## 6 Conclusions
Validation of the proposed model in real conditions within industrial, service, and commercial enterprises. This would provide empirical evidence of its applicability and robustness in diverse operational environments. 2. The assessment framework could be extended to include emerging Industry 5.0 technologies, such as neurotechnologies, immersive technologies, and advanced tools for human-machine collaboration. 3. Integration of the model with ERP/MES tools and AI-based decision-making systems. Future work could explore the integration of the model with ERP, MES, and AI-based decision support systems, enhancing its utility in real-time planning and operations. 4. The application of alternative MCDM methods (e.g., fuzzy AHP, ANP, TOPSIS) and exploration of machine learning models for dynamic weight adaptation. Further analysis could employ other multi-criteria decision-making techniques--such as fuzzy AHP, ANP, or TOPSIS--and investigate the use of machine learning algorithms for adaptive weighting based on evolving priorities or data streams. 5. The development of user interfaces in the form of dashboards and tools for quick interpretation of scenario results. Designing interactive dashboards and scenario-based visualization tools could facilitate faster interpretation of results and support real-time strategic decision-making by managers and stakeholders. 6. Conducting a dedicated sensitivity analysis to evaluate the robustness of the ranking results in relation to possible variations in the criteria weights. Methods such as weight perturbation analysis or Monte Carlo simulation could provide additional validation of the stability and reliability of the prioritization outcomes. The results showed moderate to strong agreement among expert judgments (\(W=0.60\) for DEMATEL; \(W=0.79\)  for PROMETHEE II), which supports the reliability of the findings. However, several limitations should be acknowledged:  
1. This study is based on a limited expert panel and single-round evaluation. Expanding the panel and introducing iterative or Delphi-based procedures could enhance the robustness and stability of the findings. 2. Although the methods applied provide a balance between rigor and managerial usefulness, more advanced statistical methods (e.g., structural equation modelling or Bayesian analysis) could provide greater validation precision and deepen theoretical insight. 3. There is a lack of a fully developed visual layer of results--developing intuitive dashboards, interactive maps, or graphical summaries could significantly increase the interpretability and practical utility of the findings for decision-makers. 4. The set of 22 technologies assessed in this study represents a static selection based on their current strategic relevance; however, it does not encompass rapidly emerging domains such as generative AI or neurotechnologies. While this constitutes a limitation of the present version, the framework is intentionally designed to be extensible. Future assessments may expand the technology pool and incorporate new developments by reapplying the same MCDM procedure with updated expert input. 5. The expert evaluation offers a static snapshot of judgments captured at a single point in time. Although the method ensures consistency within the panel, it does not account for evolving perspectives or dynamic shifts in strategic priorities. To enhance the temporal robustness of expert input, future studies could adopt iterative approaches, such as Delphi panels or multiple rounds of feedback. 6. Although the expert panel was diverse in terms of industry background and domain expertise, it did not include representatives from small- and medium-sized enterprises (SMEs), whose priorities and constraints may differ significantly from those of larger organizations. To ensure broader applicability and relevance, future research should consider stratified sampling or targeted inclusion of SME stakeholders. The research presented in the article was carried out at the Maritime University of Szczecin as part of task 1/S/KI/25.