How AI is disrupting and transforming the logistics and trade industries

The logistics and trade industries, like many other industries, are currently undergoing a gradual but accelerating transformation driven by artificial intelligence (AI). From optimising supply chains to enhancing trade routes, AI is revolutionising how goods move across the world.

By leveraging advanced algorithms, machine learning, and real-time data analytics, AI is addressing longstanding inefficiencies, reducing costs, and enabling unprecedented levels of scalability and adaptability.

This article examines the primary ways in which AI is disrupting and reshaping the logistics and trade industries.

Optimising supply chain management

Supply chains are complex networks involving suppliers, manufacturers, distributors, importers, customs brokers, freight forwarders and retailers. AI is streamlining these processes by providing predictive insights and automating decision-making.

Machine learning models are able to analyse historical data, market trends, and external factors like weather or geopolitical events to forecast demand with ever-increasing accuracy. This allows companies to optimise inventory levels, reducing overstocking or stockouts.

For example, AI-powered tools can predict disruptions, such as port congestion or raw material shortages, enabling companies to make proactive adjustments.

Companies like Amazon and Walmart use AI to dynamically adjust their supply chains, ensuring goods are available where and when they’re needed. According to a 2023 report by McKinsey, AI-driven supply chain optimisation can reduce logistics costs and increase revenues significantly.

However, it must be noted that the flipside of these developments is that the adoption of AI-powered tools and technologies is expected to lead to decreases in the number of human staff employed by some of the larger logistics providers in the market.

Enhancing route optimisation and fleet management

Efficient transportation is the backbone of logistics, and AI is redefining how routes are planned and how ship and truck fleets are managed.

AI algorithms analyse real-time data on traffic, fuel prices, and delivery schedules to determine the most cost-effective and timely routes. This not only reduces fuel consumption but also minimises delivery times, enhancing customer satisfaction.

AI-powered fleet management systems also monitor vehicle health, predicting maintenance needs before breakdowns occur. For instance, companies like UPS are now using AI to optimise delivery routes, saving millions of miles driven annually, which translates to lower emissions and costs.

Autonomous vehicles, guided by AI, are also likely to eventually be adopted by the industry, with companies like Tesla and Waymo testing self-driving trucks that could further reduce labour costs and improve efficiency.

Improving warehouse operations

Warehouses are critical in the logistics chain, and AI is transforming them into highly efficient, automated hubs. AI-driven robotics and computer vision systems enable faster picking, packing, and sorting of goods.

Collaborative robots, or ‘cobots’, work alongside human employees using AI to navigate warehouses and handle repetitive tasks (there have been videos circulating of Amazon warehouses with these robots at work).

AI can also optimise warehouse layouts by analysing product demand and movement patterns, ensuring high-demand items are easily accessible. Amazon’s fulfilment centres, for example, use AI to manage millions of products, with robots moving shelves to workers, cutting processing times significantly.

A 2024 study by Gartner estimated that AI-powered automation could reduce warehouse operating costs by up to 30%.

Improving last-mile delivery

Last-mile delivery (or perhaps we should say last-kilometre delivery in Australia) is the final step in getting goods to customers, and AI is tackling this challenge by optimising delivery schedules, predicting customer availability, and enabling dynamic pricing models.

For example, machine learning algorithms analyse customer behaviour to suggest delivery windows, reducing failed deliveries.

AI is also powering the rise of delivery drones and autonomous vehicles. Companies like DHL and FedEx are already experimenting with AI-driven drones for remote or urgent deliveries.

Enhancing trade compliance and documentation

Global trade involves navigating a maze of regulations, tariffs, and documentation (across multiple jurisdictions). AI is simplifying this by automating compliance processes and reducing errors.

Some companies have adopted the use of natural language processing tools that extract and classify information from trade documents, such as bills of lading or customs forms, speeding up clearance processes. Platforms like Flexport are already using AI to streamline customs clearance, reducing delays and costs.

However, it should be noted that the use of these tools would ideally still be undertaken with human oversight and verification to prevent AI errors, which may lead to both financial and legal consequences for logistics providers (and others in the supply chain such as importers/exporters). For instance, AI tools may seek to adopt a blanket tariff classification for a type of goods based on a tariff ruling from the US or another foreign jurisdiction. However, tariff classifications in Australia can be different even when dealing with the same types of goods. Using the incorrect tariff classification based on input from AI could expose importers and licensed customs brokers (LCBs) to penalties or other compliance actions.

Ultimately, while AI tools will likely streamline and optimise many activities of LCBs, human expertise and oversight will remain irreplaceable when ensuring trade compliance.

Driving sustainability in logistics

Sustainability is a growing priority in logistics, and AI is helping companies meet environmental goals.

By optimising routes, reducing idle times, and improving load efficiency, AI minimises fuel consumption and carbon emissions.

AI also supports circular logistics by predicting the lifecycle of products and optimising reverse logistics for returns and recycling. For example, Maersk has used AI to optimise vessel routes and reduce fuel usage, contributing to its goal of net-zero emissions by 2040.

Challenges and concerns of AI adoption

High initial costs for AI infrastructure, data privacy concerns, and the need for skilled talent can be barriers to AI adoption, especially for smaller companies. Additionally, over-reliance on AI could lead to vulnerabilities if systems fail or are hacked.

There is also the issue of AI tools providing incorrect information, AI ‘hallucinations’ and how errors of this nature will be viewed by regulators. Noting the strict liability nature of certain offences related to making incorrect statements to the Australian Border Force, it is unlikely that reliance on AI will amount to a robust defence to such allegations by regulators.

There are also wider social and ethical considerations at play (which are affecting many industries), such as job displacement due to automation. Whether the AI disruption to labour markets is similar to previous technological advances or is a unique threat to job security remains to be seen.

To conclude, from predictive analytics to autonomous vehicles, AI is driving efficiency, reducing costs, and enhancing sustainability in trade and logistics. While challenges remain, the benefits of AI, as outlined in this article, are clear, and logistics providers will have no choice but to adapt or be left behind.

Contact us

If you would like to discuss the impact of AI on the trade and logistics industries, please contact a member of our Customs & Trade team.