As interest in artificial intelligence (AI) continues to grow across the maritime industry, many organizations are exploring how the technology can be applied in practice. At the same time, questions remain around data quality, workforce readiness, trust and the practical challenges of scaling AI beyond pilot projects. In this Q&A, Dr. Piyush Raj, Head of Maritime Technology & Innovation, DNV Maritime Advisory, and trainer at DNV Maritime Academy (Singapore), shares his perspectives on the industry’s evolving AI landscape and the skills maritime professionals will need in the years ahead: MT: AI has become an increasingly common topic across the maritime industry. Based on your interactions with maritime organizations, what are some of the key challenges they face when exploring or applying AI? From my experience, the challenge when maritime organizations look to implement AI tools is building the foundation for it to produce useful results, rather than the implementation or use of the technology itself. Many organizations have large amounts of data, but struggle with data quality and standardization. On top of that, there are governance, cyber security, and control and assurance issues, both of the data and of the tools and systems themselves. Even when we have something that looks promising, there can be scaling challenges. Moving beyond a proof-of-concept or pilot project to a broader operational or fleet level requires a whole new approach in terms of organizational buy in, process integration, and trust within and beyond the organization itself. This is because maritime is a safety critical industry and safety is the backbone of how shipping has created the modern global economy. If we lose confidence in the safety of our industry, we’ve lost everything. So, we need to be sure that as AI adoption increases, we have a firm basis for demonstrating that these systems are just as reliable, secure, transparent, and aligned with regulatory expectations