Artificial Intelligence (AI) is rapidly moving from the periphery of maritime innovation to the centre of operational strategy. What was once viewed as an experimental add‑on, AI is now becoming embedded across shipping operations, supporting decision‑making, forecasting, and emissions monitoring.
According to Thetius research commissioned by LR, the maritime AI market was valued at USD $4.13 billion in 2024, and is expected to grow at a compound annual rate of 23% over the next five years. The latest data shows AI adoption in maritime is accelerating, with 420 organisations active in maritime AI developments in the last year alone, up from 276 a year earlier. This surge shows AI is shaping the competitive landscape of global shipping.
Across the industry, shipping companies are turning to AI solutions for voyage optimisation, fuel efficiency, predictive performance analytics, and emissions management. Major fleet operators already demonstrate the scale of impact: Maersk processes over 2.5 billion data points annually to model weather, currents, and vessel performance, improving routing and reducing fuel consumption. NYK in collaboration with LR show how machine‑learning diagnostics can shift fleet operations from reactive to proactive, predicting equipment degradation before failure, which not only improves safety and reliability but also reduces unplanned downtime—an area of clear financial impact.
Embrace of technology
Greek shipping, responsible for around 20% of global deadweight capacity, is also beginning to embrace AI technologies to strengthen operational performance. A recent AI readiness survey, conducted by AMMITEC, shows that Greek operators recognise AI’s potential but also face barriers relating to data maturity, workforce skills, and integration across systems and departments.
While AI’s potential is significant, its impact depends entirely on one factor: data. Fuel, voyage, engine, and emissions data must be accurate, structured, and connected. Without this foundation, AI generated insights risk becoming fragmented, unreliable or operational irrelevant.
This is where many organisations encounter challenges. Successful AI adoption requires more than technology procurement. It hinges on investment in people, processes, governance, and long‑term strategic alignment. AI initiatives cannot exist in isolation from corporate strategy; when digital transformation efforts remain siloed, organisations often struggle to generate meaningful returns on investment.
Digital maturity
To help address this gap, LR developed the Digital Maturity Index (DMI), a structured framework designed specifically for maritime sector, offering a data‑driven view of an organisation’s readiness across the key enablers of digital transformation: connectivity, strategy, cybersecurity, culture, and standardisation/integration. With more than 50 maritime‑specific use cases embedded, the DMI also tracks adoption levels of technologies such as AI‑driven analytics, which currently ranks as the top‑scoring technology with an average score of 1.73 out of 4.
Given the rapid uptake of AI, LR has now extended the DMI framework to include a comprehensive 360-view assessment of AI maturity. This enhanced evaluation covers AI readiness, adoption levels across use cases, partner ecosystem landscape, and the perceived business value from existing AI deployments. For shipping companies, these insights give critical clarity, revealing how prepared an organisation truly is for AI, identifying barriers to progress, and highlighting the strategic steps required to maximise long-term impact.
Impact on operations
Importantly, this framework also shows whether the workforce’s skills, mindset, and confidence are aligned with the organisation’s AI ambitions. AI capability is as much about people as it is about systems.
In an era defined by volatility—geopolitical tensions, regulatory demands, market fluctuations—shipping companies must be flexible, informed, and resilient. AI and data‑driven systems, when integrated into business strategy, supported by strong governance and agile process redesign, and underpinned by high‑quality data, can become powerful enablers of smarter decision‑making, operational efficiency, and long‑term competitiveness.
