## 4 Discussion
Cybersecurity vulnerabilities pose a major concern, as warranty management systems increasingly rely on IoT-connected assets and AI-driven analytics, making them potential targets for data breaches and cyberattacks. High initial costs associated with implementing AI models, IoT infrastructure, and digital twin platforms can be a barrier, especially for small- and mid-sized enterprises. Additionally, workforce adaptation challenges arise as organizations must train employees in AI-based diagnostics, predictive analytics, and automated warranty processing, requiring a cultural and technical shift. Addressing these risks necessitates robust cybersecurity frameworks, phased implementation strategies to optimize return on investment, and ongoing workforce upskilling programs to ensure successful digital adoption in warranty management. Ref. [67] warns about the risks inherent in digital transformation and highlights the importance of implementing robust cybersecurity measures. In this context, it is essential to adopt advanced security protocols, comply with regulations of standards such as ISO 42001, and ensure ethical data management to protect both the company and customers. The comparison between traditional warranty management and management based on artificial intelligence and digital twins reveals clear advantages of the digital approach. Firstly, in terms of downtime, traditional maintenance is reactive, while predictive maintenance allows interventions to be scheduled before failures occur, improving availability and operational continuity. Secondly, resource optimization is significantly more effective with artificial intelligence, as traditional methods require more corrective interventions, while AI allows for precise interventions, thus reducing maintenance costs. The digital approach improves customer satisfaction. While traditional warranties are often standardized, the use of AI allows for a personalized service according to the individual needs of each customer, increasing the positive perception of aftersales service and strengthening customer loyalty. In terms of future opportunities and innovative potential, the integration of AI and digital twins in warranty management represents an opportunity to innovate and add value in the industry. The structured methodology for digital warranty management incorporates predictive analytics models to enhance decision-making. Bayesian networks are employed to model probabilistic dependencies between failure events, enabling risk assessment under uncertain conditions. Machine learning classification models, such as Support Vector Machines (SVM) and Random Forests, are used to categorize defect patterns and optimize warranty claim processing [45]. Additionally, cost-risk optimization functions, including stochastic programming and mixed-integer linear programming (MILP), facilitate warranty policy adjustments by balancing service costs against asset reliability. While this framework provides a structured approach, future research should focus on empirical validation through field studies and pilot implementations in real-world warranty operations. Industry collaborations could test the effectiveness of AI-driven warranty models across sectors such as the automotive, railway, and aeronautics industries, refining model accuracy and optimizing life-cycle cost predictions [59]. Such studies would provide quantifiable performance metrics, reinforcing the practical applicability and scalability of this digital transformation framework. In the future, developments in these technologies will enable adaptive business models that incorporate real-time life-cycle data, allowing adjustable warranties to be offered based on the actual use of the asset. Future research should focus on the implementation of regulations and best practices that mitigate risks and promote transparency in the handling of sensitive data [35]. Another possible line of research is to develop continuous improvement models for AI and servitization.