ATRI reports carrier profitability suffered across all industry sectors in 2025 with average operating margins below 2% in every sector aside from LTL, and the truckload sector had an average operating margin of -2.3%. Simply stated, in 2025, trucking is running in the red!
Disregard the optimistic musings of the mainstream transportation media reporting that forecasts increased freight demand in 2026. Despite anticipated ~5% reduction in trucking capacity due termination of drivers with unauthorized CDLs, sloppy freight demand is a realistic forecast for 2026. So, only fleets that can reduce operating costs by 3%-5% ASAP survive and in the freight business environment.
Implementing AI in trucking operations has yielded significant returns on investment through enhanced efficiency across major companies:
- Schneider implemented an AI solution to achieve a more than 50% improvement in average cycle time to schedule appointments and 24% reduction in the cost per appointment scheduled.
- UPS uses an AI system for optimal route calculation, saving fuel and resulting in estimated annual savings of $300 million–$400 million.
- Uber Freight utilizes AI to optimize routes and minimize empty miles, improving efficiency and reducing empty travel by 10–15%.
- XPO, implemented an AI-powered freight matching system that reduced transportation costs by 15%.
While all the examples above are for large companies, democratized cloud-based AI tools are affordable and deliver high ROI for medium size and smaller trucking companies through enhanced efficiency, reduced costs, and improved service.
Look, you’ve sat through enough AI sales pitches to last a lifetime. Smarter this, automated that, transform your business. Yet, your dispatcher is still calling carriers for check calls, and your back office is copying data between three different systems.
That’s where the real opportunity here lies. The workflows — chaining data, rules, and automation across systems to cut cost per load and free people to focus on exceptions.
Driver pay keeps going up. Insurance is its own beast. Rates are all over the place. You can’t do much about any of that. But all those manual touches between quote and delivery, the emails, the data entry, the status updates, you can actually make a dent in with AI.
Trucking companies succeeding right now have figured all that out. The most affordable and timely option for a trucking company is to replace your legacy TMS with a modern TMS that has AI enabled tools being built right into it, like EKA Omni-TMS.
What Are “AI Workflows” in a Trucking Company Operation?
The term gets thrown around a lot, usually by people trying to sell you something. So, let’s cut through the noise and talk about what AI workflows mean for your operation, how they differ from the clunky automation tools you’ve probably already tried, and what they look like when they’re running inside a trucking company day-to-day.
Rules, Triggers, and Decisions That Work Together
AI workflows are built on a simple foundation: triggers, rules, and intelligent decision-making layered on top of systems you already use, like your TMS and document management tools.
A trigger kicks things off. A new load gets created. A POD comes in. An invoice is generated. An appointment request hits your inbox. A status change. Any of these events can start a workflow.
From there, rules and AI take over. The system knows what should happen next based on how you’ve set things up, and AI handles the gray areas, like reading a document that doesn’t follow a standard format or figuring out which exception needs human attention, and which one doesn’t.
Why Old-School Automation Kept Falling Short
You’ve probably tried automation before. EDI connections, template-based tools, maybe some macros loosely held together. The problem with those systems is that they’re rigid. They work great until something unexpected happens, and then they break.
A shipper sends a rate confirmation in a weird format. A trucking company updates its SCAC code. An email comes through with the info buried in the body instead of an attachment. Suddenly, your “automated” process needs a human to step in, and that human spends the next 20 minutes fixing what should have taken seconds.
AI workflows handle exceptions differently. They can read unstructured documents, understand context, and route work based on what’s actually happening rather than what a programmer predicted five years ago.
What Your Team Sees Day to Day
Here’s where it gets practical. Think about booking an appointment or building an invoice. Right now, someone on your team probably clicks through six different screens, copies data between systems, and double-checks everything before hitting submit. Multiply that by dozens or hundreds of loads per week, and you’ve got a serious time sink.
AI workflows flip that equation. The system handles 80-90% of the work automatically and only surfaces the exceptions to your team. Your people stop being data-entry machines and start being problem solvers, handling the weird stuff that actually needs a brain while AI takes care of the rest. Below are examples of 3 of 15 key workflow Use Cases where AI can be effectively deployed.
Use Case #1 – Appointment Scheduling
Appointment scheduling sounds simple until you realize how much time your team spends on it. Emails back and forth, portal logins, phone calls when nobody responds, and a two- to three-day lag between load creation and confirmed appointments.
When confirmations come late or get missed entirely, you’re looking at detention fees, reschedules, and frustrated drivers.
For midsize trucking companies, the takeaway is clear. You don’t need Schneider’s budget to run a similar playbook. AI-enabled TMS platforms let you automate appointment scheduling at a smaller scale, and it makes a solid first AI project because the ROI is easy to measure.
Less portal time, lower cost per load, and happier drivers who aren’t sitting around waiting on confirmations.
Use Case #2 – Paperwork, Invoicing & Automatic Carrier Pay
Appointment scheduling is one thing. The paper mountain between delivery and getting paid is another beast entirely.
BOLs, PODs, rate cons, lumper receipts, accessorial backups. Most of it still gets keyed manually into your TMS, and the average fleet waits seven to ten days from delivery to billing. That lag costs money, and so do the errors. Industry data shows up to 12% of freight invoices contain mistakes, with each one costing $11 to $77 to track down and fix.
AI workflows, though, attack the problem from end to end. Documents come in through email, portals, or driver apps. AI reads and extracts the data, even from messy PDFs and phone photos. The system validates charges against rate cons, flags mismatches, and only routes true exceptions to your team. When everything checks out, invoices are generated and sent automatically.
EKA Solutions is building toward this exact model, where humans handle exceptions, and AI workflows drive automatic invoicing and carrier pay once accuracy thresholds are met. Same pattern as Schneider’s scheduling win, just pointed at the “cab-to-cash” process instead of appointments.
Use Case #3 – AI Workflows for Everyday Exceptions & Communication
Big wins like faster invoicing and automated scheduling get the headlines.
But a huge chunk of your operating cost hides in smaller, less obvious places: check calls, status updates, appointment reschedules, “where’s my truck?” emails, and the constant back-and-forth that eats up your dispatchers’ day.
In other words, death by a thousand small tasks.
AI workflows handle the repetitive communication that nobody has time for, but everybody expects. The system monitors load events and telematics, then fires off proactive updates using your templates and rules before anyone must ask. When a broker or shipper sends the hundredth “send me the POD” request of the week, AI responds automatically.
Humans only step in when something falls outside the normal pattern.
The payoff is real. Your dispatchers stop drowning in routine messages and start focusing on problems that need a brain. Shippers and drivers get faster, more consistent responses. And you avoid the penalties and frustration that come from slow communication on a load that’s already moved.
How AI Workflows Actually Reduce Carrier Costs (Breakdown)
We’ve covered the use cases. Now let’s get specific about where the money goes back into your pocket. Here’s a quick rundown of the six ways AI workflows cut costs for carriers and brokers.
- Lower Back-Office Cost Per Load: Fewer manual touches across scheduling, paperwork, billing, and payment mean your team processes more loads without adding bodies.
- Faster Time-to-Cash: Shrinking invoice lag from seven to ten days down to one or two days keeps cash flowing and cuts what you spend on factoring or credit lines.
- Reduced Errors and Disputes: AI validates charges against contracts and rate cons before invoices go out, which means fewer overpayments, underpayments, and awkward phone calls with customers.
- Less Detention and Wasted Time: Smarter appointment workflows get confirmations faster and keep drivers moving instead of sitting at docks burning hours.
- Deferred Head Count Growth: When freight picks up, AI workflows absorb the extra volume instead of forcing you to hire and train new staff.
- Better Asset Utilization: Clean, fast administrative processes mean your trucks spend less time waiting on paperwork and more time hauling freight that pays.
Final Thoughts: EKA’s Point of View & a Call to Action
EKA is building its AI-driven TMS with exactly these workflows in mind. Carriers and brokers can configure automation directly in the system to capture and validate paperwork, trigger invoices and carrier payments based on rules and data instead of spreadsheets, and surface only the exceptions that need human attention.
EKA’s deep investment in open APIs, modular and headless TMS features enable flexibility and rapid adaptability — especially important to scale AI capabilities for load matching and live ETA predictions. Typically, logistics platforms adopting incremental AI workflows see around ~25% faster decision cycles and smoother carrier-shipper synchronization, helping reduce bottlenecks in dynamic pricing and freight tendering.
Schneider reset the benchmark on scheduling. Other larger carriers have slashed invoice lag with AI workflows. Midsize trucking companies can do the same with the right platform and approach.
So, take the next step. Look at your operation and identify the two or three workflows that eat up the most time and create the most bottlenecks. Maybe it’s appointment scheduling, document processing, invoicing, carrier pay, or exception handling. Pick the ones where manual work is killing your margins.
Then, once you’re ready, reach out to us at EKA Solutions. Let’s talk about building an AI workflow road map that turns those pain points into competitive advantages.