## 6 Conclusions  
This study has effectively addressed the main research question through the systematic application of DSR. The iterative DSR process, which ranged from problem identification to evaluation and communication of the artefact, enabled a comprehensive approach to the specific challenges of offshore maintenance, resulting in a robust system adaptable to the sector's needs. The research fulfilled SO1 through the development of the APM prototype. This tool integrates 3D models with advanced computational tools and incorporates multiple functional modules that address various aspects of maintenance planning. SO2 was achieved through the triangulation of the case study results, enabling an evaluation of the impact of different optimisation strategies on the generation of painting plans and providing valuable insights into the effectiveness of each approach. Integrating artificial intelligence techniques, specifically machine learning models for corrosion progression prediction, has proven crucial in enhancing the accuracy and efficiency of maintenance planning. This data-driven approach optimises resources and contributes significantly to environmental sustainability by minimising waste and extending the lifespan of offshore structures. APM's ability to process and analyse large volumes of historical data enables more informed and proactive decision-making, fundamentally transforming maintenance management in offshore environments. The APM prototype represents a significant advancement in the digitalisation of maintenance project management, offering a human-centred approach that balances technical, regulatory, and environmental demands. Integrating AI in the decision-making process allows for more accurate predictions of asset degradation, facilitating timely preventive interventions; dynamic optimisation of maintenance plans in response to changing conditions; the analysis of complex scenarios by considering multiple variables simultaneously; and continuous improvements in maintenance strategies through machine learning. The methodology and framework developed in this study offer significant contributions that can be generalised and replicated in developing other integrity management systems. In particular, integrating 3D models for visualisation and planning presents an innovative paradigm that could be extended to various types of critical infrastructure. The modular structure of APM provides an adaptable framework for other asset management contexts, demonstrating how AI can customise solutions for different industrial sectors. The validation process through case studies and focus groups, as well as visualisation of the results and strategy comparison techniques, offer robust methodologies that can be replicated in developing and evaluating other decision-support systems in maintenance. However, this study presents some limitations. The system's applicability has primarily been tested in the context of offshore platforms, and its effectiveness in other industrial settings has yet to be evaluated. Furthermore, reliance on high-quality historical data for training machine learning models may limit its implementation in scenarios with insufficient or unreliable data. For future work, exploring several areas that would expand the proposed system's functionality and applicability is recommended. One key improvement would be the integration of Internet of Things (IoT) technologies to enable continuous real-time data collection, which would enhance the predictive capabilities and allow for a more immediate response upon detecting anomalies. Incorporating IoT sensors at critical points of the infrastructure would allow constant monitoring of the environmental and structural factors, optimising the precision of the maintenance models. Another relevant direction for future research is adapting the system to onshore installations, where the operational requirements and environmental conditions differ significantly from those of offshore platforms.