Organizations across industries are adopting artificial intelligence at a rapid pace. A new McKinsey survey found that 65% of businesses are using generative AI in at least one function, double the amount from just one year ago, and 67% expect to increase their investments in AI over the next three years.

Unsurprisingly, many of these organizations have zeroed in on supply chain management as a major focus of investment, with many already reporting revenue increases of 5% or more due to their use of AI in supply chain and inventory management.

For years, AI has been anticipated to be a game-changer for supply chain managers. Now it’s finally beginning to realize its promise. This technology is quickly bringing speed and efficiency to a new level. It’s also becoming a critical component of the drive toward sustainability, as tightening global regulations have made it increasingly difficult to maintain compliance and efficiency without sophisticated digital systems. By optimizing routes and inventories, for example, AI helps reduce fuel consumption and waste.

The news is filled with examples of some of retail’s biggest names applying AI to their supply chains. Walmart and Amazon are using AI-powered robots in fulfillment centers to manage inventory, process orders and optimize storage space. They’re also utilizing predictive analytics to forecast demand. Zara is similarly using AI for demand forecasting and inventory management, analyzing sales data, social media trends, and other data sources to predict fashion trends more accurately and adjust accordingly, minimizing overproduction and stockouts.

All this is just the beginning. As AI evolves, it's poised to take on even more complex roles. Future applications are on track to extend into autonomous decision-making, where AI systems will not only predict but also make real-time adjustments to supply chains without human intervention. Advanced AI is likely to manage most end-to-end supply chain processes, from raw-material acquisition through to customer delivery. This deeper integration promises to transform traditional supply chain models into dynamic, predictive networks that can more adeptly respond to global challenges and market fluctuations.

Brands and retailers are understandably eager to deploy AI in their supply chain operations, but in truth many have not yet created the digital infrastructure to do so. One major obstacle preventing businesses from realizing AI’s potential is the lack of organized, centralized, real-time data. To overcome this, companies need to start creating a central repository of supply chain data at the purchase-order, SKU and factory levels.

The foundation for optimizing the benefits of AI for any organization lies in the ability to interconnect thousands of proprietary data points from multiple data sets across the enterprise. That requires aggregating all data from early-stage planning through the creation of product specifications, on to sourcing, costing and logistics, and including detailed information on all suppliers along the supply chain up to the nth tier. Only when businesses have established effective data management can they begin realizing AI's full potential.

Digitizing with a multi-enterprise platform ensures that data is current, accurate and accessible. These tools provide real-time supply chain visibility, allowing businesses to monitor their supply chains continuously, identify potential issues before they escalate, and make informed decisions based on accurate, up-to-date information. Establishing this digital infrastructure is key to equipping AI with the data it needs for predictive analytics and automated decision-making.

Already these platforms are deploying AI in innovative ways, and their capabilities are continually expanding. AI-powered chain-of-custody tools can significantly enhance traceability by automating verification and documenting the chain of custody of all materials. These tools proactively assess compliance risks and ensure that every link in the supply chain meets the company’s standards of sustainability and complies with global environmental, social and governance (ESG) regulations. By automatically scanning and vetting all documents against multiple databases of blacklisted entities, and identifying gaps or missing documentation before shipping, AI dramatically simplifies compliance with global ESG laws like the Uyghur Forced Labor Prevention Act.

AI is also reimagining quality management. One exciting new application optimizes quality inspections by analyzing thousands of data points around risk factors such as product type, materials used and country of origin, to determine the likelihood of a product line failing quality inspections. This capability allows businesses to proactively identify and address high-risk PO product lines, so they can prioritize quality inspections around high-risk items, reducing inspection costs while increasing product quality.

As retail stands on the brink of a digital revolution powered by AI, the opportunities for transformation are immense. Retailers that can effectively integrate AI into their supply chains will not only achieve greater operational efficiencies, but will also gain competitive advantages in agility, customer satisfaction and sustainability. To fully capitalize on AI’s growing potential, brands and retailers must prioritize the digitization of their supply chain now, or risk missing out on critical advances and falling behind industry leaders.

Eric Linxwiler is senior vice president of TradeBeyond.