## 1 Introduction  
Artificial intelligence (AI)-powered digital twins (DTs) for Industry 4.0/5.0 integrate physical systems with virtual models to improve decision making and process optimization in smart manufacturing. DTs are created by collecting real-time data from sensors embedded in physical assets, which is then used to create a dynamic, virtual replica of the system [1]. AI algorithms process and analyze this data to predict behavior, identify potential failures, and suggest optimizations, improving operational efficiency [2]. Advanced machine learning models enable DTs to evolve and adapt by learning from new inputs over time, increasing their predictive accuracy [3]. Edge-cloud and network computing architectures are used to ensure rapid data exchange and processing, minimizing latency in real-time decision making. The integration of Internet of Things (IoT) devices enhances the ability of DTs to continuously monitor and adapt industrial operations [4]. Industry 5.0 integrates human-centric considerations by enabling collaboration between human operators and AI systems in a DT environment, ensuring that technology complements rather than replaces human expertise [5]. AI-based DTs help achieve more efficient, adaptive, and resilient industrial processes by combining human intelligence with advanced automation, leading to better resource allocation [6]. The ability to simulate different production scenarios allows manufacturers to test and optimize processes before making changes to real-world operations, reducing risk and costs [7]. DTs also support predictive maintenance by identifying potential equipment failures in advance, preventing costly downtime [8; 9; 10]. DTs support predictive maintenance by continuously monitoring equipment performance and conditions in real time [8]. By analyzing sensor data and historical trends, they can identify signs of wear, defects, or anomalies before a failure occurs. Early detection allows maintenance teams to proactively resolve issues rather than react to failures [9]. As a result, organizations can prevent unexpected equipment failures and significantly reduce unplanned downtime. Ultimately, this leads to increased operational efficiency and cost savings [10]. Cyber-physical security measures must be incorporated into DTs to ensure data integrity and prevent cyber threats that could disrupt industrial operations. The scalability of AI-based DTs enables its application in various industrial domains, from automotive to pharmaceutical manufacturing [11]. The continuous improvement of DTs through reinforcement learning can further enhance process optimization by automatically adjusting production parameters [12]. Collaboration between multiple DTs in a smart factory ecosystem enables synchronized decision making and increased overall efficiency [13; 14]. Future advances in quantum computing and generative AI could further accelerate DT capabilities, enabling more complex simulations and deeper insights into industrial processes. Future advances in quantum computing could significantly increase the computational power available for DT simulations, enabling the real-time modeling of highly complex, multidimensional systems. Quantum algorithms can solve optimization and simulation problems currently too resource-intensive for classical computers, increasing the accuracy and speed of DT analysis. At the same time, generative AI can enhance DTs by autonomously generating realistic scenarios, predicting rare events, and filling gaps in incomplete datasets. Together, these technologies could enable DTs to gain deeper insights into system behavior, uncover hidden patterns, and support more precise decision making. As they develop, quantum computers and generative AI will likely expand the scope and scalability of DT applications in industries such as manufacturing, energy, and healthcare. The aim of this study is to analyze the possibilities and occupations associated with the current and future personalization and optimization of AI-based DTs.