This insight examines how maritime organisations progress along the AI maturity curve, why many stall at early stages, and what prevents AI from becoming operationally embedded.
Organisations typically travel along an AI maturity curve that progresses from awareness to maturity. Recent research by Thetius found that while 81% of respondents are aware of AI and have run pilot projects, only 33% have progressed beyond these into operational use, suggesting many companies stall at the early stages.
This is largely due to a lack of clear use cases, enthusiastic leadership but sceptical frontline workers, conflicting interpretations by AI users, and the difficulty in actually achieving the suggestions made by AI.
Awareness is the stage where the organisation recognises AI’s potential and is exploring its relevance, often through early discussions or appointing a digital lead, but with no concrete projects yet. At this stage, AI is still conceptual and disconnected from daily operations.
Experimentation follows, where the company pilots AI on a small scale to learn and build confidence. For example, testing an AI-based weather routing tool on a few vessels. Many maritime firms are currently at this stage. While experimentation builds familiarity, it does not guarantee adoption.
Adoption occurs following successful trials, when AI is deployed more widely and integrated into workflows. Some maritime companies are now realising tangible benefits such as better decision-making, while managing challenges of scaling and change.
Maturity is reached when AI becomes embedded in operations and strategy, supported by in-house expertise and governance. The organisation continuously refines its models, adds new capabilities, and measures value through business performance and continuous improvement.
However, effective collaborations between AI and humans are still challenged, preventing organisations from moving along the maturity curve. Aligning machine outputs with human judgement, trust, and safety-critical decision-making remains a bigger challenge to AI adoption in voyage optimisation than technical limitations.
True operational intelligence comes from integrating AI’s data-processing power with human contextual reasoning. However, black-box models, poor data quality, and limited maritime-informed design undermine trust and hinder this balance. Siloed data, fragmented ship-to-shore communication, skills gaps, and low organisational agility prevent companies from moving beyond pilots and realising the full value of AI tools.
Without transparency, robust data governance, maritime expertise, and effective human oversight, AI systems risk amplifying errors, creating over- or under-reliance, increasing cyber vulnerabilities, and ultimately restricting their performance and return on investment.
To explore how a balanced human-AI approach can strengthen voyage optimisation in safety-critical conditions, read the full Thought Leadership report ‘Co-Pilots of the Sea: Exploring the Human Intelligence Behind Maritime AI’ produced in partnership with StormGeo.
