Shipping technology and freight markets: a review of 2025 and signals for 2026 in International Shipping News 05/01/2026 Understanding freight markets has always required piecing together information from multiple and often fragmented sources. From rates, vessel supply, availability, to cargo demand and port congestion, you can rarely find these sources in a single, structured dataset. Instead, most of the sources and data about such valuable commercial intelligence lie scattered across thousands of emails, WhatsApp and Teams conversations, that by themselves, offer but little value. But, when combined in a structured form, all pieced together and analyzed, you see that it can be the most valuable database, one that will bring you much closer to decoding the market. This is how AI in shipping technology begins to change how freight developments are interpreted. Modern machine learning and language-processing techniques allow for large volumes of unstructured communication to be analyzed systematically for the first time, whether it is formal emails, or informal conversations. So, instead of treating communication as isolated fragments of the market, technology helps us find patterns and uncover underlying market dynamics. Shipping conversations follow a unique language of their own, from abbreviated words or whole sentences to implied meanings, and the industry-specific phrasing that can signal actual commercial activities, vessel availability, or market sentiment, long before they appear in reports or any form of formal data. By learning these conversation patterns, AI can help structure scattered information into coherent insights, revealing how the supply and demand balance across regions and vessel classes. When combined with traditional datasets, such as vessel movement and port activity, this structured view of communication adds an important layer of context. It allows market participants to have a deep understanding of not only events in freight markets, but how and why conditions are changing. In an environment that is being shaped by rapid information flow and information complexity, the ability to organize and interpret fragmented signals is becoming central to understanding freight developments in real time. From Structured Insight to Smarter Decisions Once data and shipping intelligence has been structured and contextualized, a real opportunity arises, and it is based on how this intelligence is delivered and used by shipping professionals in their day-to-day work. Historically, analyzing data to support decision making required time, experience and manual effort. Analysts prepared reports, brokers mentally compared multiple scenarios, and commercial teams relied heavily on intuition built over years in the market. While this expertise and experience-based intuition remains irreplaceable, the growing volume and speed of information make it increasingly difficult to process everything effectively. This is where shipping technology is now entering a more evolutionary phase. With freight signals already structured and analyzed in the background, technology can begin to surface insights in ways that are more intuitive, timely, and aligned with how people actually make decisions. The focus swifts more into answering commercial questions directly, helping users to export alternatives, compare scenarios, and understand trade-offs in each case. As this evolution is taking place, there is a noticeable shift happening. The use of AI