By Luis Lopez, AI transportation consultant, CEO of Go Hub.io Holdings Corp and subsidiaries, and host of the Freight Guru Podcast
Your electronic logging device and telematics platform hold some of the most sensitive information in the company: where every truck is, how every driver drives, how many hours each one has left, and which customers you serve on which days. Nearly every AI tool sold to fleets wants access to that feed, because it is the raw material for routing, ETA prediction, safety scoring, maintenance alerts and fuel analysis.
Connecting it usually takes a few clicks and an authorization screen. Understanding what you just agreed to takes longer. As an AI transportation consultant, I ask fleets to work through the ten questions below before granting any new tool access. None of them require a technical background. They require a vendor willing to answer in writing.
1. Exactly which data will the tool pull?
“Telematics data” can mean vehicle location only, or it can mean location, engine diagnostics, driver identity, duty status logs, inspection reports, camera footage and messages. Ask for the specific list of data types the integration reads. Then ask whether you can limit it. A tool that predicts arrival times does not need driver camera clips. Grant the minimum the use case requires.
2. Is the access read-only, or can the tool write back?
Reading location is low risk. Writing is different. Some integrations can send messages to drivers, create or modify dispatch assignments, or push data into other records. Be especially careful with anything that touches hours-of-service logs. Those are regulated records with strict rules about who may edit them and how edits are documented. An AI tool should never be changing duty status on its own. Confirm in writing what the tool can alter, and insist on read-only unless there is a clear reason otherwise.
3. Who owns the data, and what can the vendor do with it?
Read the data rights section of the contract, not the sales deck. You want to know whether the vendor can use your data to train its models, combine it with other fleets’ data, produce benchmarks or market reports from it, or share it with partners. Aggregated industry data has commercial value, and your lanes and customer locations are part of what makes your business yours. If the contract allows uses beyond delivering your service, decide whether you are comfortable with that or negotiate it out.
4. Where is the data stored and how is it protected?
Ask where the data resides, whether it is encrypted in transit and at rest, who at the vendor can view it, and whether the vendor has been through an independent security audit. Ask how they would notify you of a breach and how fast. Fleet location data is a target for more than hackers; knowing where high-value loads sit overnight is useful to cargo thieves. I cover the insurance and exposure side of this in cyber liability for trucking and logistics.
5. How does the connection work, and who else is in the chain?
Some AI tools connect directly to your ELD provider through an approved interface. Others go through a third-party aggregator that sits in the middle and holds your credentials or access token. Neither approach is wrong, but every extra party is another company with access to your feed and another contract you have not read. Ask for the full list of subprocessors, and check whether your ELD provider’s own terms permit the connection you are about to make.
6. How fresh and how complete is the data the model will see?
AI output is only as current as its input. Location updates may arrive every few seconds or every several minutes depending on the device and plan. Trucks lose signal. Devices get unplugged. Drivers forget to log in or select the wrong vehicle. Ask how the tool handles gaps and stale data. A good one flags low confidence when its inputs are old. A poor one produces a confident ETA from a position that is forty minutes out of date. Data quality limits are a recurring theme in my work, and what AI cannot do in freight explains why.
7. What do drivers need to be told?
Drivers are the subject of most of this data. Adding a new tool that analyzes their driving, location history or hours changes how they are being monitored, even if no new hardware goes in the truck. Tell them what is being connected, what it is used for and what it is not used for. Check whether your driver handbook, privacy notice and any agreements with independent contractors cover the new use. Privacy and monitoring rules differ by state and country, so have counsel review it if you run in multiple jurisdictions. Fleets that explain first get far less pushback than fleets that get found out later.
8. How accurate is the output, and how will we verify it?
Ask the vendor how they measure accuracy for the specific feature you are buying, and then plan to measure it yourself. For ETA prediction, compare predicted arrival to actual arrival over several weeks. For maintenance alerts, track how many flagged issues were confirmed by the shop. For safety events, sample the flagged clips and count the false ones. Run the tool in parallel with your current process before anyone relies on it. A vendor who resists a pilot with your own data is telling you something. My broader checklist is in how to evaluate an AI vendor for trucking and logistics.
9. Who is accountable when the AI is wrong?
If the tool suggests a route, a dispatch decision or a driving-hours calculation and it turns out to be wrong, responsibility still sits with the motor carrier. Compliance obligations do not transfer to a software vendor. Read the limitation of liability and indemnity sections of the agreement so you know what the vendor stands behind, which is usually very little. Then design your process so a qualified person approves any decision with compliance or safety consequences. The AI recommends; a dispatcher or safety manager decides.
10. How do we disconnect, and what happens to our data after?
Find out how to revoke access before you grant it. You should be able to cut the connection from your ELD or telematics account without waiting on the vendor. Ask what happens to data already collected: is it deleted, on what timeline, and will the vendor confirm deletion in writing? Ask whether you can export your history and any models or scores built from it in a usable format. Also keep an internal list of every tool that has an active connection to your feed. Many fleets discover old pilots still pulling data long after the project ended.
Start With One Use Case
The fleets that get value from connecting AI to their telematics feed tend to start narrow. They pick one problem, such as predicting maintenance needs or tightening arrival estimates, connect only the data that problem requires, and prove the result before adding more. If maintenance is your first target, AI predictive maintenance for truck fleets walks through what that looks like.
A data feed is easy to open and hard to pull back once copies exist elsewhere. Ten questions and a few written answers up front cost very little compared with finding out later that your location history was used in ways you never agreed to.
For more practical guidance on AI, trucking and logistics, subscribe to the Freight Guru Podcast.
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.