AI in Freight Forwarding and Transportation

AI is not a single technology but a broad collection of approaches, methods, and systems that demonstrate intelligent behavior (reasoning, learning, and problem-solving) across different contexts and applications.

Logistics and AI are a natural fit. The industry relies on complex physical and digital networks that must operate seamlessly despite high shipment volumes, tight margins, limited assets, and strict delivery timelines. Rather than depending solely on human decision-making, companies can leverage technologies such as language models to augment human expertise. This combination enables organizations to unlock significant efficiency gains by processing and acting on vast amounts of data that would otherwise go unused due to time constraints or the limits of human cognition.

AI in the Middle-Mile of Logistics: Top 3 Use Cases 

Logistics is one of the very few industries that has chosen to stick with old technologies, but now it can't stay on the sidelines while every other niche explores artificial intelligence. And it's no wonder—it seems as if the entire business world now revolves around AI exclusively. One of the main issues with that is that the abbreviation has become a go-to buzzword in both the corporate and consumer worlds, straying from its original meaning and the benefits it can offer businesses. 

However, the middle mile of logistics (freight transportation by road, rail, sea, and air) has seen many useful applications of AI to move goods more efficiently. Below are some of the most impressive real-world examples of its use.

Route Optimization 

One of the most impactful applications of AI in freight forwarding is intelligent route optimization. By analyzing live traffic conditions, weather patterns, and operational constraints, AI enables shipments to be planned with maximum efficiency. As highlighted by Jenkar Shipping, modern AI systems can process vast volumes of historical and real-time data to recommend faster, safer, and more cost-effective transport routes.

For example, UPS's ORION system uses AI to continually re-calculate delivery routes for its drivers, evaluating up to 30,000 route options per minute to minimize distance and fuel. This has reportedly saved UPS 38 million liters of fuel per year and cut thousands of tons of emissions. 

Similarly, DHL employs a machine-learning-based platform for dynamic route planning, which has reduced delivery times by approximately 25% while automatically rerouting vehicles in response to real-time conditions. These systems illustrate how AI turns what used to be static, manual route plans into dynamic, data-driven processes, yielding faster and greener transport.

Predictive Maintenance

One of the most revolutionary ways AI will change logistics is through predictive analytics, enabling shippers and carriers to be proactive rather than reactive. Naturally, being proactive means companies are better in control of their operations and assets, increasing revenue opportunities and providing cost-saving benefits.

Fleet operators now equip vehicles, vessels, and aircraft with smart sensors that track engine status, motion, and internal climate. These devices feed continuous streams of information to AI designed to detect patterns linked to future malfunctions. Instead of reacting to failures after they happen, businesses act earlier, fixing parts just in time. This shift means fewer surprise stoppages and less expense from idle machinery.

For example, Maersk (ocean shipping) leverages AI-based predictive models trained on sensor data from more than 700 vessels to predict equipment failures with 85% accuracy up to 3 weeks in advance, reducing vessel downtime by 30% and saving approximately $300 M per year. FedEx has also implemented predictive maintenance AI that predicts truck failures at least 78 hours in advance, resulting in a 22% reduction in vehicle downtime. These use cases demonstrate how AI can increase reliability and asset utilization in logistics.

AI is also facilitating freight matching and improving network capacity utilization. Digital freight exchange providers (also called "capacity marketplaces") use AI to connect shippers' freight with carriers' capacity in real time. It reduces empty miles (trucks running empty) and increases load efficiency. Such AI-based matching can eliminate up to 45 percent of empty truck drives, thereby slashing fuel waste and costs, industry reports said. XPO Logistics, for example, deployed an AI freight-matching platform that automates 99.7% of load assignments and reduces transportation costs by 15%. 
 

Autonomous Trucks

Maybe the most headline-grabbing development in freight is the rise of autonomous vehicles (AVs) for logistics. Self-driving truck technology has moved beyond prototypes in 2025 and is now being road-tested. Companies such as Waymo Via, Aurora, TuSimple, and Kodiak Robotics are operating Level 4 autonomous trucks on long-haul routes. Initial results are promising: Aurora (partnering with FedEx, among others) has completed more than 7,000 pilot hauls covering nearly 2 million miles with its self-driving trucks, and Kodiak (with J.B. Hunt) has driven 50,000+ miles autonomously delivering freight with 100% on-time performance and zero accidents in 2024.

Although fully driverless operations at scale remain to be seen, these tests demonstrate the potential for computer vision in logistics to improve productivity and alleviate driver shortages in the near future. Startups are not the only ones developing AI-driven ADAS (advanced driver assistance systems) for new trucks to automate highway driving in supervised mode; established OEMs (e.g., Daimler, Volvo) are, too. 

On the rail and maritime front, AI is enhancing network scheduling and asset tracking. Railroads are already employing AI to help prioritize train scheduling and routing in busy networks. Ocean carriers utilize AI for stowage optimization (the placement of containers on ships to balance and facilitate easy access) and for predicting port congestion or weather interruptions that allow for re-routing. For instance, port operations digital twin models can run simulations of various loading sequences or vessel schedules using AI to identify the most efficient outcomes.

In air cargo, AI supports dynamic flight scheduling and capacity demand prediction. Even air traffic control and drone routing (as discussed later) are benefiting from AI algorithms to ensure safety and efficiency. 

Note: Numerous commercial solutions focus on optimizing transport. Oracle and Blue Yonder (JDA) provide AI-based logistics management solutions for multi-modal shipments. Startups like Optimal Dynamics (with fleet planning based on reinforcement learning) and Convoy/Uber Freight (digital brokers using AI for pricing and matching) are gaining traction. Predictive analytics for fuel efficiency is yet another micro-niche – AI models can advise drivers on fuel-optimal driving and recommend refueling stops that minimize costs. 

AI-Driven Routing Strategies

Traditional road freight operations often depend on static routes, manual planning, and reactive decision-making, which can lead to longer transit times, higher fuel consumption, and rising operating costs.

AI-driven routing transforms this model by using machine learning, real-time data, and predictive analytics to continuously optimize route planning. Rather than relying on fixed schedules or predefined paths, AI systems adjust routes in real time. Taking into account traffic conditions, weather events, delivery priorities, and road hazards, it enables more agile and efficient operations.

Freight Optimization Across the Network

Intelligent freight optimization applies advanced analytics and AI to optimize resource use throughout the logistics chain. This means that every truck, container, and driver becomes part of a broader optimization framework designed to minimize delays, reduce empty miles, and deliver end-to-end cargo efficiency. AI models account for constraints including vehicle capacity, delivery windows, weight limits, driver availability, and traffic patterns to identify the most effective options.

AI also plays a key role in improving vehicle utilization. Dynamic load-matching algorithms help prevent empty return trips by pairing outbound deliveries with backhaul opportunities. This approach lowers fuel consumption and emissions while increasing asset productivity and profitability.

Container management also benefits from AI-driven predictability. Sensors monitoring load conditions, temperature, and capacity feed real-time data into optimization models that determine where and when cargo should move. For instance, perishable goods can be automatically rerouted to avoid congestion or excessive heat exposure, protecting product quality and reducing spoilage.

Autonomous Routing

Autonomous route planning refers to AI-enabled systems that allow vehicles and logistics platforms to independently select and update optimal delivery routes. Unlike traditional automation based on fixed rules, these systems rely on continuous learning, feedback loops, and historical data to make adaptive decisions.

From a technical perspective, autonomous routing combines machine learning, real-time GPS data, onboard sensors and cameras, and environmental mapping. Together, these inputs allow systems to assess road conditions, anticipate delays, and proactively respond to disruptions without human intervention.

Imagine a delivery truck equipped with an autonomous navigation system that detects a road closure or traffic incident and reroutes itself in real time. Drawing on satellite data, IoT sensors, and weather feeds, the vehicle automatically adjusts its path, reducing reliance on dispatch centers while improving delivery reliability and safety.

Note: Real-world deployments demonstrate the maturity of this technology. Logistics operators in Europe and Asia have piloted AI-based route planning systems that adapt mid-journey, achieving delivery speed improvements of over 15%. Some automated fleets have also reported up to a 40% reduction in idle time and a 25% increase in scheduling accuracy. Despite these advances, there are areas where AI can’t help you. Data integration, regulatory compliance, and cybersecurity must be carefully managed before widespread technology adoption.

Routing Based on Instant Data Intelligence

In modern freight operations, effective decision-making depends on the quality and timeliness of data. Real-time logistics algorithms leverage live information to build adaptive, context-aware routing strategies. By continuously ingesting and analyzing inputs from GPS systems, vehicle sensors, traffic feeds, and third-party APIs, these algorithms guide delivery operations in the moment.

By evaluating variables such as vehicle health, delivery priority, traffic conditions, and driver availability, the system can determine the most efficient route at any given time. For example, when delays occur at a distribution hub, the algorithm can proactively redirect vehicles to alternative drop-off locations or reschedule deliveries to minimize congestion and disruption.

IoT devices and embedded telematics play a critical role in ensuring accuracy. They deliver real-time insights into vehicle performance, road conditions, cargo status, and environmental factors, significantly enriching the decision-making process. This real-time visibility not only boosts operational responsiveness but also enhances transparency and accountability across the supply chain.

Note: To unlock the full potential of real-time logistics, organizations must invest in thoughtful implementation. Centralized data architectures, integration with logistics control towers, and workforce upskilling are essential foundations for success.

Predictive Traffic Management

Predictive traffic management shifts logistics from reactive problem-solving to proactive disruption avoidance. By applying machine learning models to historical traffic data, infrastructure planning records, and weather patterns, AI systems can forecast congestion, delays, and network disruptions before they materialize.

This foresight enables route optimization well in advance. For instance, if construction-related congestion is predicted based on permit filings and seasonal trends, freight routes can be adjusted days or even weeks ahead. Early intervention reduces bottlenecks and supports consistent on-time performance, even in complex urban environments.

Predictive systems also incorporate freight-specific constraints, such as weight limits, bridge clearances, truck-only lanes, and toll routes, ensuring compliance while optimizing efficiency.

How Freight Brokers and Carriers Can Use AI to Automate Daily Workflows

Manual workflows slow freight operations and introduce costly errors that erode margins. From generating quotes to issuing final invoices, AI-powered tools can streamline processes and allow teams to focus on higher-value, strategic work. 

Below are seven key ways freight carriers can automate daily operations with AI.

1. Rate Negotiation and Quote Communication

When shippers request pricing, speed often determines who wins the load. Traditionally, brokers might take hours to reply or negotiate via email or phone. AI eliminates these delays by automating both analysis and communication.

• AI systems can read incoming quote requests from emails or online forms and return competitive prices within seconds. For example, C.H. Robinson's generative AI produces roughly 2,600 daily quotes in about 32 seconds—a dramatic improvement over manual workflows. Faster responses increase win rates and improve customer experience.
• AI agents can also handle real-time rate negotiations. In a FleetWorks pilot, an AI assistant countered a carrier's rate, saving $100 on a single load with minimal human involvement. By automating routine negotiations, brokers can move up to 30% more freight in the same timeframe while securing better pricing.
   

2. Booking Confirmation and Documentation

After rates are agreed upon, booking shipments and managing paperwork often create bottlenecks. AI removes much of this friction by automating confirmations and document handling.

• Instead of manually entering data into load boards or email chains, AI can convert tenders into confirmed bookings in seconds. C.H. Robinson, for example, uses AI to process around 5,500 shipment orders per day from emailed tenders—each completed in roughly 90 seconds. This represents a massive productivity gain compared to manual entry.
• AI can extract relevant data, populate forms, verify accuracy, and automatically upload files. This significantly reduces errors, which is an important benefit given that invoice error rates can reach 25% across the industry. Documentation delays alone add an estimated $25 per container on half of all shipments, contributing to billions in unnecessary costs annually.

3. Load Tracking and Real-Time Updates

Shipment visibility and status updates traditionally require frequent "check calls” with drivers and dispatchers, which is a time-consuming, inefficient process. AI-powered tracking automates these tasks and delivers real-time insights.

• AI-enabled tracking tools monitor shipments automatically once a truck is in motion, providing location updates without manual follow-ups. Syfan Logistics reduced check-call volume by 30% after implementing automated driver tracking, allowing teams to manage more loads simultaneously and focus on exceptions rather than routine updates.
• AI systems can proactively notify customers of delays, route deviations, or early arrivals. Many shippers now require 80–90% tracking compliance, a standard that's difficult to meet without automation. AI-driven platforms routinely achieve tracking accuracy rates of 97% or higher, delivering near real-time visibility across freight networks.

4. Scheduling and Dispatch Optimization

Manually coordinating pickup and delivery times, assigning loads to drivers, and planning efficient routes is both complex and time-consuming. AI simplifies scheduling and dispatch by instantly evaluating multiple variables (truck locations, driver hours of service, dock availability, and traffic conditions) to make optimal decisions.

• AI can integrate directly with dock scheduling systems to reserve and adjust appointment slots in real time. Intelligent algorithms assign the most suitable dock and time for each load, reducing congestion and wait times. In warehouse environments, AI-driven scheduling and resource optimization have helped early adopters cut overall logistics costs by 15% and labor costs by 10%, benefiting carriers, warehouses, and shippers alike.
• AI automatically matches loads with the most appropriate drivers or carriers based on location, availability, and historical performance, while also planning routes that minimize delays. Platforms such as Trucker Tools' Smart Capacity use predictive AI to help brokers secure coverage faster and at better margins. By factoring in real-time data like traffic and weather, AI dispatch systems ensure trucks follow the most efficient routes and schedules. According to McKinsey, companies using predictive AI have improved on-time fulfillment by up to 20%.

5. Data Entry and Compliance Management

Each shipment generates large volumes of data that must be entered and validated across multiple systems. Manual data entry slows operations and increases the risk of errors that can lead to compliance issues or unnecessary costs.

• In logistics, even minor mistakes (e.g., incorrect address or freight class) can lead to delivery failures or billing disputes. AI-powered automation, using technologies like OCR and intelligent form filling, dramatically reduces these errors by ensuring data flows accurately between systems without rekeying.
• Regulatory requirements, including hazardous materials documentation, weight restrictions, and insurance verification, require precise, consistent data. AI can automatically verify that all required fields are complete and compliant, flagging issues before they escalate. With nearly half of supply chain professionals still relying on spreadsheets, shifting to AI-driven compliance workflows significantly lowers risk. Forms that once required manual completion and filing can now be accurately auto-filled and submitted electronically.

6. Invoice Processing and Payment Reconciliation

Invoicing and payments are critical to freight operations, yet manual processing is slow and error-prone. Missing documents or mismatched figures can delay payments and erode profitability. AI introduces speed, accuracy, and consistency to financial workflows.

• AI systems can review incoming carrier invoices, extract line items, and automatically compare them against agreed-upon rates or contracts. Discrepancies are identified instantly—an essential capability given that up to 12% of freight invoices contain errors, with some companies seeing error rates as high as 25%. Each mistake can cost between $11 and $77 to resolve. By flagging overcharges or duplicate fees before payment, AI helps prevent unnecessary losses.
• AI also streamlines customer billing by generating invoices immediately after delivery, attaching all required documentation, and automatically tracking payment status. This automation improves cash flow by reducing days sales outstanding and eliminates the need for manual spreadsheet reconciliation or follow-up. The result is faster payments, fewer disputes, and more efficient financial operations overall.

7. Customer Communication and Support

Freight brokers and carriers handle a constant stream of customer inquiries every day. Questions like "Where is my shipment?", "Can you send the proof of delivery?", or "When is the next truck available?" While responding to these requests is essential for good service, it can consume significant time and resources. AI-powered communication tools and chatbots are reshaping how logistics companies manage customer support.

• AI chatbots and email assistants can answer common questions instantly, 24/7, using real-time shipment data and knowledge bases. Customers no longer have to wait for business hours to get updates. This aligns with growing expectations: 69% of consumers prefer chatbots for quick interactions, and 51% value them for immediate service needs. Companies using chatbots report response times up to three times faster than human-only support—especially critical when shippers reach out after hours.
• Today's AI goes beyond scripted replies. It understands context and intent, allowing it to deliver tailored responses. For example, when a customer requests an ETA, the AI can instantly check load status and respond with a specific update, such as, “Your shipment is 50 miles away and expected to arrive by 3:00 PM.” If an issue involves delays, damage, or urgency, the AI can seamlessly escalate the case to a human agent. The results are measurable: 90% of businesses report faster issue resolution after deploying chatbots, along with higher overall customer satisfaction when routine questions are handled quickly by AI.

8. Automating Quoting and Customer Engagement with AI

Traditionally, freight quoting has relied on manually collecting rates from multiple carriers—a time-consuming and error-prone process that often slows response times and weakens competitiveness. Automating this workflow allows freight forwarders to fundamentally improve how they engage with potential customers.

AI-powered tools can instantly evaluate large datasets, factoring in historical pricing, seasonality, and current market conditions to generate accurate, real-time quotes. According to CIFFA, the quoting process represents one of the greatest opportunities for AI in freight forwarding. By increasing speed and precision, AI-driven quoting improves customer satisfaction and significantly boosts conversion rates.

Beyond pricing, AI streamlines customer interactions by delivering instant, relevant responses. These systems can automatically pull information from multiple sources and integrate with transportation management systems, ensuring consistent messaging across all channels. The benefits are clear: faster replies increase the likelihood of securing shipments, improved accuracy builds trust, and automation raises service standards in an industry where timing is critical. With 45% of shippers leaving freight forwarders due to poor technology, AI adoption can be the difference between retaining customers and losing them.

Final Word

AI is unlocking levels of speed, accuracy, and efficiency in airfreight that were previously impossible, reflecting a broader industry shift where AI is no longer optional but essential for competitiveness in global trade.

That said, challenges remain. Forwarders must address data integration across multiple management systems and ensure teams are aligned around technological change. Successful AI implementation depends on defined objectives, well-structured data, and a commitment to continuous improvement. Emerging capabilities such as generative AI, predictive analytics, and autonomous AI agents are set to further transform freight forwarding by delivering deeper insight into increasingly complex operational environments.