While AI has been around for over a decade, its adoption is now more widespread than ever across industries, including the supply chain. Among the latest innovations is agentic AI, which can make autonomous decisions and take action. In supply chains, that means anything from negotiating supplier contracts to optimizing inventory levels. But there’s a huge catch: most companies are not prepared. Only 1% are truly AI-mature, and a major obstacle is data readiness.
Data is the Key to Agentic AI’s Supply Chain Success
Not embracing agentic AI is becoming a missed opportunity. In today’s competitive environment, rising customer expectations, escalating operational costs, and disruptions require agility. Supply chains that continue relying on manual processes, fragmented data, and slow, reactive decision-making will inevitably fall behind.
Agentic AI offers a transformative advantage by streamlining operations, reducing human error, and enabling real-time, autonomous decision-making. AI that can act independently—without waiting for human input—outperforms traditional systems in both speed and adaptability. However, its effectiveness hinges entirely on data quality. Many supply chains struggle with fragmented or siloed data, which can undermine AI’s ability to generate reliable predictions and automate processes.
Businesses must adopt globally recognized product identification and data-sharing standards to unlock the full potential of agentic AI. Standardized, structured, and real-time data ensures seamless communication across all supply chain stakeholders, allowing AI to make informed decisions that enhance accuracy, efficiency, and collaboration. Without a strong data foundation, AI is left to work with incomplete or inconsistent inputs, leading to inefficiencies and costly errors. This isn’t just a technical requirement—it’s the key to realizing AI’s true potential and securing a competitive edge in modern supply chain management.
The Payoff: What AI Can Do If the Data is Right
With the proper data, AI’s potential is vast. Here’s how agentic AI can transform your supply chain—and what you need to do to make it happen.
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Smarter Communication: Agentic AI can autonomously manage communication within the supply chain, translating messages, summarizing documents, and even managing routine customer service interactions. This leads to faster, more efficient responses across the supply chain network, reducing human error and increasing collaboration.
To unlock AI’s potential here, businesses must ensure their communication systems are integrated. Standardizing data formats across systems allows AI to process information effectively and consistently. Businesses that implement globally recognized standards, like GS1 standards, will ensure messages are clear, structured, and actionable for AI systems. These standards provide a common language for supply chain partners, enabling AI to interpret and share data consistently across different platforms and stakeholders. Without this level of standardization, AI-powered communication can become fragmented and unreliable. -
Optimized Inventory Management: With high-quality, standardized data, agentic AI can be deployed to predict supply and demand with high accuracy. It can reduce the chances of stock shortages or excess, forecast future needs, and recommend optimal order quantities to prevent waste and inefficiency, not to mention autonomously placing orders if re-stocking is needed. This predictive capability allows for smoother operations and reduces financial losses from overstocking or stockouts.
Before agentic AI can begin to assess inventory or make future predictions, companies must ensure their inventory data is consistent, up-to-date, and governed properly. Global unique identifiers like Global Trade Item Numbers are crucial for guiding inventory tracking, making it easier to ensure accurate stock counts and quicker restocking by enabling faster, more accurate data exchange. Companies that adopt these standards, alongside a robust data governance structure, can help ensure inventory data is accurate, timely, and aligned across the entire supply chain, enabling AI to make reliable decisions based on up-to-date information.
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Better Customer Experiences: Companies are catching on to AI’s potential to elevate customer experiences. In fact, Gartner predicts that agentic AI will handle 80% of customer service requests by 2029. In addition to tailoring product recommendations based on real-time behavior, AI-driven agents can also handle customer inquiries and complaints, offering 24/7 support and personalized service. This level of automation not only improves customer satisfaction but also helps companies scale their customer service efforts efficiently.
Companies that don’t organize their customer data will struggle to implement AI-driven service tools. AI relies on comprehensive customer interaction histories, and businesses must ensure this data is well-governed and accessible to analyze in real-time. For AI to deliver personalized experiences, businesses need to structure and standardize customer data and product information, integrating it across various customer touchpoints so AI can provide meaningful, real-time insights and recommendations.
The Path Forward
The transformative capabilities of agentic AI are undeniable, but without data readiness, it will remain untapped. Businesses that shy away from adopting robust data governance practices, implementing globally recognized data standards, and ensuring seamless data integration across systems will struggle as automation and autonomous decision-making become the new industry standard.
Now is the time to prepare. Companies that take these steps today will position themselves to harness the power of agentic AI to future-proof operations and power the next generation of supply chains.