Adoption is accelerating, but maturity remains limited

Artificial intelligence is gaining ground across the shipping industry, from charter party reviews and procurement to voyage planning and operational management. However, wider adoption does not necessarily translate into greater efficiency or stronger competitiveness.

The 2026 report Earning Trust: AI in Maritime, produced by Thetius in partnership with Marcura, surveyed 60 maritime professionals. It found that 63% used AI daily, while 72% said their organisations were using, piloting or planning to use agentic AI. Yet only 15% believed their organisations had governance arrangements ready for these systems.

The findings highlight a growing gap between technological adoption and organisational maturity. For shipping companies, the challenge is shifting from gaining access to AI towards integrating it into business processes in a controlled and commercially meaningful way.

Productivity must be measured in business results

One of the report's key concepts is the "verification tax": the time and effort required to check and correct AI-generated outputs before they can be used.

Although 55% of respondents spent at least an hour a week checking AI outputs, 85% still believed AI saved them more time overall. Another 73% said AI enabled them to perform tasks they could not previously undertake.

These figures suggest that AI can deliver productivity gains, but its value cannot be measured simply by the speed of generating information.

For a shipowner, an AI system that accelerates supplier comparisons or contract reviews is commercially useful only if the overall process becomes faster, more accurate or less costly. If extensive manual checking offsets the time saved, the expected return on investment becomes questionable.

Companies therefore need to assess AI against measurable outcomes, including labour hours, processing costs, error rates and decision quality, rather than the number of employees using AI tools.

Integration matters more than the number of tools

The next source of competitive advantage is likely to come from integrating AI into core commercial and operational workflows.

The report identifies several examples. Tomini Chartering is implementing an AI-supported charter party system that draws on historical contractual experience to identify unusual wording and missing clauses. Marcura applies AI to supplier selection and the matching of procurement documents. ZeroNorth's Propel platform uses AI agents to support voyage planning and update recommendations based on feedback from vessel masters.

These applications illustrate a shift from using AI for individual tasks towards connecting information and decisions across business processes.

This distinction is particularly important for shipping companies. General-purpose AI can improve everyday productivity, but more complex applications require access to reliable operational data, historical company records and existing business systems.

The survey found that 42% of respondents relied primarily on general-purpose AI providers, compared with 10% using specialist maritime vendors. This suggests that specialised maritime applications remain a relatively small part of the reported AI landscape.

The opportunity is not necessarily to purchase more software, but to make existing company data and operational knowledge more accessible and useful.

Governance is becoming a business requirement

Greater automation also brings greater exposure to risk.

The survey found that 48% of respondents reported at least occasional use of unofficial AI tools within their organisations. Meanwhile, only 35% said their companies had formal AI policies, and just 25% said employees actually followed them.

For shipping companies, this creates practical concerns over commercial confidentiality, data security and decision-making authority.

An AI system may help review a charter party or recommend a supplier, but who approves the final decision? If an AI agent takes an incorrect action, who is responsible for the resulting financial or operational consequences?

Notably, 80% of respondents believed that a named individual should always remain accountable for a commercial decision, regardless of AI's capabilities.

AI governance should therefore be treated as part of corporate risk management, rather than simply an IT issue. Companies need clear rules covering data access, authorised tools, human approval and the limits of automated actions.

What should shipping companies measure?

The findings point to three practical indicators of AI maturity.

First, measurable economic returns: whether AI reduces operating costs, saves labour hours or improves the quality of commercial decisions.

Second, operational integration: whether AI is embedded in core workflows rather than being used only for isolated tasks.

Third, governance and accountability: whether companies can control AI-generated decisions, protect sensitive information and identify who is responsible when something goes wrong.

The survey's relatively small sample means its findings should be treated as an indication of industry developments rather than a representative measure of global shipping. Nevertheless, it highlights an important management challenge.

As access to AI technology becomes increasingly widespread, the ability to use it effectively will matter more than simply adopting it.

The next competitive divide in shipping may not be between companies that use AI and those that do not, but between those that merely adopt the technology and those that can turn it into measurable business performance.