AI moves from experimentation to a more mature phase

Approximately six out of ten shipping companies and maritime organizations have already launched pilot programmes or are experimenting with the integration of Artificial Intelligence.

However, only 7% have progressed to the next stage — namely, the full-scale and structured deployment of AI technologies. This figure perhaps best illustrates the current position of the shipping industry, caught between enthusiasm over AI’s potential and the challenge of transforming it from a collection of standalone tools into a fully operational business infrastructure.

This is also the central conclusion of a study conducted by Veson Nautical on the foundations of artificial intelligence in shipping.

The study argues that the sector is moving beyond the experimentation phase into a more mature stage, where the critical question is no longer whether AI will be adopted, but rather how it will be integrated into data ecosystems and operational decision-making frameworks. In an industry facing mounting pressure from geopolitical disruptions, stricter environmental regulations and increasingly complex commercial decisions, artificial intelligence is increasingly emerging as an issue of competitiveness rather than merely technological innovation.

According to the available data, 62% of companies are attempting to address specific use cases, without yet integrating AI into the core of their operations.

Veson warns that experimentation can easily lead to fragmentation, with multiple overlapping tools, inconsistent datasets and reduced confidence in outcomes.

Despite these challenges, the study indicates that artificial intelligence is already generating measurable value across complex areas of maritime operations.

The first area concerns unstructured communications, cargo offers and vessel position updates, from which AI systems can extract critical commercial intelligence.

The second relates to the cross-referencing and analysis of contractual data.

The third involves improving the financial evaluation of voyages and freight rates through scenario analysis combining historical and real-time data.

The fourth concerns the delivery of market insights and trend analysis embedded directly within the workflow, eliminating the need for separate research processes.

As Veson explains that AI operates across three distinct levels. The first is the most visible layer, involving automation and systems that transform the way day-to-day work is carried out.

The second level is reached when AI ceases to function merely as an assistant and begins delivering reliable outputs aligned with the actual operational dynamics of the shipping industry.

The third level concerns the underlying data infrastructure, governance and compliance framework, without which even the most advanced AI model cannot provide solutions that shipping companies can fully trust.

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