Techno-optimism

Supply chains have long relied on the experience and judgment of their managers. Today, they increasingly rely on a system that promises greater accuracy: artificial intelligence (AI). From optimising operations to scenario simulation and automated procurement, generative AI is reshaping trade and supply chains. According to our survey, it is the strongest reason to be optimistic about the future of global trade. Many businesses are using AI to create new value opportunities, as well as to boost efficiency. However, smarter algorithms do not always mean smoother operations. Firms still face challenges in adopting AI, from regulatory risks to concerns over data security.

Beyond cost-cutting

When firms introduce AI into their supply chains, their priority is cutting costs. Processes such as automating invoicing, optimising shipping and easing customs clearance help firms to lower their trade costs. This was cited as a top outcome of AI by 35% of the businesses we surveyed (see Figure 1). Another 28% of firms say that AI improved their resource and supply-chain planning, and a quarter say that AI is boosting workforce productivity, mainly by automating time-consuming tasks such as contract management. This frees up supply-chain managers to focus on more strategic decisions, such as strengthening (human) relationships and identifying new suppliers. Although AI can handle the bulk of administrative and operational tasks, human oversight is still needed to ensure accuracy and make judgment calls where algorithms fall short.

Increasing productivity and cutting costs are only some of AI’s benefits—the technology is also helping firms create value in new ways. Consider AI-enabled forecasting: traditionally, forecasting models relied on static inputs, but generative AI can enable more accurate and dynamic forecasting with real-time data. “Rather than just looking at past trends, AI-driven models can simulate multiple future scenarios,” says Sreejith Balasubramanian, head of the centre for supply-chain excellence at Middlesex University. If an event, such as a raw material shortage, is likely to occur, AI can factor it into pricing and procurement strategies in real time. This shift from predictive to descriptive modelling and scenario planning helps firms “anticipate disruptions rather than just react to them”, adds Mr Balasubramanian.

For firms like Dacia, a Romanian carmaker, the ability to integrate real-time information across different data systems has transformed how it anticipates problems and makes decisions. Daniel Enache, a procurement executive at the firm, says that AI has been “especially important for managing spare parts, where demand is notoriously difficult to predict”. If a supplier recall suddenly disrupts availability, for example, AI can instantly adjust orders between suppliers and redirect shipments, ensuring production lines keep moving.

Another new capability of AI is tracking emerging consumer trends, with 20% of firms using it to identify new sources of demand. By analysing patterns in online behaviour, for example, AI can detect market trends long before they show up in sales data. “Consumers leave digital footprints everywhere—on social media, in reviews, in online searches,” says Mr Balasubramanian, adding that “AI can process vast amounts of text to detect early signals of shifting preferences.”

AI is also allowing firms to create new supply networks in response to shifting demand. Of the companies we surveyed, 18% use the technology to find alternative suppliers—a growing priority as Western firms try to diversify away from China as geopolitical tensions sour. Take electric vehicles: as demand grows, AI can identify emerging suppliers of lithium, cobalt and other critical materials, ensuring automakers can pivot before bottlenecks emerge. By analysing supplier performance, pricing trends and geopolitical risks, AI can help firms identify the most stable and cost-effective partners.

The risks of smart tech

Not all firms are comfortable handing over decision-making to AI. About 12% of those we surveyed worry about biases in algorithms—particularly with generative AI, which can inadvertently favour certain suppliers over others. Another 19% cite data security as a concern, as supply-chain management often requires sharing sensitive information across multiple platforms.

Firms are taking steps to mitigate these risks. About 43% of respondents said they had implemented measures to increase security in the digital space, including data encryption and anonymisation protocols (see Figure 2). Similarly, about 38% have turned to secure cloud services to protect their data. About a third of the firms we surveyed reported that they are conducting audits and compliance checks as a standard practice.

Conclusion

The use of AI in supply chains is no longer experimental. Most firms are already using it to increase efficiency and make their supply chains more resilient and adaptive. However, firms must take extra measures to ensure AI-driven decisions remain reliable, such as managing data security and reducing algorithmic bias. Those that demand more than just increased efficiency from AI will be the ones to enjoy a competitive advantage in global trade.