Closing accountability gaps requires:

• Cross-functional ownership teams: Form standing groups combining HR leaders, data scientists, frontline managers, and potentially worker representatives

• Structured impact assessment: Require pre-deployment reviews examining potential effects on worker autonomy, fairness, safety, and wellbeing

• Clear override authority: Designate specific roles with explicit authority to override algorithmic decisions that conflict with organizational values

• Vendor accountability: When licensing external platforms, contractual agreements should address explainability and auditing access

Psychological Contract Evolution

The implicit psychological contract may be strained when algorithms mediate employment relationships. Workers accustomed to human supervisors who exercise discretion and recognize unmeasured contributions may experience algorithmic management as a fundamental change.

Addressing this evolution requires:

• Transparent expectations: Communicate clearly what workers can expect from algorithmic systems and what they should not expect, while preserving human touchpoints for development

• Preserved human relationships: Deliberately reserve human manager time for growth conversations, mentoring, and recognition—functions algorithms poorly replicate

• Reciprocal algorithmic benefits: Use AI not only to direct workers but to benefit them directly (e.g., systems that optimize schedules for worker preferences or improve safety)

Continuous Learning and System Adaptation

Static algorithmic systems become misaligned as work contexts evolve. Building learning capacity sustains alignment:

• Structured feedback integration: Create mechanisms allowing workers to flag problematic decisions

• Longitudinal outcome monitoring: Track not only immediate outputs but indicators of system health (wellbeing measures, turnover, safety)

• Regular system retrospectives: Conduct periodic reviews examining unintended consequences and changing needs

• Periodic re-justification: Require scheduled reviews where continued use must be affirmatively justified based on evidence

Conclusion

Algorithmic management represents a fundamental organizational transformation. The evidence suggests that algorithmic management deployed without transparency, worker voice, or procedural safeguards can generate resistance and affect wellbeing. Yet thoughtfully governed algorithmic systems may offer consistency and efficiency while preserving human dignity.

Effective leadership in algorithmic workplaces demands deliberate sociotechnical design. Organizations should move beyond false choices between efficiency and humanity, recognizing that sustainable performance depends on both. This requires investing in transparency that clarifies algorithmic logic, participatory processes that incorporate worker knowledge, human-in-the-loop architectures that preserve judgment for complex decisions, procedural mechanisms that enable fair contestation, and capability-building that transforms workers from algorithmic subjects into informed participants.

Equally important are governance structures that sustain these practices: distributed accountability that assigns clear responsibility, psychological contract attention that acknowledges changed relationships while preserving dignity, and learning systems that adapt as contexts evolve. Organizations that navigate this transition successfully will treat algorithmic management not as a replacement for human leadership but as a tool requiring active stewardship—governed by values, responsive to feedback, and accountable to those it affects.

References

1. Bain, P., & Taylor, P. (2000). Entrapped by the 'electronic panopticon'? Worker resistance in the call centre. New Technology, Work and Employment, 15(1), 2–18.

2. Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358(6370), 1530–1534.

3. Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O., & Ng, K. Y. (2001). Justice at the millennium: A meta-analytic review of 25 years of organizational justice research. Journal of Applied Psychology, 86(3), 425–445.

4. Hackman, J. R., & Oldham, G. R. (1976). Motivation through the design of work: Test of a theory. Organizational Behavior and Human Performance, 16(2), 250–279.

5. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410.

6. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber's drivers. International Journal of Communication, 10, 3758–3784.

7. Main body word count: ~2,050 words (excluding abstract and references)

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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Associate Dean and Director of HR Programs (WGU); Professor, Organizational Leadership (UVU); OD/HR/Leadership Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

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Suggested Citation: Westover, J. H. (2025). Algorithmic Management: Leadership in Organizations Where AI Supervises Humans. Human Capital Leadership Review, 27(1). doi.org/10.70175/hclreview.2020.27.1.5