By Luis Lopez, AI transportation consultant, CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast
AI tools can help a trucking company, but they do not repair a broken operation. They tend to speed up whatever is already there, including the confusion. Before you sign a contract or start a pilot, it is worth checking whether the basics are in place.
Here is a short readiness test. It is not a scorecard that proves anything. It is a list of warning signs that, in my view, predict a frustrating experience. If several of these sound familiar, that is useful information, and the fixes are mostly things you should do anyway.
The ten warning signs
-
Your data lives in people’s heads and personal spreadsheets
If key information, such as customer rules, preferred carriers, or lane notes, is not written anywhere shared, there is nothing for a tool to learn from or draw on.
What to fix: Pick the five pieces of knowledge that your best dispatcher or biller relies on and write them down in one shared place. Keep it short and keep it current.
-
The same customer or carrier appears under several names
Duplicate and inconsistent names break reporting and confuse any system that tries to match records. A model cannot tell that two spellings are the same company unless you tell it.
What to fix: Clean up your customer and carrier lists, merge duplicates, and set one naming standard that everyone follows. The related article on the data fields to clean before buying AI is a good checklist.
-
Nobody can say how long basic tasks take today
If you do not know how long it takes to invoice a load, book a truck, or onboard a carrier, you cannot tell whether a tool improved anything.
What to fix: Time a handful of core tasks for two weeks. A rough baseline is far better than none.
-
Your processes change depending on who is working
When two employees handle the same situation in two different ways, there is no stable process to automate. Automation locks in whatever you feed it.
What to fix: Write a one-page standard for your most common workflows, and get your team to agree on it before introducing a tool.
-
Documents arrive in every format and are filed inconsistently
Photos taken at an angle, scans with missing pages, and attachments saved under random names make document tools less reliable. They also make audits and disputes harder with or without AI.
What to fix: Set simple rules for how documents are named and stored, and ask drivers and partners to send complete, legible files.
-
You have no owner for the project
Tools adopted by committee, or by whoever had time, tend to fade. Someone needs to be responsible for setup, training, and results.
What to fix: Name one person, give them time to do it, and make clear that they have authority to say no to a vendor.
-
Your team has not been told what the tool is for
Employees who fear that a tool will replace them can resist it quietly, and their cooperation is what makes it work. The question of which roles change first is covered in AI and trucking jobs.
What to fix: Explain which tasks the tool will take on, which decisions stay with people, and how success will be judged. Ask for feedback early.
-
You are buying because of a sales pitch, not a problem
If you cannot describe the specific problem in a sentence, such as “invoices go out too late because we chase missing documents,” then the purchase is driven by the demonstration rather than the need.
What to fix: Write the problem, the current cost in time or errors, and what a better result would look like. Then evaluate sellers against that. This guide on how to evaluate an AI vendor can help.
-
Your systems do not connect to each other
If your dispatch, accounting, and tracking tools do not share data, an AI tool will either sit on its own island or require a lot of manual copying. Neither is satisfying.
What to fix: Find out what integrations your current software supports. A modern transportation management system with open connections makes everything else easier. See what to look for in TMS software for small carriers.
-
Nobody reviews the output
Any tool will make mistakes. If your culture is to accept whatever the system says, you will pass those mistakes straight to customers and carriers.
What to fix: Decide in advance which outputs need human approval, such as anything involving money, contracts, or safety, and who does the checking. Sample the rest regularly.
How to use this list
Count the signs that fit. A few is normal; most small operations will recognize at least a handful. What matters is the pattern. If the problems are mostly about data and documentation, the work is tedious but straightforward. If they are about ownership and process, the work is more about management than technology.
Notice also that none of the fixes require buying AI. Cleaner records, written procedures, a named owner, and a baseline are valuable on their own. They improve training, reduce errors, and make your company easier to run. If you later decide to adopt a tool, you will be in a much better position to choose well and to tell whether it helps.
A sensible first step
Choose one sign that you can fix this month and fix it. Then pick one small, low-risk task for a trial, keep a person reviewing every result, and compare against your baseline. If the numbers improve, expand slowly. If they do not, you will have learned that cheaply.
For a broader look at where smaller operations can start, read AI for small carriers and owner-operators.
Subscribe to the Freight Guru Podcast to keep up with practical conversations on freight and AI.
About the author: Luis Lopez is a Miami-based AI transportation consultant and logistics entrepreneur, the CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast.