Efficient supply chain management (SCM) requires contending with vast amounts of data, constantly changing variables and multiple stakeholders; factors that have traditionally made SCM difficult for humans, to put it mildly.
But thanks to the advent of a new type of technology, agentic AI, humans no longer have to fight supply chain battles alone. AI agents can dramatically speed up and streamline even the most complex SCM tasks.
What are AI agents and how can they manage supply chains?
AI agents are autonomous software programs that can carry out complex, multi-step actions. They work by parsing data relevant to their assigned roles, then using an AI model to determine how to react to the data by taking the actions necessary to fulfill their mission.
In the supply chain space, AI agents have the potential to help automate a variety of processes that have traditionally required significant time and manual effort. Example use cases for agentic AI in SCM include:
- Tracking customer orders and ensuring that relevant inventory is available and ready to ship to fulfill them.
- Managing the physical placement of inventory within a warehouse.
- Syncing schedules between warehouses and trucks to ensure that transport is available when goods need to be shipped or received.
- Managing personnel schedules such that adequate staff are on hand to manage incoming or outgoing shipments.
- Making adjustments to scheduling to account for unexpected issues like equipment breakdown or shipping delays.
- Helping businesses source and validate vendors based on factors like availability, pricing and shipping times.
Each of these tasks could be handled partly or fully by an AI agent. What’s more, the agents could work together (either in parallel or in serial, depending on whether one agent must complete a task before another can begin a different task), effectively creating a fleet of virtual supply chain managers capable of overseeing all core aspects of supply chain planning, implementation and monitoring. Indeed, the ability to automate multi-process workflows and to contend with a wide array of changing and unpredictable variables is a large part of what makes agentic AI particularly valuable.
By leveraging AI agents for these use cases, businesses get more than just faster processes or more efficient use of staff time. Well-designed and properly implemented AI agents, meaning those deployed with guardrails that balance the ability to work through challenges creatively with the need to prevent harmful actions (like deleting critical data), can also reduce risks by bringing consistency to processes that might otherwise be fraught. They also make it easier to scale without being constrained by staff availability.
AI agents vs. traditional SCM software
To be sure, software and automation are nothing new in the supply chain space. Businesses have long used digital tools to help track inventories, manage fleet schedules and so on as a way of boosting efficiency and scalability.
Agentic AI, however, goes further than traditional SCM software tools, offering capabilities that conventional systems lack. For instance, because agents are guided by AI models, they are capable of identifying novel solutions to challenges they encounter. Traditional SCM tools can’t do this because they rely on pre-scripted options and don’t know what to do when they encounter a scenario no one envisioned beforehand.
AI can also automate multiple, interdependent SCM processes, as I mentioned above. Traditional SCM tools don’t usually do this; they tend to focus on singular tasks that, although they may involve multiple steps, are challenging to automate fully because conventional tools can’t reason their way through unforeseen variables in the way AI agents do.
How to get started with agentic AI for SCM
If AI agents sound like a great way to supercharge the efficiency and reliability of your supply chain, you may be thinking, “How do I get started?”
The answer, in part, is that it depends on exactly what you want to do with agentic AI. For simple, single-process facets of SCM, like inventory monitoring, you may be able to find off-the-shelf solutions that will meet your needs.
But for more complex use cases — like using agents to oversee inventory, fleet scheduling and warehouse staffing at the same time, based on interdependent variables — you’re likely to need a custom solution. I’ve seen companies manage to build these on their own if they have the right in-house software development and AI expertise. But because skilled software developers with expertise in cutting-edge AI tend to be in short supply, many businesses will benefit from working with an implementation partner that offers not just the team and expertise necessary to create custom agentic AI solutions, but also the experience that helps to ensure they implement agents with best practices in mind.
Best practices for implementing AI agents for SCM
On that note, let’s talk a bit about what it takes to build a great AI agent for supply chain management. In my experience, having helped create custom agents for a variety of businesses and use cases, these are the factors that organizations need to be thinking about to ensure that agents don’t just do their jobs, but that they do it in an efficient, reliable and secure way:
- Start with a POC: Deploying agents directly into production is enormously risky because it can be challenging to predict what they’ll do. Instead, begin with a proof of concept and use it to validate agent features and reliability. Don’t let agents touch production systems until you’re deeply confident in their abilities.
- Erect guardrails: Guardrails that restrict what data the agents can access and the actions they can take are another important way of protecting against unintended actions.
- Keep humans in the loop (for complex tasks): For high-stakes or particularly complex workflows, it’s often wise to keep a human in the loop. For example, an agentic workflow might require a human to approve the shipment of goods whose value exceeds a certain threshold, as a safeguard against mistakes the agents might make.
- Ensure data quality: Agents are only as effective and reliable as the data that they use to assess situations and make decisions. For that reason, it’s critical to ensure that the databases, inventory systems and other resources agents can access include complete, clean data.
- Monitor and manage costs: While AI agents are powerful, they can also be costly to operate because each agent interaction with an LLM incurs a token cost (although the exact costs vary depending on how extensive the interaction is and which particular LLM the agent connects with). Pulling data may also come with a price. Thus, it’s important to know how much you’re spending on your agents and identify opportunities to optimize (by, for example, switching to a different LLM in a scenario where a less expensive one would suffice).
It’s worth noting that none of these practices are unique to agentic AI. These are the types of things companies should think about when implementing virtually any new type of technological solution. But because AI agents often feel like a brand-new, almost magical type of technology, it can be easy to forget the essentials, especially for teams that are new to agentic AI development and implementation.
AI agents and the future of the supply chain
The bottom line: AI agents are poised to make supply chains easier to manage, while also significantly increasing reliability and efficiency. The question facing businesses that need to oversee supply chains is not whether they should leverage AI agents, but, instead, how best to design and deploy them to meet bespoke SCM needs. The future of SCM is agentic and now is the time to start making the pivot.
This article is published as part of the Foundry Expert Contributor Network.
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