The recent conflict in the Middle East has triggered a major supply chain crisis with wide-reaching impacts for global trade. Shipping companies, now more than ever, are focusing on how to increase efficiency and output in their dockyards amid the chaos.
The pressure to automate is immense, with AI presented as the solution to productivity bottlenecks. Yet as new research at Erasmus University reveals, the way we integrate this technology into ports does more than just accelerate cargo handling. It is fundamentally reshaping business management approaches, shifting decision-making authority, redefining the nature of skilled work, and creating new models for how organisations extract value from their workforce.
Productivity through learning
Across the port industry, AI is being deployed to boost productivity in ways that go far beyond traditional automation. Where previous technologies mechanised physical tasks such as lifting, moving, and stacking, AI targets the realm of information and decision-making, optimising vessel berthing, yard coordination, and shift allocation with speed no human planner can match.
AI’s true productivity gains lie in its capacity to learn from the workers themselves. Every time a crane operator makes an effective adjustment or a planner solves a complex logistical puzzle, AI systems can record these decisions, absorbing years of tacit expertise into algorithms that can later propose or impose new ways of organising work. This turns the workforce into an engine of continuous improvement for the very systems that may eventually reduce their numbers.
A new management paradigm
What makes AI distinct from earlier automation waves is how it changes business management approaches, rather than operational workflows. Management no longer oversees workers; it oversees algorithms that increasingly manage workers. AI systems are being used to allocate tasks, monitor performance, and even evaluate individual productivity, effectively inserting a layer of machine governance between managers and the workforce.
This shift enables organisations to standardise operations across global networks, reducing dependence on human judgement and ensuring predictable outcomes regardless of local conditions. In this new world, control over data and decision-making tools becomes as critical to competitive advantage as physical infrastructure, prompting port authorities to treat AI investment as a strategic imperative rather than a simple cost-saving measure.
The data-driven organisation
This transformation is giving rise to a new type of organisation, one where data itself becomes the primary source of value and strategic leverage. Port operators are increasingly viewing the data generated by vessel movements, cargo flows, and worker activity not as a byproduct of operations but as a core asset to be monetised.
The Port of Rotterdam, for example, launched PortXchange to commercialise its digital coordination platform and sell port-data services worldwide, while terminals such as Abu Dhabi Terminals have partnered with technology giants like Microsoft to deploy AI capabilities that strengthen their competitive position.
For management, this represents a fundamental shift in business model, from moving containers to managing information networks. However, this data-centric approach also creates new vulnerabilities, as decisions about scheduling, hiring, and performance evaluation become embedded in algorithms whose logic may be opaque even to the managers who deploy them.
Reshaping authority and accountability
As AI takes on more decision-making functions, traditional management structures are being quietly rewritten. When an algorithm determines shift patterns, flags workers for low productivity, or recommends staffing cuts, managers can defer responsibility by saying “the AI made the call.” This diffusion of accountability represents a profound change in how authority is exercised and justified within organisations.
For example, in Rotterdam, some terminal operators report that AI-driven systems could reduce planning staff by as much as 60%, although such projections often reflect the promise of the technology more than its demonstrated capabilities in practice. Management nevertheless tends to present these projected cuts not as strategic choices, but as unavoidable consequences of technological progress. Framed this way, technology becomes a rationale for pursuing aggressive efficiency gains while distancing leadership from the difficult conversations that accompany job losses.
Efficiency versus equity
The productivity gains from AI are real, but who benefits from them remains an open question shaped by management choices. When AI speeds up cargo handling, the savings can be passed to shareholders, reinvested in infrastructure, or used to reduce working hours while maintaining pay. The management approach taken determines whether technology augments workers or replaces them.
The Dockers’ AI Toolkit for the International Transport Workers’ Federation documents how AI systems sold under the banner of “augmentation” often result in net job reduction, with efficiency defined narrowly as labour savings rather than improved working conditions or sustainable operations. This reveals that AI implementation is not merely a technical decision but a managerial one, reflecting choices about whose interests the technology serves.
Lessons for other industries
The experience of ports offers a template for how AI is reshaping management across sectors. In logistics, manufacturing, retail, and even professional services, we see the same pattern emerging where AI is being deployed not just to automate tasks but to centralise decision-making, standardise operations, and convert worker expertise into machine-readable data.
Management approaches that succeed in ports, like prioritising transparency, involving workers in system design, and linking productivity gains to job protection, offer a model for other industries navigating similar transitions.
Conversely, the risks of exclusion, opaque algorithms, and unaccountable systems are equally transferable. As organisations everywhere race to capture the efficiency benefits of AI, the choices managers make today about governance, transparency, and equity will shape work for decades to come.
A source of shared prosperity
The supply chain crisis triggered by geopolitical conflict has made productivity a matter of urgency, but the rush to implement AI should not come at the cost of thoughtful governance. Our research demonstrates that when AI enters the workplace, it does more than change how work is done, it changes who decides, who benefits, and who bears the risk.
For managers across industries, the lesson from the docks is clear, AI is not simply a tool for boosting output but a force that rewrites organisational structures, accountability, and the very nature of skilled work. How organisations choose to manage this transition will determine whether AI becomes a source of shared prosperity or simply a more efficient means of concentrating control.
Bio:
José Luis is a PhD candidate in the Department of People and Organisations at Rotterdam School of Management, Erasmus University. Largely, his work focuses on how automation, AI and digital systems affect workers’ jobs, skills and ability to exercise voice in organisations. He is especially interested in settings where these changes are highly visible and contested, such as ports, logistics and telecommunications.
