Artificial intelligence has moved from research labs into everyday work, shaping hiring, productivity, and even promotion decisions. While organisations see efficiency gains, many workers experience growing uncertainty and a loss of control.
Such disruption is not new, but the speed, scale, and opacity of today’s AI systems set this moment apart. Across sectors, shared anxiety about algorithmic decision-making is emerging as a potential catalyst for renewed worker organisation.
This article explores how AI-driven workplace anxiety is taking shape, why it could fuel a new workers’ movement, and what this means globally.
Understanding AI Anxiety in the Workplace
Artificial intelligence refers to software systems that perform tasks traditionally requiring human judgment, including pattern recognition, prediction, language processing, and decision support. In workplaces, it is commonly applied through machine learning models, automation tools, and algorithmic management systems.
AI anxiety emerges when workers feel these technologies threaten their livelihoods, autonomy, or dignity. Unlike earlier automation that replaced discrete manual tasks, AI increasingly reshapes decision-making itself, influencing performance assessment, scheduling, hiring, and dismissal through systems that are often opaque.
This anxiety reflects a bigger structural change. As algorithms function as de facto managers, workers may struggle to challenge errors or bias, while constant digital monitoring intensifies work and destabilises career paths. Significantly, these concerns now span skill levels, uniting clerical and professional workers alike and giving AI the potential to reconfigure labour politics rather than divide it.
How AI Is Reshaping Power at Work
In practice, AI systems alter workplace power dynamics by concentrating decision-making authority. Employers gain tools that promise objectivity and efficiency, yet often rely on proprietary models designed and owned by external technology firms such as OpenAI, Google, and Amazon.
For workers, this can mean fewer opportunities for negotiation. Decisions once made by supervisors who could be challenged informally are now embedded in systems presented as neutral or scientific. The language of optimisation can obscure the fact that these systems encode managerial priorities, cost pressures, and, at times, flawed data.
Algorithmic management is particularly visible in logistics, ride hailing, and warehousing, where software assigns tasks, tracks performance in real time, and enforces discipline. Yet similar logics are spreading into offices through productivity analytics, AI-assisted evaluation tools, and automated workflow systems.
The result is not just fear of job loss, but concern about fairness, accountability, and voice. These concerns form fertile ground for collective responses.
Historical Parallels and Lessons
History suggests that periods of technological upheaval often trigger labour organisation rather than silence it. The Industrial Revolution gave rise to early trade unions as workers sought protection against unsafe conditions and arbitrary dismissal. The spread of mass manufacturing in the twentieth century led to the development of collective bargaining systems to manage productivity gains and distribute benefits.
What distinguishes the AI era is the invisibility of the technology. Workers may not even know when AI is shaping their work or on what basis decisions are made. This lack of visibility complicates traditional organising strategies but also creates new shared grievances.
Past labour movements succeeded when they reframed technological change as a social issue rather than an individual failing. The emerging AI debate echoes this pattern, with growing calls to treat algorithmic management as a matter of rights rather than just efficiency.
Global Responses to AI-Driven Labour Anxiety
Across Europe, labour anxiety linked to AI has prompted regulatory action and stronger union engagement, with an emphasis on transparency, worker consultation, and limits on automated decision-making in employment.
In North America, responses have been more fragmented. Worker resistance has emerged through strikes, lawsuits, and sector-specific organising, particularly in media, logistics, and technology, where algorithmic management and generative AI are most visible.
Across Asia, governments have largely prioritised national competitiveness and reskilling, often containing labour unrest through state-led frameworks rather than collective bargaining, even as worker concerns persist beneath the surface.
In Latin America, AI anxiety is intersecting with longstanding informal labour structures, leading to sporadic mobilisation focused on platform work, surveillance, and job precarity rather than broad-based AI governance.
In Africa, responses remain uneven. While AI adoption is accelerating in finance, telecommunications, and public services, labour institutions are still adapting, and collective engagement with AI-driven workplace change is limited. In Nigeria, AI-related labour anxiety is growing quietly within the formal sectors, shaped by high unemployment, weak enforcement of labour protections, and limited transparency around algorithmic decision-making, creating conditions that could eventually push AI into the centre of labour discourse.
Implications for Jobs, Skills, and Governance
The most immediate implication of AI anxiety is its impact on job quality rather than job quantity. While some roles will be displaced, many more will be transformed. Workers may retain employment but under conditions of intensified monitoring and reduced autonomy.
For education and skills development, the challenge is not only technical training but critical literacy. Workers need to understand how AI systems influence decisions and what rights they have in relation to them. Without this, reskilling initiatives risk placing the burden of adaptation entirely on individuals.
From a governance perspective, AI-driven labour issues raise questions about accountability. When a worker is dismissed based on an algorithmic score, who is responsible? The employer, the software vendor, or the data itself? Clear answers are essential for trust and stability.
If these questions remain unresolved, collective action becomes more likely. Workers may organise not only around pay and conditions but around transparency, data rights, and human oversight.
Could AI Spark a New Workers’ Movement?
The possibility of a new workers’ movement centred on AI does not imply a return to old models of industrial unionism. Instead, it may take more hybrid forms. Professional associations, digital collectives, and cross-sector alliances could emerge alongside traditional unions.
AI anxiety has the potential to unite workers across class and skill divides. A software engineer concerned about automated code review and a call centre worker monitored by sentiment analysis face different realities, yet share concerns about opaque decision-making and diminished agency.
In Nigeria, this could translate into new forms of labour advocacy that combine technical expertise with social organising. Universities, civil society groups, and labour federations may need to collaborate to build capacity around AI literacy and worker rights.
However, this outcome is not inevitable. Without institutional support, anxiety can also lead to disengagement, migration, or quiet compliance rather than collective action.
Coping with the dilemma
For AI-driven labour anxiety to be channelled constructively, several shifts are necessary. Transparency must become a baseline requirement for workplace AI systems. Workers should know when algorithms are used and how they affect decisions.
Regulators need to update labour and data protection laws to explicitly address automated decision-making. This includes rights to explanation, appeal, and human review. Employers, in turn, must recognise that trust is a productive asset, not a regulatory burden.
In Nigeria, investment in institutional capacity is critical. Labour organisations need access to technical expertise, while policymakers require evidence-based frameworks tailored to local market conditions. Public dialogue on AI should move beyond innovation rhetoric to include worker experience.
Artificial intelligence is reshaping work in ways that challenge long-standing assumptions about control, expertise, and fairness. The anxiety it generates is not a sign of resistance to progress, but a response to rapid change without sufficient social negotiation.
Whether this anxiety fuels a new workers’ movement will depend on how societies respond. If AI is deployed as an unquestionable authority, conflict and mistrust will grow. If it becomes a subject of democratic oversight and collective bargaining, it may yet strengthen the social foundations of work.
Senior Reporter/Editor
Bio: Ugochukwu is a freelance journalist and Editor at AIbase.ng, with a strong professional focus on investigative reporting. He holds a degree in Mass Communication and brings extensive experience in news gathering, reporting, and editorial writing. With over a decade of active engagement across diverse news outlets, he contributes in-depth analytical, practical, and expository articles exploring artificial intelligence and its real-world impact. His seasoned newsroom experience and well-established information networks provide AIbase.ng with credible, timely, and high-quality coverage of emerging AI developments.