If you run a trucking operation in 2025 and you still have dispatchers buried in check calls and copy-paste emails, you are burning money. Quietly. Every single day.
Let’s talk about something most carriers feel but rarely say out loud:
A good dispatcher is not paid to chase ETAs in five different systems. A good dispatcher is paid to think.
This is where tools like ChatGPT come in. Not as a replacement for dispatchers, but as the “second brain” that quietly eats 30 to 40 percent of their repetitive load so they can actually do dispatching, not data entry.
I am going to break this into two parts:
- Where dispatchers actually waste time today.
- How AI can realistically take that chunk of work without touching the human part of the job.
No fluff, only operations reality.
The uncomfortable truth: where dispatchers really lose hours
Forget the job description. Let us look at what a dispatcher actually does between 7 AM and 5 PM inside most trucking operations.
1. Check calls that add zero value
Half the morning goes like this:
- “Hey, where are you at right now”
- “You still on track for 2 pm”
- “Send me a picture of the BOL when you are empty”
Multiply that by 25 to 40 drivers per dispatcher and you already see the problem. The ELD has location. The TMS has the load. The dispatcher has to manually translate data from one screen into human language for planners, brokers, and shippers.
This is not skill. This is repetition.
2. ETA summaries that no one wants to type
Every operation has some version of this:
- End of day, “Send me status of all active loads”
- Customer wants hourly ETA email updates on high value loads
- Sales or leadership wants “what went wrong today” summary
Dispatchers usually export a report, open Excel, clean it, then type a paragraph for each lane or customer. It is boring, and it is exactly the sort of thing that causes burnout.
3. Customer updates that are the same 80 percent of the time
Emails like this:
“Hi John, just an update. Driver is currently 142 miles away, ETA 3:10 PM local, no issues reported. We will send POD once empty.”
You know this template. Different name, different ETA, same message. Dispatchers retype this all day, or copy from old emails and edit the details. Easy to mess up, easy to forget, and completely automatable.
4. Detention follow ups that die in someone’s inbox
Detention is a perfect example of silent revenue leak.
- Check in and check out times are in ELD or in-in / out-out from the driver.
- Someone has to compare appointment to actual, apply the rules, send “detention request”, then follow up three times until someone responds.
What usually happens First email goes out. Then the day explodes. No one tracks who did not reply. Money is left on the table.
5. Documentation reminders that nag dispatchers all day
“Send me the POD.” “Upload lumper receipt.” “Where is the scale ticket.”
This is death by a thousand cuts. Dispatchers ping drivers in WhatsApp, SMS, TMS chat, and then manually check if documents were uploaded. It is not complex work. It is persistent noise.
All of this is where AI lives.
Not in “replacing dispatchers.” In quietly killing repetitive, low judgment tasks.
What 40 percent less repetitive work actually looks like
Let us be concrete. When I say “ChatGPT can replace 40 percent of a dispatcher’s repetitive work,” I am not talking about some futuristic robot control tower.
I am talking about very boring workflows that can be systemized today using tools that already exist.
Think of ChatGPT here as a smart assistant that connects to your TMS, ELD, emails, and internal chat, then does three simple things:
- Reads data faster than any human.
- Summarizes it in human language.
- Sends it in the right format to the right person.
Your dispatcher becomes the decision maker and gatekeeper, not the typist.
Use case 1: AI generated check calls and ETA updates
Current reality
- Dispatcher opens ELD platform.
- Looks at last ping, average speed, remaining miles.
- Mentally calculates ETA or uses the built in tool.
- Types update to broker or customer.
With AI in the loop
You connect your TMS/ELD feed to an internal AI assistant built on ChatGPT.
The dispatcher asks:
“Give me ETA and status for all CanAmex loads for Customer X, in one clean summary for email.”
The AI responds with something like:
“Load 1234: 92 miles away, tracking shows arrival at 14:20 local, no delays reported. Load 5678: Arrived at consignee at 09:05, currently unloading, detention starts at 11:05 per customer rules. Load 9101: Left shipper late at 08:45 due to dock delay, new ETA 17:30, risk of missing delivery window by 30 minutes, suggest notifying customer now.”
Dispatcher skims, edits if needed, and sends. That “thinking and typing” time of 20 to 30 minutes just dropped to 3 to 5 minutes.
Net effect
- Fewer manual check calls.
- Cleaner, faster communication.
- Dispatcher still controls tone and exceptions, AI does the grunt work.
Use case 2: Customer updates at scale, without human copy paste
You have 60 active loads today. Ten high touch customers want updates twice a day.
Right now, that can eat one dispatcher for an entire hour or more.
With AI, you can set a simple workflow:
- Pull all active loads for Customer A from TMS API.
- Feed the data into ChatGPT with your communication rules.
- Ask it to draft one structured email per customer or per lane.
For example:
“Draft a status email for Customer XYZ from this load report. Focus on delays, early arrivals, and risk loads. No fluff, just clean bullet points.”
AI returns a ready to send email:
- Loads on track
- Loads delayed with reasons
- Any critical exceptions
Dispatcher checks for accuracy, hits send. That is the pattern. AI as writer, dispatcher as editor.
Use case 3: Detention tracking that actually gets billed
Detention is where most fleets talk tough but follow up weak.
Here is a simple AI powered detention process:
- System pulls check in / check out times and appointment times from TMS and ELD.
- ChatGPT calculates detention eligibility based on your rules per customer.
- It drafts:
Dispatcher does not have to:
- Build each email from scratch.
- Manually check who replied and who ignored.
They only need to:
- Approve the initial claim.
- Intervene if a customer disputes it.
You protect revenue without burning human energy on repetitive follow ups.
Use case 4: Documentation reminders that do not rely on memory
Instead of dispatchers chasing drivers all day, let AI handle the nagging part.
Example flow:
- Load is delivered in the TMS.
- System checks: is POD uploaded, is lumper receipt attached, is rate confirmation linked.
- If missing, AI assistant sends a WhatsApp or SMS template to the driver:
“Hey Mike, POD for Load 1234 is missing in the system. Please upload via app or send a clear picture here.”
After a few hours, if still missing, AI nudges again or escalates to dispatcher.
The dispatcher only spends time on edge cases, not on routine reminders.
So why not “replace” dispatchers entirely
Because AI cannot do the core human work of dispatch.
Here is what AI is terrible at in real operations:
- Reading a driver’s voice at 2 AM and deciding, “This guy is done, do not push him.”
- Knowing that one customer will explode over a 30 minute delay and another does not care as long as you tell them.
- Balancing who gets the next good load because someone took three ugly ones in a row.
- Handling real world chaos like breakdowns, family emergencies, weather freak events, and emotional drivers.
That is judgement. That is relationship. That is pattern recognition built over hundreds of messy days.
Keep that with humans.
What AI is good at:
- Repetition.
- Summarization.
- Pattern spotting in data.
- Generating clean, consistent communication.
So the smart carrier does not ask “Can ChatGPT replace dispatchers” but “What part of dispatching is so repetitive that it is stupid to pay a human to do it.”
What this means for trucking trends and dispatch strategy
From a big picture view, this shift fits directly into the larger trucking trends and supply chain updates we are already seeing:
- Driver shortage: You cannot afford to waste human capacity in the back office when you are short on quality talent in every department. AI lets each dispatcher handle more trucks without burning out.
- Logistics technology: Everyone talks about “control towers” and “digital visibility” but if your people still type every status email by hand, you are not actually digital yet.
- Fleet optimization: When dispatchers spend less time typing and chasing, they can finally focus on planning, dwell time, empty miles, and better load pairing. That is where real fleet optimization lives.
- Cross border freight: For US Canada and US Mexico operations, repetitive customs status updates, border crossing ETAs, and document checks are prime AI territory.
AI in dispatch is not a trend for 2030. It is a competitive advantage for 2025.
How to start, without breaking your operation
If you are serious about this, skip the “innovation theater.” Start small, targeted, and measurable.
Here is a simple starter path you can give your ops team:
- Pick one use case For example: customer ETA updates or detention emails. Do not boil the ocean.
- Create a tight template Decide how you want emails and messages to look. Tone, structure, key data points. Feed that to ChatGPT so it learns your style.
- Connect data, even manually at first In the beginning, your dispatcher can export a daily CSV or copy a load list into ChatGPT. Later, your tech team can integrate APIs from your TMS or visibility tools.
- Keep dispatcher as final gate AI drafts. Dispatcher approves. This keeps risk down and builds trust in the system.
- Measure the impact Track before and after:
If you are not seeing at least 20 to 30 percent time savings on that one workflow, refine the prompts and structure. Once it works, extend across other repetitive tasks.
Final thought: AI will not steal dispatch jobs, lazy processes will
The carriers that scare me are not the ones “afraid of AI.”
The ones that worry me are the fleets that:
- Pay talented dispatchers to behave like data entry clerks.
- Refuse to standardize processes, then blame “people issues.”
- Talk about “driver shortage” while suffocating the staff they already have.
ChatGPT and similar tools will not replace dispatchers. But dispatchers who know how to work with AI will absolutely replace the ones who do not.
If you run trucking operations and you want your dispatch strategy to actually match the logistics technology of 2025, start asking a simple question every week:
“What is one repetitive thing my dispatchers are doing today that AI could do faster, cleaner, and cheaper”
Then fix that one thing.
Do that for a few months and you will not just be “using AI.” You will be building a smarter, calmer, more profitable operation that your drivers and dispatchers actually want to stay in.
Truck & Trade Trends 
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