Wolfgang Lehmacher argues that shipping stands at an inflection point as agentic AI, systems that not only predict but decide and act, moves from forecasting into operational control, creating a paradox in which software behaves like a colleague while remaining an asset on the balance sheet. According to the original report, this shift raises a fundamental question about accountability when machines prioritise speed, cost or carbon in ways that may conflict with safety or law. [1]
Three schools of thought have emerged for how to manage that shift. The “supertool” view keeps humans firmly in charge: algorithms recommend and automate routine tasks while people set objectives, interpret trade-offs and sign off decisions. The “digital coworker” framing treats agents as teammates with roles, KPIs and an “HR for agents” that assigns ownership and escalation rules. A third camp rethinks operating models from a blank sheet, giving agents responsibility for fleet, network and hinterland rebalancing while humans focus on resilience, relationships and stewardship. [1]
Practical deployments illustrate the trade-offs. The Port of Rotterdam uses platforms such as PortXchange and Pronto to combine public data, partner inputs and machine learning to predict arrivals, coordinate port calls and optimise yard operations, reducing waiting times and improving utilisation, yet responsibility for safety, commercial exposure and liability remains with port authorities, terminals and shipping lines. PortXchange provides the shared dashboard and APIs while Pronto applies self‑learning models to arrival data drawn from the port authority and AIS feeds. [1][4][5]
The operational benefits are clear, but technology leaders and consultants warn of new vulnerabilities. Industry playbooks stress that deploying agentic AI without robust safety, security and governance can disrupt operations, compromise data and erode trust. McKinsey’s guidance underlines trust as the foundation and sets out lessons for safe scaling, while consultancies such as Capgemini emphasise governance frameworks, integration routes (off‑the‑shelf, custom or embedded agents) and ethical controls to manage accountability and compliance. [2][3][6][7]
Legal analysis and maritime studies underline persistent grey zones: no matter how autonomous a system becomes, it does not become a moral or legal person, and humans remain accountable when harm occurs. Regulators, class societies and ethicists continue to demand “seaworthy” human oversight, even as operators argue that insisting on manual signoff may simply preserve outdated hierarchies and forgo efficiency gains. This tension makes clear why boards, not just vendors or project teams, must own choices about models, data and guardrails. [1][2]
The pragmatic path Lehmacher favours is to let AI orchestrate flows at machine speed while treating it as a tool: assign clear ownership for each critical agent, codify escalation and override rights, and cultivate a culture that views every AI decision as the outcome of prior human choices. According to the original report, as agentic systems increasingly run ships and ports, one question will grow louder: when the system acts, who will stand up and say, "I am in charge"? [1]
📌 Reference Map:
##Reference Map:
- [1] (Splash247) - Paragraph 1, Paragraph 2, Paragraph 3, Paragraph 5, Paragraph 6
- [4] (Port of Rotterdam / PortXchange) - Paragraph 3
- [5] (World Ports / Port of Rotterdam Pronto) - Paragraph 3
- [2] (McKinsey) - Paragraph 4, Paragraph 5
- [6] (McKinsey) - Paragraph 4
- [3] (Capgemini) - Paragraph 4, Paragraph 6
- [7] (Capgemini) - Paragraph 4
Source: Noah Wire Services