What is algorithmic management?
Algorithmic management involves using data-processing systems to organize, assign, monitor, or evaluate work activities. It can influence task sequencing, shift scheduling, routes, or performance indicators. Its preventive importance stems from how it modifies the conditions under which people work.
Artificial intelligence is not always used. The ILO distinguishes between rule-based systems and those that learn or make predictions. A simple mechanism that automatically assigns work based on recorded times can significantly influence exposure, even if it doesn’t use an advanced model or isn’t commercially presented as AI.
Prevention among workers on digital platforms requires analyzing how assignments, schedules, and performance evaluations influence the work. The assessment must encompass both the digital organization and the material conditions of the work.
Where can it be found
These management methods appear on digital platforms as well as in traditional settings, such as warehouses, transportation, customer service, or healthcare. Individuals may receive instructions via an application, while management retains only partial information about how those instructions were generated or what restrictions were taken into account.
To determine its scope, it’s helpful to ask which decisions are automated, which are merely recommended, and which remain subject to human intervention. It’s also important to know if a recommendation can actually be rejected. A review option that is never used due to lack of time or penalties may leave human oversight as merely a formality.
Effects on working conditions
Automatic assignment can reduce incoordination, but it can also intensify the pace or limit autonomy. A system may ignore actual movements, pauses, task difficulties, or environmental variations. The ILO has highlighted psychosocial risks related to surveillance, intensification, loss of control, and concerns about data use.
The effects are not limited to the psychological. An overly structured route can encourage rushing; a repetitive sequence can concentrate physical exertion; a performance ranking system can discourage incident reporting. The analysis must examine the relationship between digital decisions and real-world activity, without assuming that automation always improves or worsens safety.
Evaluation before implementing or changing
Change management should review the system before implementation and whenever objectives, rules, or relevant data change. It’s advisable to analyze representative tasks, exceptional situations, and affected groups. Testing limited to ideal conditions can mask problems that arise during peak demand or operational failures.
The psychosocial assessment should consider control, predictability, support, and time pressure, along with other risks. Applicable labor, data protection, and AI obligations also require specific analysis. Not all systems have the same legal classification or requirements, so regulatory conclusions should not be generalized.
Preventive design and use criteria
Objectives must incorporate safe work constraints, including times and conditions that a productivity metric might overlook. Mechanisms must be in place to report assignment errors, correct data, and review decisions. The responsible person needs the information, competence, and effective authority to intervene when the system produces inappropriate instruction.
It is important to explain to employees which relevant aspects the system organizes and how to raise a discrepancy. The information should enable them to take action, not be limited to an incomprehensible technical description. It is also advisable to avoid supervision that adds unnecessary technostress through constant alerts or the requirement to justify every minor deviation.
Practical example
A warehouse uses an application that assigns the next task based on historical average time. During a test, the team observes that the calculation doesn’t include additional trips when an area is temporarily closed. The indicator interprets these trips as lower throughput and increases the pressure to make up for lost time.
The review incorporates the actual status of the zones, allows incidents to be recorded without automatic penalty, and establishes who can suspend the assignment. It then checks if the routes are viable and if safety times are maintained. The solution requires modifying the logic and its use, not asking staff to achieve an unattainable average.
Data and decision tracking
The quality of data analysis is crucial: incomplete or unrepresentative records can lead to inappropriate decisions. It’s important to monitor whether the system performs differently across tasks, shifts, or groups, and whether human corrections are incorporated into organizational learning.
Monitoring includes vendor updates and configuration changes. A system validated for one process is not automatically validated for another. It’s also important to verify that the review channel is accessible and that people aren’t hiding issues for fear of automatic consequences on their evaluation.
Common mistakes and responsibility
Common mistakes include considering any IT decision neutral, accepting a score without context, or equating human oversight with simply pressing a confirmation button. It’s also a mistake to buy a solution without analyzing what new logging, review, and maintenance tasks it creates for the organization.
Preventive responsibility is not transferred to the algorithm or the provider. The company must evaluate and monitor the relevant conditions of use, with the participation of those affected. The aim is for the system to support a safe, understandable, and reviewable work organization throughout its use.
