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, 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.