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To better understand the impact of worker management through algorithms or artificial intelligence (AI) on workers’ safety and health, it is essential to examine facts and figures. Reliable data help assess the current state of implementation, identify emerging risks and trends related to occupational safety and health (OSH) and gain a clearer view of how these systems are perceived, experienced and managed by both workers and employers.

State of play of Artificial intelligence-based and algorithmic worker management (AIWM) in EU workplaces
AIWM systems are gaining ground in conventional workplaces across Europe, extending beyond their initial use in atypical jobs within the digital platform economy. Employers are adopting them for a variety of purposes, from assigning tasks to workers to tracking their performance. Recent surveys on the use of digital technologies in worker management (although not always focused exclusively on AIWM) offer a snapshot of how these new management technologies are being applied in practice.

According to a 2024 OECD survey of mid-level managers, in the four EU Member States included in the study (France, Germany, Italy and Spain), 79% of organisations have already adopted AIWM tools. The most common applications are for giving instructions to workers (69%) and basic monitoring (33%), such as software used to track working time. A smaller share, 6%, use AIWM to monitor the content and tone of conversations, calls or emails.

What's more, findings by the latest European Survey of Enterprises on New and Emerging Risks (ESENER 2024) show that more than one in ten EU establishments use systems to automatically allocate tasks, working time or shifts. Additionally, 7% have adopted technologies to monitor performance or behaviour. Insights from the 2022 OSH Pulse survey further highlight this trends from the workers’ perspective: 30% of them report being subject to tools that assign their tasks, shifts or working time, while 25% say these systems are used to monitor their work or behaviour.

However, adoption rates vary significantly depending on factors such as sector, company size and type of occupation. AIWM is more common in repetitive and routine jobs like warehouses, transport, retail, cleaning, manual and farm work. It is also prevalent among clerical, sales, service and call centre staff, but less so in professional, technical or higher administrator roles. The use of these systems is also more widespread in larger organisations with sizeable workforces, and/or with remote workers or teleworkers, and among people in certain atypical employment arrangements like digital platform work.

Impact of AIWM on workers and psychosocial risks
The managers surveyed in the OECD survey reported an improvement of their own job satisfaction due to algorithmic management, citing reductions in repetitive tasks and stress. However, 60% of them mentioned that concern for workers remains a common reason for not adopting these management technologies. Notably, over a quarter acknowledged inadequate protection of workers’ physical and mental health, highlighting a growing awareness among management of the potential negative impacts of AIWM on workers.

From the workers’ perspective, the main concerns include increased monitoring, micro-management, higher work intensity and performance pressure, as well as reduced autonomy and privacy.

Further insights from the 2022 OSH Pulse indicate that organisations using digital tools to automatically allocate tasks or working time, or to monitor work or behaviour see an increase in reported psychosocial risks. Over half of the surveyed workers in these environments experience severe time pressure and work overload. Nearly half report working alone, more than a third cite poor communication within their organisations and over a quarter note reduced work autonomy.

These psychosocial risk factors are closely linked to mental health issues in workers such as stress, depression or anxiety — conditions reported by 31% of workers where digital technologies are used to automatically allocate tasks or working time, and 34% where they are used to monitor work or behaviour (2022 OSH Pulse). Symptoms such as headaches, eyestrain and overall fatigue also appear to correlate with the increasing use of digital technologies in the workplace.

Mitigating negative effects
OSH prevention measures are being implemented across workplaces in the EU to mitigate the risks associated with digital technologies. For instance, according to ESENER 2024, 56% of establishments using digital technologies confirm that their use is considered in the workplace risk assessments. Moreover, a third of organisations report discussing the potential impacts of digital technologies with workers, compared to a quarter in 2019. 

According to the OECD report on algorithmic management in the workplace, a survey of union members in Denmark, Sweden, Finland and Norway reveals that algorithmic management does not negatively impact autonomy, trust or job satisfaction in workplaces where employees have substantial influence over company decisions and are actively involved and consulted during the implementation of new systems.

As also confirmed by EU-OSHA’s findings from case studies, worker participation and transparency are important mitigating factors for some of the psychosocial risks associated with AIWM. Therefore, it is crucial that organisations meet the legal requirements from the OSH Framework Directive, the AI Act and further relevant regulations on workplace prevention, worker participation and algorithmic transparency to protect workers' health and wellbeing from the risks of AIWM.

• Take a look at all content related to this priority area, worker management through AI.
• Explore our previous HWC article focusing on real-world learnings for implementing worker management through AI.
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