In this article, learn about:
What agentic AI actually is in a supply chain context
How agentic AI is changing day-to-day work
Where to start with agentic AI so you see value
Agentic AI might sound futuristic, but it’s a technology that is becoming more widely adopted every day. It’s being applied to a wide range of planning, replenishment, logistics, and customer service workflows across retail supply chains. It promises efficiency gains like faster exception handling, fewer manual touches on routine tasks, and quicker responses to issues as they emerge.
But those promises don’t always translate into impact. It can be a challenge for supply chain leaders to discern which agentic capabilities bring true value to their business and which just look impressive in a software demo.
In this article, we’ll discuss how agentic AI is actually being used in supply chain operations today, what it could realistically change in your team’s day-to-day work, and the foundation needed to deliver meaningful results.
What Is Agentic AI?
Broadly speaking, agentic AI is a form of artificial intelligence that relies on rules, data, and systems to analyze and take action. These AI “agents” rely on machine learning models to mimic human decision-making. Oftentimes, agents are created to perform a very specific task with clear decision-making logic.
While these agents are often referred to as “autonomous,” that isn’t the whole picture. In real-world applications, AI agents often operate within tight guardrails:
They can only act in systems they’re explicitly connected to.
They follow business rules that humans have defined and approved.
They can be designed to escalate or pause when a situation falls outside those rules.
A helpful way to view agentic AI right now is as highly specialized digital coworkers. Agents can watch data, make routine decisions, and execute repeatable tasks at scale, but they still depend on your team for strategy, judgment, and optimization as your business evolves.
How Is Agentic AI Being Used in the Supply Chain?
In 2026, “agentic” in supply chain often translates into semi‑autonomous helpers that monitor, decide within guardrails, and execute repetitive tasks. But that doesn’t mean there can’t be significant impact. When these agents are implemented in high‑volume, rules‑driven workflows, they can shorten the length of tasks from hours to seconds and take on millions of routine actions.
Let’s take a look at how that’s playing out in practice.
How Agentic AI Is Changing Day-to-Day Work: Real-World Use Cases
CH Robinson: Agentic AI at Scale in Logistics
C.H. Robinson runs a “fleet” of more than 30 AI agents across the shipment lifecycle, in areas like pricing, order intake, freight classification, appointment scheduling, truck posting, and shipment tracking. Agents read emails, interpret tenders, classify LTL freight, set thousands of dock appointments, and respond to tracking requests in seconds instead of hours.
Collectively, they’ve automated millions of individual shipping tasks and contributed to a reported 30% productivity lift since 2023. For employees, that means far fewer repetitive keystrokes and status checks, and more time spent on exceptions, problem-solving, and more strategic work.
Unilever: Building an AI-First Foundation
Unilever is a great example of integrating AI into processes and workflows, not simply layering it on top. Unilever recently entered a partnership with Google Cloud to migrate enterprise apps and data onto a single platform. In addition, the partnership offers access to tools like Vertex AI and Gemini to build “AI‑first” capabilities across marketing and consumer engagement.
Behind the scenes, Unilever has already run hundreds of AI projects and trained tens of thousands of employees, applying AI to use cases like reducing manufacturing waste and improving social engagement. Unilever's new agentic ambition is to move from isolated pilots to a system of intelligence that can reason over unified data, take defined actions, and support decisions at every link of the value chain.
ThredUp: AI-Assisted Inventory Management
ThredUp is a good example of how AI can take over a very specific, high-friction piece of supply chain work and make it more scalable.
ThredUp has thousands of unique SKUs to process each day. Their inbound team used to spend time manually dealing with tagging hierarchies to capture basics like brand, size, care instructions, and attributes. Now, in-house visual AI tools read garment images and identify those details in seconds, allowing team members to focus on handling, quality inspection, and photography. That shift delivered an immediate productivity lift of about 10%.
Related Reading: How Retailers Are Rebuilding Around AI and Customer Experience
Where To Start: A Practical Adoption Path for Supply Chain Teams
So how do you actually get started with agentic AI without overhauling your entire tech stack or overwhelming your team? A good rule of thumb is to start narrow to identify and prove value, then expand.
Step 1: Identify One or Two High Friction or Repetitive Workflows
Look for processes that are painful, predictable, and happen every day or every week. These are usually great areas to implement an agent.
Examples might include:
Low inventory alerts and follow‑up for a key retailer
Late shipment notifications
Data validation and common data entry fixes
If your team is spending a lot of time doing the same checks, sending the same emails, or updating the same fields, that’s a strong signal that optimizations can be made.
Step 2: Define Rules and Success Metrics
Before implementing an agent, it’s important to create a clear standard of what “good” looks like.
That could mean spelling out things like:
The business rules and thresholds (when to alert, when to act, when to escalate)
Which customers, SKUs, or regions are in scope
How you’ll measure success (for example: fewer manual touches, faster response times, fewer stockouts, etc.)
This gives the agent something concrete to follow and gives you a way to judge whether it’s actually helping.
Step 3: Test for Accuracy
To safeguard your business from the risk of inaccurate actions from the agent, it’s crucial to test the agent heavily before releasing it to your wider organization. Early on, instead of having the agent take action, you can ask it to make recommendations.
Have the agent:
Monitor the workflow
Propose specific actions (e.g., “email this buyer,” “adjust this order,” “create this ticket”)
Route those recommendations to humans for review and approval
Then, compare the agent’s recommendations against what your team would have done historically. If the agent gets things right, you can build confidence in the tool. If it gets things wrong, this is a signal to refine the rules or data inputs.
Step 4: Gradually expand automation scope
Once the agent is consistently making good recommendations in a narrow area, you can begin to automate tasks within guardrails. The goal should be evolution, not a massive overhaul of how tasks are completed within your organization. To responsibly scale this technology, you should focus on steadily shifting well‑understood, repeatable work from humans to agents, while keeping humans in charge.
Summary
Agentic AI should be used as a multiplier of the knowledge and talent within your organization, not a "cheat code" that replaces people or fixes broken processes. For leaders looking to implement these tools, the focus should be on areas where your team is overwhelmed with repeatable work and decisions that follow clear patterns.
Ultimately, the winners won’t be the ones with the flashiest agentic AI solutions, but rather the businesses that quietly plug agents into the right workflows and free their people to focus on the decisions that require human judgment.
Ready To Embed AI into Your Supply Chain?
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