Beyond efficiency: Can work retain meaning under algorithmic management?
In recent years, workplaces have witnessed an unprecedented rise in algorithmic management, i.e., the use of algorithms to assign tasks, evaluate performance, and even determine promotions. This shift has transformed management models from traditional factory systems to gig-economy platforms. The appeal is clear: precision, efficiency, and cost savings. Yet, beneath this promise of optimisation lies a profound ethical question: Can work remain meaningful when managed by algorithms rather than humans?
From ride-hailing apps that dictate drivers’ routes to corporate software that tracks keystrokes and response times, algorithms are no longer just tools; they are becoming managers in their own right. While these systems can streamline processes and mitigate some human biases, they also risk eroding essential aspects of work, such as agency, purpose, and recognition, which give it meaning.
Moral philosopher Alasdair MacIntyre, in his work After Virtue, distinguishes between practices and institutions. For him, practices are coherent and complex activities that end in themselves, while institutions are social structures that sustain practices. As such, meaningful work emerges when practice is pursued by employees and is valued within the constraints of an institution. The danger with algorithmic management is that it can tilt heavily toward maximising output, speed and profit, while neglecting the ethical and social dimensions of the work environment that make practice worthwhile for employees.
Advocates for algorithmic management infer that algorithms can improve transparency and enhance fairness in the organisation. To them, this can be seen in the reduction of bias, as automated hiring tools can anonymise résumés to focus on skills rather than other considerations like gender, ethnicity, or age. They also argue that it boosts clarity by providing clear performance metrics that remove ambiguity from evaluations, and they laud its prospects for efficiency gains, as automated scheduling can ensure optimal resource allocation, thereby minimising idle time and overstaffing. In theory, algorithmic systems can free managers from repetitive tasks by allowing them to focus on critical issues like coaching, mentoring and strategy. This would enhance the meaningfulness of work by giving employees more space to pursue creative and value-driven goals.
However, real-world implementation usually reveals a different story, because the truth is that algorithms can create a “black box” effect, where decisions that concern the work environment, such as workloads, hiring, promotions and even terminations, are made without clear explanation, leaving employees puzzled and uncertain. For instance, consider the experience of a gig driver who wakes up to find that his account has been deactivated due to unclear algorithmic “fraud detection” systems, or a warehouse worker whose every movement is tracked and reported in real-time and becomes the basis for her performance appraisal. In such environments, work can start to feel less satisfying and more like a game of survival against a faceless scorekeeper. Looking at these scenarios from the MacIntyrean perspective, it shows that the overreach of algorithms can erode the virtues (such as honesty, craftsmanship and perseverance) required to sustain meaningful work, thereby leaving employees little space to exercise ethical judgement or develop mastery. When success is defined purely by algorithmic output, the psychological benefits of work are sidelined.
Rescuing this challenging situation behoves business leaders, middle managers, and HR departments to align algorithms with purpose. How can this be done? The answer lies not in rejecting algorithmic management outright but in embedding it within structures that preserve ethical and human dimensions of work. This can be achieved by instituting some valuable measures. First, HR managers and middle managers should coach employees to understand how algorithmic decisions are made and what data is being used. This will increase transparency and reduce alienation. Second, business leaders should introduce human oversight in critical decisions such as discipline, promotion and termination. This is important because algorithms lack moral and empathic reasoning, and as such, human oversight is needed to espouse contextual insights and humanity in the work environment. Finally, business leaders are advised to train algorithms to pursue metrics that go beyond efficiency. As such, performance dashboards on algorithmic balanced scorecard dashboards should measure qualitative factors such as customer satisfaction, innovation and collaboration, alongside quantitative metrics like output. This reinforces team spirit, social cohesion and recognition, which are important indicators of job satisfaction.
In all, algorithmic management is here to stay, but whether it enhances or erodes the meaning of work will depend on the values embedded in its design and use. Therefore, business leaders, HR departments, ethics officers and business regulators should frame algorithmic systems as tools that serve human practices, not replace them, by pushing policies that protect dignity and agency.
Dr Emmanuel Orakwe is a Research Associate at the Christopher Kolade Centre for Research in Leadership and Ethics (CKCRLE), Lagos Business School.
