It’s Friday.
Before you shut down your computer today, think about everything you and your logistics team did this week.
How much of it actually required years of logistics experience?
Negotiating.
Solving serious customer problems.
Improving your transportation network.
Working with suppliers.
Reducing freight spend.
Improving inventory flow.
Making important decisions.
And how much of your week was spent doing this?
Checking tracking.
Reading emails.
Looking for late shipments.
Hunting down PODs.
Checking whether someone replied.
Copying information between systems.
Updating customers.
Following up with carriers.
Opening your TMS.
Opening your ERP.
Opening another spreadsheet.
Building reports.
Then doing most of it again tomorrow.
What if you could give a meaningful chunk of that work to an AI agent before next Friday?
Not by replacing your TMS.
Not by replacing your ERP.
And definitely not by replacing the experienced people who understand your operation.
Connect agentic AI to the tools you already use and let it start doing some of the repetitive work around them.
That’s what we’ve become increasingly interested in at Easy Logistics.
We Started Doing This to Ourselves
We’ve spent years automating pieces of freight, sales and customer operations.
But the latest generation of agentic AI has opened up something considerably more interesting.
We’re increasingly able to connect AI to systems we’re already using—email, CRM, APIs, freight systems and operating data—and give the agent actual jobs to perform.
Instead of asking ChatGPT a question and getting an answer, the concept becomes:
Watch this.
Check this.
Compare these things.
Research this.
Tell me when something doesn’t look right.
Draft the response.
Follow up on this.
Summarize everything before I get to work.
That last one is particularly interesting.
What if your AI operations shift started before you did?
Imagine arriving at work at 7:30 Monday morning.
Before you’ve opened Outlook.
Before you’ve logged into your TMS.
Before you’ve checked tracking.
Before you’ve asked your team what happened overnight.
A first shift has already happened.
Your AI agent has reviewed the systems you’ve authorized it to use and gives you a briefing:
7:30 AM OPERATIONS BRIEF
142 active shipments reviewed
128 — Progressing normally
9 — Worth monitoring
5 — Need human attention
27 overnight emails reviewed
8 — Require responses
4 shipment documents retrieved
3 unresolved issues carried forward from yesterday
YOUR PRIORITIES THIS MORNING
1. Shipment 92837 — Potential delivery problem
Expected delivery today. No recent movement.
Relevant tracking history assembled.
Carrier follow-up prepared.
2. ABC Manufacturing — Customer response needed
Customer requested an updated ETA.
Shipment status and previous correspondence summarized.
Draft response prepared for review.
3. Shipment 81772 — Missing documentation
Shipment delivered.
POD has not yet been received.
Follow-up recommended.
Then your team gets to work on those things.
Not searching for them.
This Isn’t Science Fiction
Companies are already applying AI to some of the most repetitive parts of transportation operations.
For example, Shipwell published results from Airlite Plastics’ use of its Track & Trace AI Worker.
The system autonomously monitored shipments and collected delivery confirmations. Shipwell reports that it saved approximately one hour per employee per day, including time previously spent manually emailing carriers for tracking information and collecting delivery confirmations. The company also reported achieving 98% tracking compliance during the pilot.
That’s roughly 15% of an employee’s working year redirected away from repetitive track-and-trace activity.
Read the Shipwell Track & Trace case study
That’s the kind of AI implementation we’re interested in.
Not:
“How do we put AI everywhere?”
But:
“What are our experienced people doing repeatedly that a machine could prepare, monitor or handle for them?”
8 Logistics Workflows We’d Investigate First
1. Stop Checking Every Shipment
This may be one of the easiest opportunities to understand.
If you have 100 active shipments, your team doesn’t really need to know that 94 are moving normally.
They need to know about the six that aren’t.
An agent connected to appropriate tracking/TMS data could potentially monitor:
- Shipment status
- Tracking events
- Expected delivery
- Missing updates
- Missed pickups
- Potential delays
- Delivery exceptions
Then surface the exceptions.
Humans solve problems. AI looks for them.
This is already becoming a real transportation use case. Shipwell’s published Airlite example reported an hour per employee per day saved from autonomous monitoring and related tracking work.
2. Have AI Work Your Operations Inbox Before You Arrive
Think about how much of logistics still runs through email.
Tracking requests.
Appointment changes.
Carrier updates.
Customer questions.
POD requests.
Rate confirmations.
Invoices.
Problems.
Instead of asking somebody to read everything, AI can potentially:
Read → classify → connect to the appropriate shipment → retrieve context → prioritize → summarize → draft.
The objective doesn’t have to be letting AI autonomously answer every email.
Start with:
“Read everything and tell my people which messages actually deserve them.”
From there, low-risk repetitive responses can gradually be automated as confidence increases.
3. Let AI Answer “Where’s My Shipment?” Before Your People Do
Consider the workflow behind a simple customer email:
Where is PO 47391?
Someone may have to:
Read email → identify shipment → find it in TMS → check tracking → interpret status → review previous correspondence → write response → send.
Much of that isn’t judgment.
It’s information retrieval.
An agent with appropriate access could assemble the information and draft:
Shipment 47391 departed the Phoenix terminal yesterday evening and is currently scheduled for delivery tomorrow. No exceptions are currently showing.
Your employee reviews it.
Clicks send.
Eventually, when you’re comfortable with the process, perhaps routine responses don’t require that click.
4. Find Problems Before Your Customer Does
This is one we’re particularly interested in at Easy Logistics.
Traditional freight service is often reactive.
Customer:
Where the hell is my shipment?
Then everybody starts looking.
Agentic monitoring creates the possibility of reversing that:
We noticed your shipment isn’t progressing normally. Our team is already looking into it.
Other logistics AI implementations are reporting substantial reductions in exception-handling effort. Published case studies have reported manual exception reductions of 61% to 74%, although these are vendor-reported results from specific deployments and shouldn’t be assumed to apply universally.
The underlying workflow is what matters:
Monitor continuously → identify anomaly → gather context → prioritize → escalate to human when judgment is needed.
That’s a very different operating model from waiting for someone to complain.
5. Stop Paying Experienced People to Hunt for Documents
PODs.
BOLs.
Rate confirmations.
Packing lists.
Invoices.
Delivery receipts.
The document exists.
Somebody just has to find it, match it to the correct shipment and put it where somebody else needs it.
Document automation is another area already showing meaningful results.
Published logistics implementations report automating large portions of document processing and eliminating substantial manual entry. One recent freight-forwarding case study reported approximately 1,100 staff hours per year saved by automatically extracting information from BOLs, PODs and carrier invoices, matching the documents to shipments and posting the data into the TMS.
Again, results will vary.
But the question for your operation is simple:
Why is somebody with 10 years of logistics experience spending Friday afternoon looking for PDFs?
6. Let AI Reconstruct the Story Before Escalating the Problem
Here’s another enormous time sink.
Something goes wrong.
Now an operations manager needs to figure out what happened.
Open TMS.
Read notes.
Search Outlook.
Find tracking.
Look at yesterday’s email.
Check the carrier response.
Find the customer’s original request.
Ask another employee what happened.
Then finally make a decision.
An agent connected to those authorized systems could potentially assemble:
Shipment history
Tracking events
Customer correspondence
Carrier correspondence
Internal notes
Relevant documents
and hand the manager:
Here’s what happened. Here’s what’s already been tried. Here’s what needs your decision.
AI doesn’t make the important decision.
It eliminates 20 minutes of detective work before the decision.
7. Have AI Build Your Morning HIT List
This might ultimately be one of our favorite applications.
Don’t give your operations manager another dashboard.
Give them priorities.
7:30 AM — OPERATIONS HIT LIST
🔴 4 shipments need attention
🟡 7 shipments worth monitoring
📧 6 customer emails require responses
📄 3 missing documents
📅 2 appointments need confirmation
💰 2 possible billing discrepancies
✅ 128 shipments progressing normally
Now your best people start their morning doing valuable work.
They aren’t spending their first hour figuring out what the valuable work is.
8. Make the Agent Better Every Week
This may ultimately be where things get really interesting.
Once an agent is participating in a workflow, it can also help identify where repetitive human work still exists.
Imagine Friday’s report saying:
CONTINUOUS IMPROVEMENT OPPORTUNITIES
Your team manually checked delivery appointments 17 times this week.
Estimated repetitive effort: 85 minutes.
Recommendation: Add appointment verification to the morning workflow.
22 customer emails required the same shipment-status lookup.
Estimated repetitive effort: 2.4 hours.
Recommendation: Connect the tracking workflow to the customer-response drafting process.
14 delivered shipments required manual POD retrieval.
Recommendation: Automatically retrieve and stage PODs after delivery.
The agent shouldn’t simply rewrite its own rules and grant itself new permissions.
Instead:
Observe → Recommend → Human Approves → Implement → Measure → Improve
Then repeat.
Now AI isn’t simply performing work.
It’s helping you identify the next piece of work that shouldn’t require a human.
The Goal Isn’t an Autonomous Logistics Department
That’s not what we’re proposing.
Experienced logistics professionals do things AI isn’t going to magically replace.
Relationships matter.
Negotiation matters.
Judgment matters.
Knowing when a customer is about to lose their mind matters.
Understanding that this particular shipment matters considerably more than the $800 invoice suggests matters.
The opportunity is to surround those people with an agentic layer that handles more of the repetitive information work.
Let machines do more of the searching, checking, sorting, summarizing and drafting.
Let your people negotiate, solve, improve, communicate and make decisions.
Don’t Replace the Software You Already Bought
This is another part of the opportunity that interests us.
You may already have perfectly good systems:
TMS
ERP
WMS
CRM
Outlook or Gmail
Carrier APIs
Spreadsheets
Document storage
The question may not be:
What new software should we buy?
It may increasingly be:
How do we connect an intelligent agent to the software we already have?
Your TMS remains your TMS.
Your ERP remains your ERP.
Your email remains your email.
The agent becomes an intelligent layer working across them.
The Go Home Early Next Friday Challenge
Here’s what I’d do before leaving today.
Take a piece of paper.
Write:
What did my team do repeatedly this week that didn’t really require their experience?
Don’t overthink it.
Write down five things.
Then circle one.
Not twenty.
One.
Maybe:
We manually checked 80 shipments every morning.
Maybe:
Sarah spends two hours every day answering status requests.
Maybe:
Someone spends every afternoon finding PODs.
Maybe:
I spend an hour every morning figuring out what went wrong yesterday.
Maybe:
We’re constantly moving information between Outlook and our TMS.
Then ask:
Could an agent connected to the tools we already use do 50% of this?
Don’t assume the answer is yes.
Test it.
Measure how long the process takes today.
Connect the appropriate systems.
Teach the agent the SOP.
Keep humans in control of consequential actions.
Run it.
Measure again.
Then next Friday ask one question:
Did we get any of our Friday back?
If the answer is yes, pick the next workflow.
Want Easy Logistics to Help You Try It?
We’ve been going through this process ourselves.
We’re connecting newer agentic AI capabilities to systems we already use and learning where they genuinely save time—and where they don’t.
And that has given us an idea.
Instead of selling you another piece of software, maybe we can help you make the software you already have work harder.
If you’re a logistics, transportation, supply-chain or operations professional, show us:
Your TMS.
Your ERP.
Your email.
Your CRM.
And most importantly:
Show us the repetitive task your team is sick of doing.
We’ll help you map the workflow and determine whether today’s agentic AI can realistically remove a meaningful portion of it.
Maybe it can.
Maybe it can’t.
But we should be able to figure that out pretty quickly.
Reply “GO HOME EARLY” and tell us the one piece of repetitive logistics work you’d love to never do manually again.
Let’s see if we can give you some of next Friday back.
Jeremy Curran
President
Easy Logistics Management