## 5 Conclusions
The data-driven approach with machine learning algorithms offers effective methods to handle multi-source data and capture the data context. Simulation andexperience reinforcement are other approaches to understanding the potential risks behind future scenarios. Simulation helps to study scenarios and estimate the effect of uncertainty. Regarding the project phase, there is a clear shift in the mindset toward \"design for it earlier\" rather than \"manage and modify it later\". The practices observed in this study indicate that a comprehensive evaluation of the available maintenance concepts, increased diagnostic coverage, and preparation of the assets to unlock the value of specific technologies are required to enable this mindset change. In addition, lifecycle costs and benefits must be estimated to motivate and encourage such a mindset shift. Regarding the engineering analysis in the project phase, the maintenance data used for reliability and risk analysis, e.g., failure rate, likelihood, and consequences, have emerged. The volume and type of data fed into the reliability and risk analysis are different compared to the past (where the corrective and scheduled maintenance data are only fed). This indicates a transition in the treatment of the maintenance data where generic stochastic models were used to more classified stochastic models that capture the common and unique patterns. Maintenance data is observed and expected to produce more spread-out, skewed, or even multimodal data (having multiple peaks) due to several different maintenance events taking place. This mix of data will affect the reliability and precision of stochastic models. Classical risk-based maintenance uses consequences to determine the maintenance task and the associated manning demand for preventive maintenance and to prioritize corrective maintenance tasks. These two purposes do not require physical modification of the assets. Basically, it requires RAM and personnel studies. Adapting condition-based, predictive, and prescriptive maintenance requires physical attachments and cyber infrastructures, which require specialized engineering analysis to determine their technical specifications. In addition, integration of condition-based, predictive and prescriptive maintenance into a risk-based maintenance program requires economic justification, i.e., cost-benefit studies and not only technical studies. The required technical and economic studies put more load on the project phase and the \"design for it\" mindset. More guidelines should be put in place on how to design and evaluate the proper smart assets and maintenance systems. The practices observed in this study indicate a move from risk at design (failure rate with consequence), risk at operation (consequence of failure), to risk at condition (health status and consequence of further deterioration). Keeping maintenance work based on risk at design is overwhelming for operators that invest in condition-based and predictive maintenance, while they have to perform heavy functional testing to comply with regulations. Definitely, regulations are established to ensure the safety and integrity of such critical assets, as most condition monitoring systems do not fully cover the potential failure modes. However, condition monitoring instrumentations and their valuable information about the health of these assets should be considered in determining testing intervals. The goal should be to ensure the safety integrity level using flexible intervals (based on conditions) rather than fixed intervals. It is worth mentioning that the new monitoring instrumentation brings its own failure modes and risks, e.g., over-instrumentation, obsolete parts, errors (false alarms), and cyber attacks. These failure modes and risks must also be assessed and mitigated. The emerging practices captured and their impacts are related to maintenance departments in some companies within the Norwegian continental shelf.