# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 7 Conclusions
Furthermore, the study provides valuable input for AI4 LABOUR's skill-based modeling efforts by highlighting emerging job roles and competencies associated with AI-assisted maintenance and workforce planning. The adoption of AI-driven tools necessitates targeted training programs for service personnel, fostering adaptability and digital readiness. This aligns with AI4 LABOUR's long-term objective of building policy recommendations and skill-matching frameworks to support companies and institutions in navigating AI-induced labor shifts. In addition, this work contributes to bridging ESCO and O*NET occupational classification frameworks with AI-driven workforce evolution. By analyzing the transition from rule-based maintenance systems to automation-supported service roles, this study provides a concrete application of how emerging tasks and digital skills can be mapped onto standardized taxonomies. ESCO's emphasis on AI-related technical skills and O*NET's focus on evolving job tasks offer structured ways to understand how predictive analytics reshape workforce requirements. By incorporating these insights into AI4 LABOUR's methodology, the study helps refine labor forecasting models and supports data-driven policy design. Despite its contributions, this study has certain limitations. It focuses on two sub-models of a single vehicle brand, which restricts the generalizability of the findings. Future research could expand this framework by incorporating multiple vehicle brands and models to enhance its generalizability, while also exploring the integration of customer churn prediction and win-back strategies to further optimize after-sales services. Moreover, the application of AI-driven forecasting techniques in workforce planning represents a promising direction, as the predictive methodology utilized in this study aligns closely with AI4 LABOUR's objective of anticipating emerging occupations and evolving skill requirements, indicating the potential for cross-domain adaptation of similar models. This framework has the potential to be widely adopted across the automotive industry as a scalable and effective solution for optimizing after-sales service operations. Its machine learning-driven approach can be easily customized and extended to accommodate diverse vehicle brands, models, and customer segments, making it applicable to various contexts within the automotive sector. By providing actionable insights into maintenance demand and customer behavior, this methodology enables manufacturers and service providers to improve resource utilization, enhance customer retention, and ensure vehicle safety on a broader scale. The generalizability of this framework positions it as a valuable tool for standardizing predictive maintenance practices and fostering data-driven decision-making throughout the industry. Currently, we are actively working on extending the proposed framework to cover three additional vehicle brands. Although pilot implementation and usability testing across different brand ecosystems involve lengthy validation processes, preliminary results have been promising in terms of prediction accuracy and operational integration. These ongoing efforts aim to further strengthen the framework's generalizability and its potential for cross-brand applicability. Findings from these expanded implementations will be shared in our subsequent publications, contributing to the development of a brand-independent predictive maintenance architecture in the automotive domain. Moreover, we believe that the modular structure of the feature engineering and model training pipeline makes it suitable for deployment in varied geographical regions and under different market dynamics, with minimal customization. By minimal customization, we refer to the limited adjustments required to align with different market conditions, regulatory constraints, or data availability.