At the end of the day, AI’s not really that different from any other new technology—it has to work for the people in your operation who are actually using it. If you want to unlock the transformative powers of some of the newest applications of AI in logistics, you’ve got to make sure that your team is ready to adopt these tools and get the most out of them.
Anyone who has had to grapple with change management in the past knows that this isn’t always straightforward. But you don’t have to reinvent the wheel when it comes to upgrading your technology and boosting your supply chain efficiency. Even as the underlying technology evolves rapidly, the fundamental things will still apply.
In this post, we’ll give a rundown of how AI can impact the modern supply chain (and why it’s worth adopting for furthering your goals), as well as a practical, step-by-step guide to increasing AI tool adoption across supply chain and logistics functions.
AI is already making its presence felt in the world of supply chain and logistics management. It’s helping to increase fleet utilization, boost customer engagement, and enhance warehouse operations. You can use it to empower your drivers with enhanced location-based intelligence, and it’s even able to act as a copilot for dispatching, reporting, and other key functions.
The impacts have already been tangible. Even just among our customers, we’ve seen AI play a role in increasing route density by 10% or more, speeding up responses to customer inquiries, decreasing phone time, and increasing the number of stops completed per day.
That said, there are plenty of horror stories out there about AI agents going rogue and companies selling vaporware that’s packaged as AI. To get value out of supply chain AI, you have to implement the right technology in the right way.
Here’s how to do precisely that:
It can be easy to get the priority here backwards, but the first step towards increasing AI adoption (and gaining the value that comes from it) is to remember that AI usage in and of itself isn’t the goal. Sure, all of the hype around AI can make it feel that way, but to set yourself up for success you have to start with an operational goal and work from there.
So, let’s say your top priority at the moment is improving customer experience. Once you’ve settled on that and decided what that means to you in terms of measurable outcomes (CSAT scores, NPS, on-time delivery rates, whatever it is for your business), you can use that as your north star for everything that comes after.
If we run with the customer experience example, one of the first areas you might leverage AI in the supply chain is within your customer communication workflows to automatically answer some portion of customer questions. AI-powered chat agents have only been increasing in sophistication, so this kind of application can go a long way towards keeping simple and repetitive questions off your customer support team’s desk while ensuring faster responses to questions on average.
To set this kind of deployment up for success, it’s crucial to have an extremely clear procedure for the way customer requests and questions are handed off to a human.
And when we say that the procedure needs to be clear, we mean both on a technical level (the AI agent has well-defined boundary conditions) and on an organizational level (your customer support team knows exactly what to expect and what steps to take). This helps ensure that your AI agent is working to support your team and drive efficiency—without the risk that the ball gets dropped in the middle.
From the outside, this one can be a tough proposition, which is why there’s some value to thinking about scope again. Does the scope of what a vendor is proposing for their agentic AI capabilities seem too good to be true? There’s a good chance that it is.
Again, you can think of AI like any other technology here: you’re looking for a tool that will make the slow or repetitive or complicated parts of the job quicker, easier, and simpler. If a given AI tool actually does that, deploy it! If not, look for something that actually makes life easier for your team.
If we move away from customer experience for a moment and think about driver management, it’s easy to imagine the wrong way to utilize AI. For instance, if the driver’s attention is being called away from the road by an alert in their driver mobile app that they have to interact with, that’s obviously not a recipe for success. Essentially, anything that interrupts a driver’s normal workflow has the potential to do more harm than good.
Conversely, you can provide value to drivers by seamlessly slotting AI capabilities into their existing workflows. If you can have an AI readout of location-based intelligence, essentially a location briefing for the upcoming stop, as the driver heads to a job, you can provide helpful context and information without interrupting the driver and their work.
This kind of technology can be “like having an expert in the cab,” but it only works because it slots into an existing workflow without requiring the driver to take any action outside their normal routine. When you’re dealing with this kind of process, the ability to unobtrusively make someone's job easier is a huge boon.
The AI revolution is proving the importance of an idea that was already extremely applicable in logistics: data is destiny. When you’re trying to do more with that data, the garbage-in, garbage-out problem is only exacerbated. That’s why you can’t deploy AI effectively onto a shaky technology foundation.
The keys here are to ensure effective integration across your various logistics software solutions, as well as meaningful strategic visibility across the board. This is easier said than done, but the right supply chain software providers will make it easy to integrate different systems in a way that makes data visibility the default.
When you have a complete digital picture of what’s happening across your entire supply chain and logistics motion, you can suddenly extract high-value insights and turn existing operational data into more effective AI capabilities.
Like we said at the top, leveraging AI in the supply chain isn’t a goal in and of itself—but it's the most promising way forward right now for improving logistics efficiency and visibility while ensuring great customer experiences. Cost pressures and market volatility aren’t going away any time soon, but logistics organizations can get more out of their resources by deploying AI in measured, proven ways.
As with any new technology, the playbook is still being written. The best practices above have shown themselves to be effective at boosting AI adoption in the supply chain, but the field is going to continue to evolve. To keep up, logistics operators need technology partners they can trust.