There is a lot of discussion about AI in transportation and logistics right now, but much of it is still fairly abstract. Companies are being told they need an AI strategy, AI agents, AI assistants or some new piece of AI software. I think there is a much simpler way for a 3PL to look at the opportunity.
I managed a 3PL operation for more than a decade. When I look back at how much time people spent receiving information, entering it, checking it, chasing it and moving it from one system or person to another, there are some very obvious places where today’s AI and automation technology can help.
The starting point should not be “What can we do with AI?” The starting point should be “What are our people doing manually every day that is repetitive, time consuming and reasonably predictable?”
That is a much easier business problem to solve.
A 3PL Is an Information Business
Obviously, a 3PL moves freight, but a huge portion of the actual work is managing information around that freight.
An order comes in by email. Someone reads it and enters the shipment. Operations sources a carrier. Someone verifies the details. The carrier gets dispatched. People track the shipment. Customer service answers questions about it. Documents come back after delivery. The POD needs to be matched to the shipment. Carrier invoices need to be checked. Customer invoices need to go out.
Then there are all the exceptions. The carrier falls off, the truck is late, freight is damaged, the customer changes the shipment, an accessorial appears or the consignee refuses the load.
Most 3PLs already have a TMS handling the core shipment record. The problem is that there is still a tremendous amount of work happening around the TMS through email, PDFs, spreadsheets, phone calls, carrier portals and manual data entry.
That is where I see the immediate AI opportunity.
Order Entry Is a Good Example
Take something as simple as an order arriving by email.
The customer may send a PDF, spreadsheet or simply put the shipment information in the body of the email. Someone has to identify the pickup location, delivery location, dates, commodity, weight, equipment requirements, reference numbers and special instructions. They then have to enter all of that into the TMS.
That process is not particularly complicated, but it consumes time and introduces the opportunity for mistakes.
AI can now read the email and attachments, extract the shipment information and turn it into structured data. Automation can then create the shipment or prepare it for review. If required information is missing or something looks unusual, the order goes to a person instead of the system trying to guess.
The experienced transportation person is still involved where their experience is useful. They just don’t need to spend as much time copying information from one place to another.
Track and Trace Is Another Obvious Area
Anyone who has operated a transportation company knows how much time can disappear into track and trace.
Where is the truck? Did it pick up? Has it crossed the border? Is it going to make the delivery appointment? Did it deliver?
Depending on the carrier, employees may be checking tracking platforms, sending emails, texting, calling dispatchers or looking through previous communications.
Much of that information gathering can be automated. More importantly, AI can help interpret the information rather than simply collect it.
Knowing that a truck is currently in London, Ontario is one thing. Knowing that the truck is in London, has a delivery appointment in Montreal tomorrow morning, and based on its current progress may now be at risk is much more useful.
That changes the role of track and trace. Instead of employees spending time checking every shipment, the system can monitor the normal shipments and bring the ones that require attention to the operator.
That is a much better use of experienced transportation people.
I Have Already Built This Around POD Processing
One workflow I have already built as a proof of concept deals with incoming proof-of-delivery documents.
This is a good example because the individual steps are simple, but they create a lot of administrative work when repeated across hundreds or thousands of shipments.
A POD arrives by email. Someone needs to determine which shipment it belongs to, verify the document, save it, attach it to the appropriate shipment and potentially update the shipment so it can move forward for invoicing.
The workflow I built uses AI to read the POD and evaluate the available information against shipment records. It can look at shipment numbers, reference numbers, BOL numbers, carrier information, pickup and delivery information, dates and other available clues to determine the most likely matching shipment.
If the match is sufficiently clear, the automation can attach the POD and update the shipment. If the document is unclear, the match is uncertain or the POD contains something requiring attention, it is sent to a person for review.
The intended benefits are straightforward. There is less administrative work, shipments can become ready for invoicing sooner, DSO can be reduced and cash flow can improve.
This is also an important example of what I think AI implementation should look like. We didn’t replace the TMS. We made the process around the TMS more efficient.
The Same Pattern Exists Throughout the Operation
Once you start looking at a 3PL this way, you see similar opportunities everywhere.
A carrier rate confirmation can be read and compared with the shipment. Does the rate match what was negotiated? Are the pickup and delivery locations correct? Is the equipment correct? Are the dates correct? Are the special instructions there?
A carrier invoice can be compared with the rate confirmation, shipment events and approved accessorials. If everything agrees, it can follow the normal process. If something does not agree, finance or operations can review the exception.
Claims are another example. Much of the work around a claim involves collecting information. PODs, photographs, invoices, emails, shipment history and carrier correspondence all need to be assembled before someone can properly deal with the claim. AI can help collect and organize that information without attempting to replace the person responsible for determining what should actually happen.
Customer service works the same way. When a customer asks where their shipment is, the employee often spends more time finding the answer than communicating it. If the relevant shipment information can already be assembled, the person can deal with the customer instead of searching through several systems first.
I Don’t Think the Answer Is Ten More Pieces of Software
There are already a growing number of specialized AI products entering the transportation market. Some handle tracking. Some handle documents. Others focus on carrier sourcing, communications, invoicing or another specific part of the operation.
There will be very good products in this category, and buying a specialized solution will sometimes be the right answer.
What I don’t think makes sense is creating another technology problem by buying a separate AI application for every individual process.
A shipment is one connected business process. The order affects carrier sourcing. Carrier selection affects dispatch. Dispatch and tracking affect customer service. Delivery produces the POD. The POD affects invoicing. The carrier agreement affects accounts payable. Exceptions throughout the shipment can affect claims, customer relationships and profitability.
If every part of that process operates inside a different automation product, you can quickly recreate the same fragmentation AI was supposed to solve.
I think a better long-term model for many 3PLs is to have an automation layer connecting the systems they already use. The TMS remains the TMS. Accounting remains accounting. Email remains email. Carrier systems and APIs continue to provide information. Automation and AI handle the movement, interpretation and comparison of information between them.
Then new workflows can be added over time without starting over each time.
The Goal Is Not to Remove Transportation People
This is particularly important in transportation because the normal workflow is usually easy. The exceptions are where experienced people earn their money.
A carrier falls off an important shipment. A customer tenders something different from what was quoted. A driver is going to miss an appointment. Freight gets damaged. A consignee refuses delivery. The market suddenly moves and the available carrier cost is higher than the customer’s sell rate.
I would not want AI independently making many of those decisions.
What I do want AI doing is detecting the problem quickly, assembling the relevant information and making sure the experienced operator does not have to spend the first fifteen minutes figuring out what happened.
Automate the predictable work and make people more effective when something becomes unpredictable.
That is a far more realistic goal than trying to build an autonomous freight brokerage.
Start With One Process
I also don’t think most 3PLs need a massive AI transformation project.
Start with one process that is causing real pain. Understand exactly how it works today, how much manual effort is involved, what systems are involved and what happens when something does not follow the normal path.
Then determine what can safely be automated and what should still go to a person.
If the project works, measure it. Did it reduce administrative time? Did the process become faster? Did errors decrease? Did invoices go out sooner? Did employees stop spending their day chasing routine information?
Once you have a result, move to the next process.
Over time those individual workflows can become something much more significant, but each step needs to solve a real business problem.
This Is Where I Think the Opportunity Is
After spending more than a decade managing a 3PL and many more years working with business systems, CRM, integrations and automation, I don’t see AI as something that needs to be bolted onto transportation for the sake of saying a company uses AI.
I see it as a new set of tools that can finally deal with some of the messy information work that traditional business software has never handled particularly well.
Emails, PDFs, loosely structured documents, customer instructions, carrier communications and exceptions have historically required people because software needed everything to be perfectly structured.
AI changes that.
The opportunity is to combine that new capability with good workflow automation and the systems the business already has. The difficult part is not simply connecting an AI model. The difficult part is understanding the operation well enough to know where automation creates value, where it creates risk and where a person should remain involved.
That is why I keep coming back to the same starting point.
Don’t start with AI.
Start with a business process that is taking too much time, creating too many errors or requiring people to repeatedly move information around. Understand the problem first, then determine whether AI and automation can make it better.
If you run a 3PL and have one of those processes, I’d be interested in hearing about it.
Book a consult with me and I’d be happy to talk about where an AI automation might fit in your business.
