Power to AI? Power to the people!

The rise of artificial intelligence (AI) solutions, especially in the offshore industry, does not mean our work is done. The key question is not whether AI can replace us, but how we can leverage it to make smarter, more informed choices. After all, AI is not magic, but understanding what we want it to achieve is.

To successfully integrate AI, we first need a clear strategy. This means deeply understanding our existing processes, identifying inefficiencies and recognizing bottlenecks that impact efficiency and cost the most. We must assess how our workflows are structured, where the biggest time losses occur, and which processes contribute most to operational expenses. Only with this insight can we determine where AI adds value and where human expertise remains irreplaceable. One thing is certain: AI is here to stay and will continue evolving. To remain competitive, we must adapt and find ways to integrate AI effectively into our workflows.

AI’s economic potential is undeniable, with estimates projecting a US$15.7 trillion[1] contribution to the global economy by 2030. As businesses increasingly recognize its impact, 72% of organizations worldwide[2] have integrated AI into at least one business function, marking a significant rise in adoption. What were once heavily manual processes just a few years ago have now been radically transformed. A prime example is seabed boulder detection, where automation has accelerated processing by several dozen times, depending on data quality, seabed morphology and sediment type. By drastically reducing processing timelines, this advancement is directly improving the efficiency of offshore construction and monitoring.

AI adoption in offshore industries depends largely on the type and characteristics of data. AI is most effective in structured, numerical and repetitive datasets, such as bathymetry, coordinate-based spatial mapping and magnetometry anomaly detection, where patterns are well-defined and statistical learning can automate large portions of processing. However, more complex, interpretative and decision-based processes – such as sub-bottom profiling, seismic interpretation, geological assessments, hydrography-driven seafloor classification, marine geophysics surveys or real-time processing of seismic survey data during acquisition – still demand extensive human expertise. These processes require deep integration of multidisciplinary knowledge, making full automation challenging and in some cases, particularly in critical infrastructure monitoring or high-risk offshore operations, too high-risk for AI to be fully adopted without human oversight.

Strategic AI integration: enhancing, not replacing, offshore workflows

The key to unlocking AI’s full potential in offshore industry lies in assisted workflows, where AI enhances efficiency but remains a support tool rather than a decision maker. This distinction becomes particularly evident when comparing two approaches to improving process efficiency – the conventional and AI-based methodologies – which apply across various offshore operations, from construction, infrastructure establishment and monitoring to surveying, data analysis and processing.

The conventional approach is characterized as highly labour-intensive, time-consuming and dependent on expert interpretation, requiring significant manual effort and relying on proven, widely used methodologies, workflows, practices and software tools. This is visually represented in Figure 1 (left), where conventional processing follows a complex, winding route, reflecting the step-by-step nature of traditional methods. On the right side of Figure 1, the automated approach integrates automation at key points, making the entire workflow more efficient. This reflects the principle that AI is most effective when strategically applied to specific tasks rather than replacing human expertise entirely.

This approach is often a hybrid solution, combining automated processes with AI-driven enhancements to unlock its full potential. Automation introduces flexibility and adaptability, while AI, when trained on well-structured and properly modelled datasets, further optimizes and accelerates the process. By leveraging both, offshore data processing can achieve greater efficiency, reduced manual workload and improved accuracy, ensuring a streamlined and scalable workflow.

Ensuring reliability in AI-driven processes

The primary challenge in these processes is the need for rigorous quality control of AI-generated outputs. Ensuring data reliability requires deep expertise in specific scientific disciplines, making seniority and domain knowledge crucial for validation. At the same time, the hydrography, marine geophysics and seismic industries are evolving rapidly; particularly in renewables, where the demand for skilled professionals exceeds supply. This talent shortage further highlights the need for AI augmentation rather than replacement, ensuring that human expertise remains at the centre of critical decision-making while AI optimizes workflows and speeds up processing.

Structured data models for AI training

Training AI models requires well-structured and properly modelled datasets, rather than simply feeding large volumes of raw data. Without carefully prepared training data, AI struggles to generalize patterns, leading to false positives, misclassifications and unreliable predictions. High-quality datasets must include clear metadata, labelled features and diverse environmental conditions to ensure adaptability to real-world variability. Relying on unstructured data increases the risk of overfitting noise or missing critical anomalies, reducing reliability. The key to effective AI-driven automation is not just big data, but well-modelled, high-quality training data that accurately represents the target environment.

Regulation and standardization: closing the gap

The regulation and standardization of AI in the hydrography, marine geophysics and seismic industries should advance more rapidly to ensure alignment with legal frameworks governing ethics, privacy and security. Without clear guidelines, uncertainty will persist, slowing adoption, increasing risk and limiting AI’s full potential. A structured regulatory framework would provide clarity, build trust and create the conditions for responsible and effective AI deployment.

This process depends on cooperation between ethical and security regulators, international standardization bodies, offshore industry organizations and corporate leadership. Frameworks such as the EU AI Act and GDPR establish legal and ethical boundaries, ensuring fairness, transparency and data protection. However, without accompanying technical standards, compliance remains challenging in practice. International organizations responsible for standardization, such as ISO, contribute significantly to defining how AI should function in offshore environments, but their impact would be strengthened by industry-specific guidelines.

The offshore industry plays an essential role in shaping AI adoption, ensuring it aligns with safety protocols, operational efficiency and risk management. At the same time, leading offshore companies can help bridge the gap between regulatory requirements and real-world applications by fostering shared industry-wide principles. Without clear and consistent guidelines, inconsistencies in implementation will continue, creating hesitation among businesses looking to integrate AI into their operations.

Until a well-defined regulatory and standardization process is in place, many companies will approach AI adoption with caution due to legal uncertainties and operational risks. Accelerating this process would provide clarity, remove barriers and allow the offshore sector to embrace AI with greater confidence, unlocking its potential to enhance efficiency, safety and sustainability.

Conclusion

Over time, the application of AI will continue to yield increasingly reliable results, and we are already seeing improvements in efficiency and accuracy. However, the transition will not be immediate, as the high stakes and inherent risks in offshore operations make full automation unlikely any time soon. That said, we should remain optimistic and continue pushing boundaries, exploring new possibilities and accelerating AI adoption where feasible. By strategically integrating AI into workflows, we can enhance efficiency while maintaining expert oversight, ensuring that innovation drives progress without compromising reliability and safety.

The future of offshore AI will not be decided by technology alone, but by the people who learn how to use it best.

[1] PwC (2020). Sizing the Prize. What’s the Real Value of AI for Your Business and How Can You Capitalise?
[2] Statista. (2024, December 5). AI adoption among organizations worldwide 2017-2024, by type