Executive Commentary |
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predicting outcomes, managing exceptions and transforming visibility into actionable intelligence and real-time decision-making. Downstream, these connected toolsets enable automated event management, billing, auditing, payment and reconciliation.
This turns digital logistics into a self-improving, adaptive ecosystem, one where visibility fuels intelligence and intelligence drives action.
PSA BDP
Neil Wheeldon
Chief Digital & Innovation Officer www. psabdp. com
AI is quickly reshaping how global logistics operates, but in ocean shipping its impact is more evolutionary than revolutionary. The maritime world is defined by physical limits: port congestion, weather and vessel schedules as well as regulatory complexity. No algorithm can rewrite those fundamentals. What AI can do is bring unprecedented foresight to a system long driven by reaction.
Predictive analytics are already helping carriers, terminals and shippers anticipate bottlenecks. By combining automatic identification systems data with weather, port call history and congestion trends, AI models are producing more reliable ETA forecasts and disruption alerts. Machine learning tools are identifying patterns in vessel behavior and port throughput that even experienced operators can miss. Generative AI is starting to transform the documentation burden, extracting, classifying and summarizing the flood of unstructured data that clogs supply chains every day.
These advances matter most when AI is embedded in operational systems rather than treated as a bolt-on feature. Integrated with network planning, capacity management, booking management and risk modeling, AI can drive measurable efficiency gains and faster response cycles. But it still faces limits. It can’ t create berth space, bend physical capacity or guarantee the accuracy of its own predictions. And it can’ t replace human judgment,
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“ The conversation about AI implementation should begin with data quality.”
Niels Kristiansen
“ What AI can do is bring unprecedented foresight to a system long driven by reaction.”
Neil Wheeldon
“ Automation capabilities are the other core value of visibility optimization.”
Jared Weisfeld
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particularly in volatile or crisis conditions where experience remains the best safeguard.
RXO
Portchain
Niels Kristiansen
Co-founder and CEO www. portchain. com
Artificial intelligence is applied in shipping to streamline operations, analyze large data sets and support decision-making across complex logistics networks. While progress is underway, the impact of these tools depends on the quality of the data: AI is only as reliable as the data it relies on. Without a foundation of accurate, timely and standardized information, even the most advanced models will struggle to deliver actionable outcomes.
Before investing in any kind of optimization or prediction capabilities, carriers and terminals should address the fundamentals: the quality,
Jared Weisfeld
Chief Strategy Officer www. rxo. com
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Supply chain visibility has become a commodity. Knowing the precise location of a truck or container is now the minimum requirement for operating in modern logistics. If passive data observation is commoditized, the question becomes: where does technology add exponential value?
The answer lies beyond data collection to leveraging supply chain technology for proactive intelligence, predictive modeling and autonomous execution— transforming data from
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structure and timeliness of operational data. True optimization begins with accurate, real-time information from vessel schedules, berth availability and terminal operations. The complexity and number of parties involved in vessel scheduling and berth alignment create enormous potential for error. Data remains fragmented across emails, spreadsheets and legacy systems. When arrival times, move counts or terminal updates are not synchronized, decision-making becomes reactive instead of predictive. In this context, the conversation about AI implementation should begin with data quality.
When accurate data is connected and shared in real time, carriers and terminals can collaborate on a shared version of the truth and respond to changes faster. Building this foundation is essential to improving predictability and efficiency across the industry.
a historical record into real-time, optimized decision-making. As AI advances in complexity and capability, it is table stakes for businesses to identify the right tools that enhance their operations and drive better business outcomes.
Advanced platforms using machine learning can ingest vast datasets— including historical performance, market fluctuations, weather patterns and port congestion— to predict trends and outcomes. This allows logistics professionals to anticipate disruptions, capacity crunch points and pricing volatility days or even weeks in advance, uncovering proactive updates to strategies that avoid or minimize disruptions. This shift from reactive to proactive risk mitigation is the key to modern operational efficiency.
Automation capabilities are the other core value of visibility optimization. By automating repetitive tasks such as truck check-ins and dynamic rate setting, business leaders can free up employees’ time to focus on more strategic tasks like building relationships with customers and providing solutions.
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