Organizations across industries are rapidly adopting artificial intelligence. A new McKinsey survey found that 65% of companies are using generative AI in at least one function, double the number from just a year ago, and 67% plan to increase their AI investments over the next three years.
Not surprisingly, many of these organizations are zeroing in on supply chain management as a primary focus of their investments, with many already reporting revenue increases of 5% or more thanks to the use of AI in supply chain and inventory management.
AI has long been expected to be a game changer for supply chain managers, and now that promise is finally becoming a reality. The technology is rapidly driving new levels of speed and efficiency. It’s also becoming a key component of sustainability efforts, as tightening global regulations make it increasingly difficult to stay compliant and efficient without advanced digital systems. For example, by optimizing routes and inventory, AI can help reduce fuel consumption and waste.
The news is full of examples of retail giants applying AI to their supply chains. Walmart and Amazon use AI-powered robots in their fulfillment centers to manage inventory, process orders, and optimize storage space, and they also use predictive analytics to forecast demand. Zara similarly uses AI for demand forecasting and inventory management, analyzing sales data, social media trends, and other data sources to better predict fashion trends and adjust accordingly, minimizing overproduction and out-of-stocks.
All this is just the beginning. As AI evolves, it will take on even more complex roles. Future applications will expand to autonomous decision-making, where AI systems will not only be able to predict but also adjust the supply chain in real time without human intervention. Advanced AI will likely manage most of the end-to-end supply chain process, from sourcing raw materials to delivering them to customers. This deeper integration is expected to transform traditional supply chain models into dynamic, predictive networks that can better respond to global challenges and market fluctuations.
Brands and retailers are understandably eager to implement AI into their supply chain operations, but the reality is that many companies have yet to build the digital infrastructure to do so. One of the biggest obstacles holding companies back from realizing the potential of AI is the lack of organized, centralized, real-time data. To overcome this, companies must start creating a central repository of supply chain data at the purchase order, SKU, and factory level.
The foundation for optimizing the benefits of AI for any organization is the ability to interconnect thousands of unique data points from multiple data sets across the enterprise. This requires aggregating all data from early planning to product specification, sourcing, costing, and logistics, and including detailed information about every supplier down to the nth tier of the supply chain. Only when companies have effective data management in place can they realize the full potential of AI.
Digitizing with a multi-enterprise platform ensures that data is current, accurate, and accessible. These tools provide real-time supply chain visibility, enabling companies to continuously monitor their supply chains, identify potential issues before they become problems, and make informed decisions based on accurate, up-to-date information. Establishing this digital infrastructure is key to providing AI with the data it needs for predictive analytics and automated decision-making.
These platforms are already deploying AI in innovative ways, and their capabilities are continually expanding. AI-powered chain of custody tools can significantly enhance traceability by automating the validation and documentation of chain of custody of all materials. These tools proactively assess compliance risks and ensure every link in the supply chain meets corporate sustainability standards and complies with global Environmental, Social, and Governance (ESG) regulations. AI can dramatically simplify compliance with global ESG laws, such as the Uyghur Forced Labor Prevention Law, by automatically scanning and scrutinizing all documentation against multiple databases of blacklisted entities and identifying gaps or deficiencies in documentation before shipping.
AI is also rethinking quality control. One exciting new application optimizes quality inspections by analyzing thousands of data points about risk factors such as product type, materials used, and country of origin to determine the likelihood of a product line failing a quality inspection. This capability enables companies to proactively identify and address high-risk PO product lines, allowing them to prioritize quality inspections on high-risk items and improve product quality while reducing inspection costs.
Retail is on the brink of an AI-powered digital revolution, and the opportunities for transformation are enormous. Retailers who can effectively integrate AI into their supply chains will not only see increased operational efficiency, but also gain a competitive advantage in agility, customer satisfaction, and sustainability. To fully leverage the growing potential of AI, brands and retailers must prioritize digitizing their supply chains now, or risk missing out on key advancements and falling behind industry leaders.
Eric Linxwiler is Senior Vice President at TradeBeyond.
