Fixed-bottom or floating offshore structures, including wind farms, oil/gas platforms, and subsea systems, provide higher and more consistent wind speeds, higher energy yield per turbine and per km, vast available area, reduced visual and noise impact, proximity to coastal load centres, lower land acquisition costs and conflicts, and synergies with existing maritime infrastructure, ports built for oil/gas, or shipping that can be repurposed. Design-to-Decommissioning AI-Driven Plant Design Modelling (D2D-AI-PDM) has the potential to enable a scalable, sustainable offshore energy, whether renewables or transition fuels, ensuring assets are designed once but optimised forever. Traditional fragmented approaches adopt tools that create data silos, leading to inefficiencies in the process. The end-to-end D2D-AI-PDM approach has the potential to evolve autonomously with real-world data, enabling predictive, optimised, and increasingly autonomous decision making.

This study reviews and highlights the current trends and level of adoption of the latest technologies, such as generative AI, neural networks, reinforcement learning, and self-updating digital twins, into a unified, continuous workflow that spans the entire offshore asset lifecycle, from conceptual design and engineering, procurement, construction, installation, and operations and maintenance (O&M), to decommissioning. The current trends and available platforms for achieving D2D-AI-PDM, as well as the current state and level of readiness of International Oil Companies (IOCs) to adopt them, were analysed using publicly available information. This study also highlights the challenges of data integration and interoperability, data privacy and governance, trust, and regulation, as well as skills in the offshore energy sector.