The digital tools that drive team activity are multiplying, and with them, new regulatory obligations. Companies looking to improve their performance and management face a paradox: the software intended to optimize productivity can now engage their legal liability if its deployment is not properly managed.
Algorithmic Management and Compliance: A Blind Spot for Companies
Assigning tasks, organizing schedules, assessing workload: these functions, even without the use of artificial intelligence, can fall under algorithmic management. The OECD recommends regular audits, consultation with workers, and the establishment of dispute mechanisms. Data protection law and labor law remain fully applicable to these systems.
The European regulation on artificial intelligence (AI Act) goes further. Systems used to influence working conditions, promotions, contract termination, or performance evaluations are likely to be classified as high risk under Annex III of the text. Their deployment requires human oversight, prior information for employees, and the ability to contest results.
For SME leaders, this means that a simple planning or goal-tracking software can fall within the scope of these obligations. Before even discussing efficiency gains, it is necessary to discover the Performance and Management services suited to this regulatory framework, or risk deploying non-compliant tools.
Team Performance: What Productivity Does Not Measure
Most optimization approaches focus on performance indicators: deadlines, volumes, conversion rates. These metrics remain useful, but they capture only part of the operational reality.
The effects of a tool on employees’ mental load and autonomy escape traditional dashboards. A company that introduces task-tracking software may see a short-term increase in productivity while generating a deterioration in the work environment that financial indicators do not reflect for several months.
Integrating AI or automation projects into the prevention of psychosocial risks becomes a concrete challenge. Measuring productivity gains is not enough: it is necessary to examine the effects on pace, cooperation, responsibilities, and then update the risk assessment when the organization of work changes. This is an obligation that falls under the single document for assessing professional risks.
Engagement and Internal Processes
A poorly designed process consumes managerial time without creating value. Field feedback shows that companies that sustainably improve their efficiency do not start by adding tools, but by simplifying their existing decision-making circuits.
Employee engagement depends less on tools than on the clarity of roles and the frequency of direct exchanges. When an employee clearly understands what is expected of them and has a channel to report difficulties, their net contribution increases, regardless of the software used.
Mandatory AI Training: An Underestimated Management Lever
Article 4 of the AI Act provides for a mandatory training obligation for personnel deploying or using these systems. This regulatory constraint can become a management lever if treated as an investment rather than a checkbox.
- Training managers on the limitations of algorithmic tools allows them to retain control over decisions, instead of blindly delegating to the software.
- Involving teams in the selection and configuration of tools strengthens their buy-in and reduces resistance to change.
- Documenting acquired skills facilitates compliance audits and feeds into the skills development plan.
Field feedback varies on the extent of gains achieved through training alone. Some companies see rapid adoption, while others face cultural blockages that technical training does not resolve. Training works when accompanied by adjustments to processes and dialogue about actual usage.
Performance Management: Quantitative Indicators and Weak Signals
Financial and operational KPIs remain the foundation of management. Margin rates, delivery times, customer satisfaction: these data allow for quick corrections of deviations. However, they say nothing about the sustainability of performance over time.
Weak signals, on the other hand, emerge from informal exchanges, turnover rates, and feedback during individual interviews. Increased turnover in a high-performing team is a warning signal, not a trivial indicator. It often reveals excessive pressure or a lack of recognition that dashboards do not capture.
Articulating the Two Levels of Analysis
An effective performance management system combines monthly reviews of quantitative objectives with regular check-ins on team experiences. The difficulty lies in the frequency: if too spaced out, qualitative exchanges lose their utility. If too close together, they become an additional administrative burden.
- Set a quarterly rhythm for qualitative assessments, monthly for operational indicators.
- Systematically cross-reference productivity data with HR indicators (absenteeism, internal mobility, training requests).
- Reserve dedicated time during management committees to analyze discrepancies between measured performance and field perception.
Management is not just about data collection: it requires managerial judgment between what the numbers show and what the teams express. Companies that endure over time are those that accept to slow down a profitable project when human signals demand it.
Improving performance now requires a dual reading, regulatory and human, that neither software nor indicators alone can provide. The framework established by the AI Act and the obligations to prevent psychosocial risks redefine what it means to effectively manage a company.
