Agentic AI Isn’t Just Coming to Supply Chain - It’s Already Reshaping It
How AI Is Moving from Assistance to Actual Execution
The next wave of AI doesn't just surface insights - it acts on them. From completing customs entries to resolving compliance issues, it’s taking on work once considered untouchable by technology and doing it with unprecedented speed, accuracy, and auditability.
At Raft, we’re deploying AI across thousands of users who manage billions of shipments globally. That gives us a front-row seat to how this shift is playing out in practice - where it’s working, where it’s not, and what it really takes to make it deliver value.
In this series, we’ll use that experience to demystify agentic AI for supply chain. We’ll break down what it is, how it works, and where it’s driving real impact, along with practical insights to help you turn it into your competitive advantage.
But First, the Basics
At its core, agentic AI refers to a system that can autonomously plan and execute tasks to achieve a goal.
It's like assigning a customs issue to an experienced operator. You don’t specify every step - you define the outcome, and they take it from there: reviewing shipment documents, pulling data from internal systems, checking historical declarations, applying customer-specific rules, resolving the issue, and updating the entry - all without needing constant direction.
What Can Agentic AI Actually Do?
It's the question we hear most from executives. To help simplify this, we’ve broken down where agentic AI delivers and where it still needs humans in the decision loop.
Can Do | Can’t Do Alone |
Understand large amounts of operational context across multiple data sources | Operate without defined goals, policies, or guardrails |
Plan multi-step workflows to complete complex tasks | Replace human oversight for regulated or high-risk decisions |
Execute actions across connected systems and tools | Guarantee perfect decisions when data is incomplete or incorrect |
Identify exceptions, risks, and opportunities in real time | Interpret ambiguous situations without sufficient context |
Adapt workflows based on new data, results, or feedback | Take legal responsibility for regulatory filings or compliance decisions |
Why Does This Matter for Supply Chain?
Recent advances in large language models (LLMs), such as ChatGPT, have made it possible for AI to reason through complex problems and plan multi-step workflows. However, on their own, these models are limited to understanding and generating information. You ask a question, and the model responds based on the data it was trained on. It has no awareness of real-world events or the ability to take action.
That limitation is critical in supply chain, where value isn’t created by insight alone; it comes from execution. Agentic systems bridge this gap by connecting LLMs to the tools, data, and workflows required to actually get work done.
Take a common example: validating a payables invoice. An LLM can read the documents. But to validate the invoice, it needs to access your cost estimates, accruals, and contract terms, and compare them with the invoice.
An agentic system can:
Retrieve the required information from your systems
Perform the comparison
Flag discrepancies automatically
From there, it can recommend any required changes, request missing information if needed, and update the entry once that information is received - ready for human approval.
This shift, from understanding to execution, is what makes agentic AI so powerful for our industry. It moves AI from being a tool that informs decisions to one that helps complete the work itself.
Below, we outline the key differences between agentic and other categories you might have heard about. The key difference is simple: Most AI helps people decide what to do - agentic AI actually helps get it done.
Traditional ML | Uses historical data to learn patterns and make static decisions. Requires manual feature engineering and predefined rules/objectives. |
Predictive | Focuses on forecasting future outcomes (demand, delays, inventory needs). Outputs probabilities or predictions, but does not act on them autonomously. |
Generative | Creates new content or data (text, images, simulated scenarios). Can reason and simulate possibilities, but doesn’t take real-world actions. |
Agentic | Combines reasoning + tools + memory to take autonomous actions toward goals. Can plan, decide, and execute workflows dynamically. |
Use Cases of Agentic AI in Supply Chain
AI has already transformed many aspects but as macroeconomic and geopolitical pressures continue to increase complexity, organizations need AI that can do more than analyze - they need it to act.
This shift is most visible in high-friction, multi-step workflows, where data is fragmented, processes are manual, and compliance is critical. In these environments, incremental improvements aren’t enough. What’s needed is a step change in how work gets done. That’s why we’re seeing a growing number of organizations explore the idea of a “digital workforce” - using agentic systems to augment teams and give operators more leverage.
Some areas we’re seeing customers achieve the highest ROI:
10× Faster Customs Declarations | Customs declarations are a good illustration of where agentic AI starts to show real operational value.
Instead of operating as a point solution, the system works across the end-to-end workflow - supporting operators with real-time answers and guidance. The result is a more streamlined process and materially faster clearance times. While customs declarations operate in real time, similar challenges emerge in adjacent workflows - especially those that require going back and coordinating actions across historical data. |
Navigating Tariff Refund Complexities | Recent court rulings unlocking tariff refunds highlight a related but distinct challenge. While customs declarations focus on processing shipments in real time, tariff refunds require organizations to go back and coordinate actions across large volumes of historical data. Capturing this opportunity is complex. Teams must identify eligible entries, update records, generate supporting documentation, and maintain full auditability - often across fragmented systems. Agentic AI can streamline this by:
As with customs workflows, the value comes from coordinating across the entire process rather than optimizing individual steps. This shifts work from reactive, manual effort to a more continuous, event-driven model, where actions are triggered, executed, and tracked with far less human intervention. The result isn’t just efficiency; it’s the ability to unlock significant financial value, possibly amounting to millions in recovered duties. |
Where Are We on the Adoption Journey?
While we're still in the early innings, agentic AI is rapidly moving from concept to adoption. In supply chain specifically, IBM reports that 76% of Chief Supply Chain Officers expect AI agents to significantly improve operational efficiency. For organizations operating in complex, high-volume environments, the implications are clear. Agentic AI enables teams to scale operations, improve compliance, and respond more effectively to disruptions - without relying on manual effort.
Those that begin adopting these systems early will be better positioned to navigate the increasing complexity of global supply chains.
If you’re looking to turn agentic AI into your competitive advantage, book time with one of our experts.
