Retailers are no longer speculating about the impact of artificial intelligence (AI)—they’re putting it to work. AI is already addressing some of the industry’s toughest challenges, from supply chain disruptions to risk management and sustainability.
Last year, a survey found that 57% of companies are considering AI to support supply chain decision-making, while a separate survey of retail executives found implementing AI was the most cited top priority for their supply chain operations.
Each week the news is filled with innovative examples of how AI is transforming operations. Retail giants like Walmart and Amazon use AI-powered robots to manage inventory and process orders, ensuring products are available precisely when and where they’re needed. Zara leverages predictive analytics to track sales data, social media trends, and other sources to forecast demand, minimizing overproduction and stockouts.
AI’s Role in Traceability
AI is now being used to optimize everything from carbon footprint tracking to dynamic pricing strategies in retail. Additionally, AI is playing a key role in route optimization, helping retailers reduce unnecessary fuel consumption and waste. By analyzing real-time data on traffic, weather, and shipment priorities, AI is able to predict the most efficient delivery routes, reducing delays and cutting unnecessary fuel consumption. In warehouses, AI dynamically adjusts inventory levels to reduce overstocking and prevent waste, creating a more sustainable supply chain.
One area that has particularly benefited from AI is traceability. As regulations around sustainability and human rights tighten worldwide, groundbreaking AI-powered chain of custody tools are simplifying compliance by automating the verification of supply chain documentation, mapping the origin and journey of materials while identifying compliance risks. By automatically assessing sustainability risks and generating required regulatory reports, AI is drastically reducing compliance complexity. This AI proactively scans supplier records against multiple databases of flagged entities, ensuring that every link in the supply chain meets sustainability standards. It highlights any gaps or missing documentation before shipments are made, drastically reducing the administrative burden and minimizing regulatory risks.
Challenges to Implementing AI
In quality management, AI is proving equally transformative. New AI-powered PO line risk rating functionality, for instance, analyzes thousands of data points—such as product type, materials, and country of origin—to assign a risk score to each purchase order line. By leveraging AI’s predictive capabilities, companies can detect patterns of defects before they occur, allowing them to refine sourcing strategies and enforce stricter quality controls. This allows companies to focus their limited resources for inspections on high-risk items. With these tools, businesses can shift from reactive problem-solving to proactive quality control, catching issues early and preventing costly mistakes.
The great limitation of AI is that its potential is only as strong as the data that feeds it. Without centralized, high-quality data, AI’s predictive power is significantly diminished, yet many organizations are hindered by fragmented and outdated systems that prevent them from creating the seamless data foundation AI needs. Companies must prioritize building this infrastructure by consolidating data from multiple sources, including purchase orders, SKUs, supplier details, and factory information across all supply chain tiers.
Multi-enterprise platforms offer a powerful solution, integrating not only with ERP systems but external systems, including critical third-party compliance and sustainability databases, to provide a single source of truth. These platforms ensure data accuracy, enable real-time monitoring, and automate key processes like supplier audits and chain-of-custody verification. They also enable AI to analyze and act on data across the entire supply chain, turning information into actionable insights, allowing continuous monitoring, faster decision-making, and complete end-to-end visibility. By connecting fragmented systems, companies can create a seamless data environment that fuels AI’s full potential and ensures compliance with global standards.
Just the Beginning in Retail
AI’s role in supply chain management is set to grow exponentially. It’s on track to evolve into autonomous decision-making systems that will predict and adjust operations without human intervention. In the near future, AI-driven supply chain control towers will provide real-time oversight, automatically rerouting shipments, adjusting procurement strategies, and fine-tuning production schedules based on demand fluctuations and geopolitical risks.
From raw material acquisition to customer delivery, AI will eventually manage most end-to-end processes, turning traditional supply chains into adaptive, predictive networks that can adapt instantly to global disruptions and market shifts.
Retailers that adopt AI now will lead the next era of supply chain innovation. The opportunity to significantly advance digital transformation is immense, but it requires bold investment in data infrastructure and multi-enterprise platforms. Those who take the leap will find themselves not just future-proofing their operations but building supply chains that are more efficient, more transparent, and more responsive to the demands of tomorrow’s market.
