While still in the early stages of adoption among freight forwarders and logistics service providers, artificial intelligence (AI) is already beginning to reshape operations.
Historically, traditional freight forwarders’ processes have been done manually, leading to inefficiencies, delays, and errors. As the complexity of logistics operations accelerates, the need for swift and accurate document processing, visibility to logistics operations, and effective decision-making is necessary, especially considering ongoing supply chain disruptions and risk, and changing customer expectations.
To date, logistics technology has delivered greater efficiency, important digital connectivity, and visibility. AI accelerates these improvements by analyzing vast amounts of data to provide valuable capabilities. Forwarders, armed with data analysis, can develop more informed logistics strategies, optimize their operations, and offer clients a new level of intelligent solutions. AI technology offers tremendous potential to strengthen the role of forwarders and deliver greater value to shippers.
Human-in-the-loop
Human expertise plays a key role in AI adoption, providing essential know-how and practical knowledge to verify AI outputs. This is known as human-in-the-loop, where people offer critical judgment to ensure AI outputs are accurate. Combining AI with human expertise ensures that humans continue to retain their pivotal role as trusted partners, which is vital to shipper-forwarder relationships and logistics strategy. The ability to navigate complex regulations, negotiate contracts, and handle unforeseen challenges requires human judgment and experience. In the freight forwarding industry, strong, collaborative forwarder–client relationships are the key to success.
How human-in-the-loop works
To grasp the concept of human-in-the-loop AI, think of a system where humans participate with AI in specific, defined ways to play an active role in overseeing, guiding, and validating the outputs of AI systems.
Human-in-the-loop AI provides the value of human knowledge, experience, and understanding with the accuracy and efficiency of advanced AI technology. Humans provide a greater sense of meaning to AI and provide critical input, domain knowledge, and contextual understanding that AI algorithms may lack. The aim is to enhance the accuracy, reliability, and relevance of AI-generated results.
The loop typically involves several stages. First, the AI system collects and processes data, utilizing machine learning algorithms and other techniques to generate insights or predictions. The results are presented to human operators or experts who review and validate them, incorporating their own knowledge and judgment. This feedback from humans is then used to refine and improve the AI system’s performance in subsequent iterations. Ultimately, human-in-the-loop AI ensures that intelligent systems remain accurate, trustworthy, and aligned with real-world operational judgment. The AI acts as a force multiplier, not a replacement to human work.
AI in practice
There are several ways that AI is currently improving logistics processing and services, including:
· Route optimization and planning. AI optimizes delivery routes, intelligently matches loads with carriers, and enables real-time customer updates, reducing fuel consumption, shortening delivery times, lowering emissions, and improving overall customer satisfaction.
· Inventory management. By analyzing historical sales data, seasonality, market trends, and external factors, such as weather, AI effectively predicts future demand and automatically replenishes stock based on this predictive data. Overstocks are reduced, efficiency improves, and there are fewer stockouts with AI.
· Automating documentation. AI-driven Intelligent document processing technology is used to instantly extract, classify, and validate data from PDFs, emails, and photos for faster processing and a reduction in manual entry errors. Staff members are freed up, allowing them to focus on higher-value tasks. This can be especially helpful with data-intensive logistics tasks like digitizing lengthy rate sheets, where hundreds or thousands of lines of data need to be updated on a frequent basis.
· Risk management and fraud detection. AI algorithms are used to analyze data and identify anomalies that may indicate fraudulent activities or potential risks in freight transactions. Common use cases include detecting duplicate or inflated invoices, flagging unusual routing or carrier behavior, identifying mismatches between declared and actual cargo details, monitoring abnormal rate changes, and spotting patterns that suggest cargo theft, compliance violations, or payment fraud before losses occur.
· Customer support. Front-line automation, AI-powered chatbots and agents are available 24/7 to handle routine inquiries and shipment tracking, freeing up human agents for more complex issues. Another key tool is sentiment analysis where AI monitors the emotional tone of a conversation in real time. If it detects frustration or anger, it can instantly prioritize the case for escalation to a human.
· Image and object recognition. Uses deep learning to automatically identify, count, and track packages and parts from camera feeds, enabling faster inventory management, precise sorting, and real-time tracking through OCR and VLM technologies. Additionally, it can read shipping labels to detect carriers and digitize address data. This reduces manual errors and accelerates operational performance.
· Customs compliance. Customs compliance is one of the most data-intensive areas of logistics, making it well-suited for practical AI adoption. AI is now automating the extraction of key data from commercial invoices and other trade documents, while also recommending Harmonized Tariff Schedule (HTS) codes, duties, and applicable tariffs. This significantly reduces manual data entry and classification work, delivering meaningful time savings and greater consistency. Human-in-the-loop oversight remains essential in customs operations, ensuring entries meet U.S. Customs and Border Protection requirements, which prohibit fully automated customs entry processes.
· Quoting. For full truckload (FTL) and less-than-truckload (LTL) cargo, shippers look to truck brokers and forwarding partners for timely quotes, especially when delivery schedules are tight. AI is now serving as a key solution that automates quotes by extracting details from customer emails that request a quote and generate accurate quotes in seconds, learning, and improving over time.
By embracing AI, freight forwarders reach a new level of efficiency, resource optimization, accuracy, and the ability to perform tasks faster and with more precision. Moreover, the adaptability and responsiveness of AI systems to dynamic market conditions empower forwarders to stay ahead in a rapidly changing industry landscape.
While the integration of AI technology raises concerns about job displacement and excessive reliance on machines, human-in-the-loop AI mitigates these worries by bridging both worlds. Human oversight and control are crucial in maintaining the balance between technology and human expertise. Freight forwarders can leverage AI as a powerful tool to enhance their capabilities, while still relying on human judgment for validation, critical decision-making and relationship management.
Today, AI is being applied inside freight forwarding operations, where technology enhances, rather than replaces, human expertise. As supply chains grow more complex and disruption-driven decision-making is critical, freight forwarders that embrace AI adoption combine the value of technology, speed, data insights, and accuracy, with human judgment.
