AI in Last-Mile Delivery: Why Strong Operations Come First

Artificial intelligence is becoming increasingly embedded in logistics, particularly in the final mile between a distribution network and the customer.

A Supply Chain Dive article highlighted how FedEx, UPS, Veho, and other parcel companies are applying AI to improve delivery-time predictions, reduce package theft, automate customer service, and analyze increasingly complex shipping data. While the article was originally published in 2024, the operational lesson behind it has only become more relevant.

The technology is advancing quickly. The companies positioned to benefit most, however, will not necessarily be the ones adopting AI first.

They will be the ones with the operational infrastructure to support it.

AI Depends on the Data Behind It

FedEx has used AI to combine real-time shipment information, last-mile delivery data, and street-level geography to provide customers with more precise delivery windows. UPS has applied AI and machine learning to identify deliveries with a higher likelihood of loss and help determine whether packages should be redirected to alternative locations.

These are sophisticated applications, but they share something fundamental.

They depend on data.

AI cannot reliably predict delivery performance if shipment information is incomplete. It cannot identify operational patterns when information is spread across disconnected systems. It cannot make better decisions when inventory, order, carrier, and customer data are inconsistent.

The conversation around AI often focuses on what the technology can do.

Operations leaders also need to ask a more basic question:

Is our operation producing the information AI needs to make a good decision?

The Foundation Comes First

For many small and mid-market companies, the more immediate opportunity may not be implementing advanced AI.

It may be improving the infrastructure that will eventually make those capabilities useful.

That means examining how information moves between systems such as:

E-commerce platforms

Inventory and warehouse systems

Shipping software

ERP and accounting platforms

Customer service systems

Carrier networks

Returns platforms

A company may have excellent technology at each point in the process and still struggle operationally if those systems are poorly configured or disconnected.

The same applies to workflows.

If inventory adjustments are inconsistent, shipping exceptions are not documented, returns are handled differently depending on who receives them, or teams rely heavily on spreadsheets and manual workarounds, adding another technology layer does not solve the underlying problem.

It can simply accelerate it.

Last-Mile Intelligence Starts Much Earlier

The term "last mile" makes it easy to focus entirely on the carrier.

In reality, delivery performance begins much earlier.

Accurate inventory determines whether an order can be fulfilled.

Warehouse processes determine whether the correct product is picked and packed.

System configuration determines which shipping service is selected.

Automation rules determine how orders flow.

Carrier data determines what happens once the package leaves the facility.

Customer service data then helps identify what happened when something goes wrong.

All of those pieces create the operational data that increasingly sophisticated technology can analyze.

This is why AI should not be viewed as a standalone solution. It is another layer within a broader operational ecosystem.

What This Means for Small and Mid-Market Companies

Large carriers such as FedEx and UPS have enormous datasets and significant technology resources. Smaller organizations operate at a different scale, but the underlying principles still apply.

Companies can begin preparing for more intelligent operations by strengthening the basics:

Create consistent workflows. Standardize how orders, inventory, fulfillment exceptions, returns, and customer issues are handled.

Connect systems intentionally. Make sure information moves accurately between platforms instead of creating unnecessary manual intervention.

Improve data quality. Product information, inventory quantities, shipping methods, customer records, and operational metrics need to be dependable.

Build visibility. Establish KPIs that show where problems are occurring rather than relying on anecdotal feedback.

Reduce unnecessary complexity. More software does not automatically create a better operation. The right technology stack should support the workflow rather than dictate it.

These improvements provide value today while also creating a stronger foundation for automation, analytics, and AI tomorrow.

Technology Should Accelerate a Strong Operation

At ScaledRight Consulting, we work with small and mid-market companies to strengthen the operational infrastructure behind inventory, warehousing, fulfillment, shipping, returns, and connected systems.

Platforms such as ShipStation, Fishbowl, Shopify, and other operational technologies can create significant efficiencies when they are implemented around a well-designed workflow.

The objective is not simply to add technology.

It is to make sure the systems, processes, people, and data work together.

AI will continue to make logistics more predictive, automated, and responsive. The opportunity is significant.

The companies that capture the most value from it will likely be those that first build an operation capable of supporting it.

Technology can accelerate an operation. It cannot compensate for a weak foundation.

Read the Supply Chain Dive article