## 1 Introduction  
Digitalization has become a buzzword in the business world as companies seek to improve their efficiency, productivity, and profitability by adopting the latest available technologies. In particular, Industry 4.0 technologies have proved to be useful for the optimization of production processes through the use of interconnected devices and systems, resulting in the improved agility and flexibility of the organization when it comes to changes in demand, reducing downtime, and increasing the overall productivity. The integration of data analytics and machine learning algorithms in production processes has also been shown to provide valuable insights into operational inefficiencies, enabling companies to identify areas for improvement and optimize their processes for maximum efficiency. Furthermore, Industry 4.0 technologies have the potential to significantly reduce costs by automating repetitive tasks and minimizing the need for manual labor. This not only reduces labor costs but also increases production rates and quality while reducing the risk of errors. While the benefits of digitalization are well known and understood, the costs and challenges of implementing these technologies are often underestimated. In particular, identifying the optimal route towards transformation involves a careful analysis of various factors, including the level of integration necessary at each stage of the transformation, encompassing the convergence of new digital processes with existing legacy and physical elements [1]. One of the main challenges is the need to customize the technologies to fit existing processes, final product specifications, and environmental conditions of the company. This requires a significant amount of time, resources, and expertise from system integrators, technicians, and engineers involved in technology transfer. The integration of new technologies is not a straightforward plug-and-play process. Instead, it requires modifications and adaptations to align with the company's requirements. The need for customization may stem from several factors, such as the company size, the type of industry, the complexity of the workflow, and the automation level already in place. For example, a large manufacturing company may have different needs and requirements than a small service-based business. Similarly, a company in the pharmaceutical industry may require more stringent quality control measures than a company in the fashion industry. The process of customizing Industry 4.0 technologies to a company's specific needs can be complex and time-consuming and may require significant financial investments. It may involve several steps, such as data analysis, process mapping, and technology selection. In this context, research and development activities become an essential part of the digitalization process, as data collected and analyzed during the process can be used to design and implement solutions that optimize the integration of new technologies. Given these challenges, this paper seeks to answer the following research question: How can companies effectively evaluate and align the maturity of Industry 4.0 technologies with their internal readiness levels to optimize digital transformation efforts, particularly in resource-constrained environments? To address this, we propose a framework that combines the Technology Readiness Level (TRL) with the Industry 4.0 Maturity Index (MI), offering a structured tool to guide technology adoption based on both technological and organizational readiness. In this paper, we explore the emerging challenges when customizing technologies for digitalization and the role of research and development activities in supporting technology integration. We also provide insights and recommendations for companies seeking to embark on a digitalization journey based on our analysis of real-life case studies.