Increase Truck Driver Optimization and Retention with AI

Attracting and keeping drivers has been an ongoing challenge for trucking companies, but artificial intelligence (AI) may be the key to building better driver-carrier relationships. 

Discover the ways AI can support fleets in this quick read. Based on a 30-minute Fleet Owner webchat, “How to Profitably Grow Your Fleet in a Recession,” the article offers advice from Magnus Technologies Founder and CEO Matt Cartwright and Solutions Engineering Manager Verlen Larsen.

Planning Drivers and Loads is Complicated and Full of Variables

“This business is very complicated with a ton of different variables all the time. To list all the KPIs that are required, you can get analysis paralysis,” Larsen said. “When high-load planners pair a load with a driver for that load, there are many variables to consider.”

The variables include:

  • Are drivers in proximity to the load?
  • What is that proximity?
  • Which of all my drivers are within X miles?
  • Do they have hours-of-service availability?
  • Did they request PTO?
  • Do they like to load on a particular day?

How can back-office operations and drivers work from a simulated load, blending the right rate to provide the optimal yield? This is tough to do manually, even for the most skilled load planner, considering all of the different items that go into a load. 

“For example, a dispatcher may have three drivers capable of a particular load, but one driver loves to drive to Florida. Another driver hates to load Sunday mornings, because he almost never does. He did only once in the last year, and afterward, he only did two loads for the next two weeks,” Larsen said. 

Planners start to learn those valuable preferences, but so can AI and machine learning (ML) - and they often do so faster than a human. AI and ML are able to better utilize, track, and analyze data, which helps make sure drivers are consistently working without distractions and confusion.

AI Solves for Driver Preference

There’s also the side of freight decisioning that involves hard constraints. If you have freight requirements that require certain equipment, those are easier to handle with a rules-based scenario. For example, if it's a hazmat load, is the driver hazmat-certified? 

“But then you've also got soft requirements like how much is a strong reward for this driver? Does this person typically like to work Monday through Friday, or do they do Sunday through Thursday? Many other other variables could go into driver-centric decisions, but they’re not as easily quantified,” Larsen said. 

It pays to know your drivers, but it seems impossible to sustain, right? Not at all, according to Cartwright and Larsen. AI and ML uses the data that is consistently provided through drivers’ daily tasks and back-office inputs to learn their preferences, helping operators to improve their route planning and overall decision-making.

How Does AI-Driven Automation Strengthen Driver Retention?

Fleets using an AI-supported TMS can keep up with all the variables, as well as incrementally and continuously learn from the data. The AI-driven support helps carriers plan more effectively with less stress and less guess. 

Driver satisfaction and retention increase when fleets:

  • Keep the drivers you have satisfied and well-directed. 
  • React quickly to opportunity and problems. 
  • Plan loads that cater to driver preference using AI to make connections. 


“That's the thing we look at to help fleets make those decisions faster and have rich data. One planner we talked to would schedule and assign 800+ loads a day. You need a pretty nimble system, or you're going to work yourself into the grave doing that,” Larsen said.

“That's where we layer in our AI and ML. It looks at trends, and so AI and ML are expert decision support that's going to suggest answers. It isn't just going to look at the hard constraints and determine this driver is incapable of doing this load,” Cartwright added.

AI-Driven Driver Knowledge Pays Off

Artificial intelligence is designed to sift through volumes of data points in an instant. Feedback loops are helpful for automating load planning in ways that maintain driver satisfaction, while creating confident dispatchers as well. Both AI-supported TMS and human users continually develop richer understanding of the data through algorithms and machine learning. This leads to a number of benefits, including:

  • Reduced recruiting and hiring spend
  • Streamlined driver-related processes
  • Improved dispatch communications

Fleets can combine AI with TMS automation for immediate driver- and load-related decisions – and benefits – at scale.

On-Demand Webchat: More Wide-Ranging Advice for More Freight Operations

With the right strategies and technologies like AI and ML, fleets can gain advantages through stronger driver-carrier relationships. Watch the full 30-minute webchat (sponsored by Magnus Technologies) to hear out what these freight technology experts also have to say about:

  • Optimizing your asset mix and key metrics for rapid decisioning
  • Embracing change and fostering a technology-friendly culture
  • Being more nimble to increase load margins and service levels 
  •   Increasing cash flow by accelerating invoicing and receivables workflow

Watch the webchat -- “How to Profitably Grow Your Fleet in a Recession” -- to learn more about using AI and ML to carry you through rough times.

To discover more about strategies about investing in technology to survive inflation, download our new guide: “Digital Deflation: Fighting Inflation with Next-Gen TMS Investments.”

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