The maritime industry stands at a turning point. While enthusiasm for Artificial Intelligence (AI) is strong, adoption is uneven and often hindered by fear, mistrust, or lack of readiness.
Through Thetius latest research on AI, we have identified several key points to help the maritime industry balance general AI guidance with industry-specific needs. The following recommendations aim to ensure that solutions are trusted, effectively integrated, and embraced by maritime stakeholders today.
1.Invest in AI Tools Built Specifically for Maritime
AI tools need to be able to understand specific maritime nuances. Generic AI may assist with routine automation, but it often fails to interpret the contractual, operational, and contextual subtleties that define maritime work. Tools trained specifically on sector data and workflows, such as charter party clause analysis or regulatory flagging, are far more likely to deliver meaningful value.
If you are an end user, prioritise AI tools and platforms that are designed and trained for maritime operations. If you are a solutions provider, there is an opportunity to show how your solutions go above generic AI and meet maritime-specific needs.
2. Foster Agency and Discernment
Organisations should invest in training programs that enhance employees’ agency and discernment to navigate the AI landscape effectively. Employees need comprehensive training programs that help them to understand, assess, and effectively use AI tools, especially if they haven’t had a chance to engage much with AI previously. Leaders need to demonstrate to their employees how AI is a co-pilot, not a new member of staff.
3. Keep the Human in the Loop to Harness Trust
Trust in AI is built when people understand its purpose and see its value in action. Human oversight is critical for users to assess the potential benefits of AI and feel confident in applying it safely and effectively.
AI projects need a clear purpose and leadership. Where trust is low, start with decision-support tools that keep humans firmly in charge. Systems that allow experts to audit, override, or question AI outputs help preserve professional judgement and confidence. AI tools should assist and guide those with advanced knowledge and experience to make decisions more efficiently, rather than making decisions autonomously.
4. Engage with Emotions, Not Just Systems
AI adoption is not just a technical process; it’s an emotional journey. Excitement, resistance, or anxiety can vary by department, seniority, or role, and these emotional responses directly affect behaviour. Identify where resistance is highest and tailor engagement and training accordingly. The most successful adoption strategies prioritise empathy as much as efficiency. Acknowledge fear, reward curiosity, and create space for people to express uncertainty without stigma.
5. Implement Governance Frameworks
Organisations must establish governance frameworks to manage AI tools effectively, ensuring compliance with data policies and ethical standards. These frameworks should cover not only compliance, but also transparency, auditability, and internal accountability. Governance is not just about risk; it’s about enabling safe and confident use. Clear policies give both leaders and employees confidence that AI solutions operate transparently and responsibly.
6. Demand Transparency and Real-World Impact from Vendors
AI should be applied to address genuine operational challenges rather than simply following industry trends. If you are a shipping company looking to invest in AI, avoid being distracted by vendor hype or adopting AI technologies that are not genuinely beneficial to your specific business needs.
Urge your vendors to provide full transparency and maintain an open dialogue, especially when dealing with complex data. Avoid being swayed by industry hype and insist on detailed explanations from technology providers about their AI’s capabilities, training data, and operational limitations. Focus on solutions proven to address actual operational challenges, deliver measurable value, and respect data privacy.
7. Encourage Experimentation
Promote a culture of creativity and innovation by allowing employees to experiment with AI tools while ensuring they understand the ethical implications. Employees need time to experiment with AI solutions without the fear that mistakes will be held against them. Those who will be using the tools need to be creators, not just consumers.
Encourage feedback loops between frontline teams and technology owners. If tools aren’t delivering value, treat that as an opportunity to refine, not a failure to adopt. Structured experimentation in safe environments allows both trust and utility to grow over time.
For further insight into the areas highlighted in this article, download our thought leadership report, Beyond The Hype, created in partnership with Marcura.
