Master’s Thesis Summary: AI-Driven Absenteeism Analytics in Manual Workforce Enterprises — A Shift from Operational Insights to Strategic Impact
Author: Severi Sinkko
Organizations having manual workforce operations face challenges in managing absenteeism, including fragmented data systems, lack of real-time visibility, limited analytical capabilities, and even organizational barriers. These issues lead to reactive mitigation and absenteeism analyses, where absences are managed only after they occur rather than being anticipated or strategically addressed. These reactive interventions rely on traditional methods such as monitoring absence rates, implementing return-to-work interviews, enforcing attendance policies, and providing generic wellness programs. However, these interventions are rather diagnostic than preventive, lacking the data-driven and predictive power that modern AI and ML solutions could provide, ultimately making absenteeism management a more strategic matter for organizations.
This master’s thesis explored the transformative potential of integrating AI-driven analytics within absenteeism management. The central aim was to investigate how such technological integration could facilitate a fundamental shift in absenteeism management - ultimately, re-conceptualizing absenteeism from an operational metric into a strategic tool. To achieve this goal, the research utilized a multiple case study approach, combining a comprehensive review of existing academic literature with expert interviews conducted with both HR professionals and technology consultants. The HR professionals were utilized to understand and validate the challenges and needs from the organizations’ perspectives. In contrast, technology consultants validated the results and analyzed whether an AI-driven absenteeism management tool could increase the strategic foothold of HR.
The key findings of this research highlight a gap in the current organizational capabilities: organizations’ HR functions cannot generally generate highly data-driven and hence more insightful analytics related to employee absenteeism, ultimately not making the matter interesting from the management point of view. This lack of capability means absenteeism management can create little to no strategic impact. The interviews with HR representatives revealed that while the negative consequences of absences on operations, productivity, and team cohesion are recognized, the systems and processes in place often struggle with data integration and the provision of data-driven insights.
To enhance the strategic significance of absenteeism management, the thesis argues the following;
Organizations must first establish a robust foundation characterized by the collection of unambiguous and consistently defined organizational-level data.
Subsequently, it is crucial to implement systematic processes for the comprehensive management of absenteeism.
Only upon this foundational layer can organizations effectively unlock the inherent potential for insightful absenteeism analytics and thereby enable a more strategic approach to management decision-making.
The anticipated impact of AI tools, as explored in this study, lies in their capacity to move beyond diagnostic analyses towards predictive workforce analytics, offering the potential to inform critical strategic decisions, such as resource allocation and corporate strategy. Figure 2 illustrates the proposed potential development track for increasing the strategic significance of absenteeism management.
Figure 1 Potential Development Track for Increasing Strategic Significance of Absenteeism Management
This thesis may be obtained from Aalto University Learning Centre upon request.
Footnote: We had the great opportunity to have brilliant Severi Sinkko, an Industrial Engineering and Management student from Aalto University working on his master's thesis for Aino with funding from the Technology Industry for Artificial Intelligence.