It is no secret that artificial intelligence is becoming increasingly prevalent in the day-to-day operations of companies. Although its use requires caution, the fact is that this technology is beginning to directly influence variable compensation models, affecting metrics for bonuses, profit-sharing, and performance evaluations.
This trend reflects a growing desire for greater objectivity in measuring productivity and efficiency, with implications for goal setting, compensation packages, and even the organization of work schedules.
Reports have emerged regarding the use of advanced analytics and AI tools to support decision-making related to professional development and compensation for employees. The focus here is not on replacing people, but rather on improving tools to measure performance, reduce subjectivity, and align incentives, including by considering the organizational structure as a whole and as a means to reduce the risk of wage distortions. Nevertheless, the consensus is that the final decision should remain under human management, especially when dealing with qualitative factors that often escape automated metrics.
Data from Lightcast indicates that AI-related skills are already associated with significantly higher salaries in the global market, demonstrating a structural shift in how work and productivity are valued.
In this context, companies have been using artificial intelligence to optimize workflows, reduce administrative tasks, and increase operational efficiency. In some cases, they are already beginning to link variable compensation to demonstrated concrete gains in productivity, process improvements, and the efficient use of technology. In practice, the market is increasingly rewarding not only results but also proficiency in artificial intelligence as a competitive advantage and a key criterion for professional performance.
This discussion directly addresses recent debates on work schedule flexibility and productivity, including in the context of the 6×1 schedule. For certain sectors, technology can enable more flexible and sustainable models, provided they are based on objective, auditable metrics that are compatible with companies’ operational realities.
However, this issue calls for caution. Transparency in evaluation criteria, data governance, traceability of metrics, and ongoing review of the parameters used are essential to mitigate the risks of indirect discrimination, algorithmic distortions, and biases that could compromise the validity and legitimacy of the models adopted.
Ultimately, companies that are able to develop smart, auditable, and legally sound metrics, always under human supervision and validation, tend to achieve significant gains in productivity, efficiency, and employee engagement.
