WORLD FREIGHT FORWARDERS AIR CARG O WEEK


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TOP AI TRENDS SHAPING FREIGHT FORWARDING IN 2026


BY Anastasiya SIMSEK 08 A


“AI can help identify optimal transport modes to reduce emissions.”


rtificial intelligence (AI) and predictive analytics are no longer buzzwords in freight forwarding. As we enter 2026, they have become essential tools reshaping how the logistics industry operates, delivers, and adapts to a world of growing complexity and unpredictability.


For global freight companies like DHL, AI is at the heart of operational transformation.


From real-time predictive optimisation and risk assessment, ETAs to smarter route these technologies are unlocking


unprecedented levels of efficiency, transparency, and responsiveness. One of the most significant changes driven by AI in logistics is the


shift from reactive operations to predictive planning. Traditional business intelligence methods


are being replaced with advanced


analytics, which extract insights and foresight from vast amounts of logistics data. These tools allow freight forwarders to forecast delivery times,


optimise routes, anticipate demand fluctuations, and even manage risks related to weather and congestion. Rather than reacting to problems after they occur, AI allows companies to make proactive decisions that prevent disruptions and reduce costs. This is especially relevant in ocean freight, where unpredictability has


historically plagued planning. Inaccurate or inconsistent estimated times of arrival (ETAs) have made it difficult for shippers to coordinate port pickups, warehousing, and downstream production.


Smart ETA: DHL’s predictive analytics solution To tackle this long-standing challenge, DHL has developed its own predictive analytics solution, Smart ETA. It’s a machine learning-based forecasting engine that generates near real-time arrival predictions for port-to-port ocean shipments. The service is available for free to DHL Global Forwarding customers via the myDHLi digital platform. The first Smart ETA is calculated when a vessel departs. Using historical


carrier data for that route, the system generates an initial prediction. As the journey progresses, it integrates GPS positioning data via an Automatic Identification System (AIS) and frequently updates the ETA. It also harmonises multiple data sources to reduce reliance on conflicting or inaccurate schedule updates. This approach provides a single source of truth and ensures customers receive reliable, transparent information about when their shipments will arrive.


ACW 12 JANUARY 2026 www.aircargoweek.com predictive Behind the scenes, AI systems are digesting massive volumes of


logistics data. Using algorithms that learn from patterns, these systems are continuously improving the accuracy of predictions. The more data the system processes, the better it becomes. Predictive analytics draws from methodologies such as statistical


analysis, machine learning, data mining, and predictive modelling. These tools are now being applied not just to ETA forecasting, but to a wide range of freight forwarding challenges. For example, AI can help identify optimal transport modes to reduce


emissions, anticipate customs delays, or dynamically reroute shipments to avoid congestion. As supply chains become more digitised, the ability to harness this intelligence becomes a key competitive advantage.


Data quality - the critical factor However, the power of AI depends on the quality of the underlying data. Clean, high-quality data is essential for generating accurate insights. DHL emphasises that reliable predictive analytics requires robust data governance frameworks to ensure integrity and compliance. That includes not only data cleaning but also careful integration into


existing IT infrastructure. For many freight forwarders, this represents a major


challenge, requiring technical investment and operational


coordination. Without strong data foundations, even the most advanced AI tools can falter. Privacy and security are also central concerns. As data flows from


multiple sources, it must be protected from breaches and unauthorised access. Building trust with customers depends on this. While Smart ETA is already improving supply chain visibility today, DHL


is planning for its evolution. The system runs on a supervised machine learning operations model that is constantly being refined. The goal is not just to predict one arrival point but to forecast the entire shipment lifecycle with greater accuracy. This includes port stops along the route and estimating the probability


of making scheduled transits. In the long run, DHL aims for a fully predictive approach that supports end-to-end planning. Looking ahead, DHL is also exploring how generative AI (GenAI) can


support operations. In the near future, freight forwarding professionals might use GenAI to generate reports or compare shipments simply by asking a chatbot – without requiring deep expertise or training.


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