AI Is Reshaping Last Mile Logistics—But How Do You Actually Implement It?

Logistics leaders are familiar with the idea of the last mile in delivery—but it’s also a useful concept for thinking about technology evolution. Sometimes, taking something from a good idea with executive buy-in to an actual, full-fledged implementation is the hardest part. The last conceptual mile can be as difficult as the last physical mile.

This is only magnified in the era of AI. On the one hand, the promise is huge for businesses that are able to take advantage of AI in the supply chain: in a recent podcast, Alberto Oca at McKinsey discussed a last mile carrier who saved millions by utilizing AI in their text threads with customers. At the same time, other businesses are “bleeding capital” on AI investments that don’t yield results.

The results that Oca is describing jibe with my experience so far. The real question for logistics organizations is: how do you overcome the AI last mile problem to actually implement this technology in a way that saves costs?

AI in Customer Experience Is the Next Frontier

The lowest hanging fruit when it comes to AI in the supply chain is leveraging it for customer experience improvements. Large language models (LLMs) have become sophisticated enough that you can easily avoid hallucinations and be confident that you’re providing accurate, relevant delivery information to customers.

Like we saw above, this can reduce inbound call volume and act as a first line of defense for straightforward customer inquiries. It doesn’t replace human customer support teams, but it does save them from answering the same questions (“where’s my order?” “what items are being delivered?”) over and over again—meaning you can do more with a leaner team.

Sounds nice in theory. And there are clear, well-defined steps you can take to get there in practice:

3 Questions to Assess AI Implementation-Readiness

1. What’s the Right Escalation Procedure?

Current AI agents are sophisticated, but they can’t handle every question a customer can throw at them. Right now, I recommend leveraging AI for providing order details, sharing delivery schedules and ETAs and processing simple delivery instructions—then escalating to a human for everything else.

Ask yourself:

• What are my most common customer inquiries and how easy is it to answer them?
• What types of queries should your chat agent ignore automatically?
• How do I want my customer support team to be alerted when a customer has an unanswered question?
• What steps should my customer support team take when the AI agent hands off a question
• What information will those team members need to have the right context to resolve the customer issue?

Even with a relatively limited scope in your initial deployment, you can establish a foundation that will make it much easier to adapt as LLMs improve over time—so you can eventually branch out into letting customers reschedule via AI chat, for instance.

Next step: Define a clear procedure for the handoff between the AI agent and your customer service team. Clearly lay out which queries will be handled by AI, which ones will be escalated to humans and what the service expectations for the team are when escalations occur.

2. How Are You Currently Communicating with Customers?

Here’s the typical use for an AI agent in customer delivery:

1. Your system sends customers automated text messages at the start of the delivery journey.

2. Customers respond to system-generated text messages and receive an AI-based response within the same thread.

3. If needed, the AI agent escalates to your customer service team.

4. Your team can then interact with that thread via a streamlined chat flow in your last mile delivery software.

To reach this point, you first need a baseline layer of bidirectional communication between customers and delivery-related teams.

Does your existing customer communication cadence already include this kind of functionality? If so, you’re set up to deploy an AI agent relatively smoothly.

Next step: Make sure you have two-way customer chat enabled with full visibility into each customer thread from across the delivery organization.

3. What Does Your Ideal Agent Look And Sound Like?

We recommend leveraging AI that relies on the capabilities of existing LLMs, configured for logistics use cases.

This simplifies the data management needs (you’ll only really need to tell it your business’s name and address, customer service hours and website URL), but within that framework, there will still be room for personalization.

Since the AI agent will be the face of your brand in any customer interactions, it’s important to match it to your existing identity with a name and a distinctive voice.

Next step: Decide on a name for your agent that will resonate with customers and project your brand image effectively. Identify any key features of your existing brand that your agent should reflect.

What’s Next For AI In Logistics?

AI implementations like the one described above are going to become increasingly commonplace in the world of logistics in the next few years. So is AI designed to empower delivery drivers, dispatchers and other crucial roles in the delivery and fulfillment process.

Further down the line, agentic AI will play an even greater role; this will certainly come with risks and challenges. The current crop of AI agents are prone to failing—up to 70% of the time.

That isn’t proof that technology that can handle transactions independently will never impact the logistics sphere (it almost certainly will), but it is an argument for a “slow and steady” approach.

That’s why simple but powerful AI implementations like the one we sketched out above are so important right now. They achieve more than just reduced costs and workloads—they put you in position to take greater advantage of new technologies (even the ones that aren’t ready for primetime yet) as they advance.