Automation Gains Push Logistics Firms To Prioritize Workforce AI Literacy Over New Tools

As artificial intelligence becomes a standard part of the corporate toolkit, the source of competitive advantage is shifting. In logistics, from warehousing to freight brokerage, the operational edge now comes less from the specific tools a company adopts and more from how well its workforce can understand, test, and collaborate with them. AI literacy is now a core job requirement, and workforce upskilling is what keeps companies competitive.

Rick LaGore is a supply chain veteran with over 30 years of experience and the CEO and Co-Founder of intermodal solutions provider InTek Logistics. He believes that to navigate the coming wave of automation, companies and their employees must embrace a new way of thinking about technology’s role in their daily work.

"We won't see employees losing jobs because of AI. We'll see job losses due to not knowing how to use AI.," says LaGore. His philosophy is built on a simple strategy: aim AI where it will make the biggest impact with the least resistance. That means targeting internal, high-volume workflows in the back office while deliberately keeping it away from customer and partner interactions. The goal is to free employees for higher-value tasks, not to force AI where it doesn't belong.

• Insights from the field: LaGore’s team draws a firm line between internal automation and external interactions, shaped by what they’ve heard from the industry. "We've heard from people that truckers don't like automated call checks; it frustrates them to deal with a machine. People that are getting quotes know it's a machine, and they don't like it."

• The ten-minute turnaround: LaGore points to accessorial billing as the clearest proof that back office automation pays off. A task with 150 line items that once took a day and a half can now "be done in ten minutes," he says, and he’s blunt about why it’s the right target. "Some tasks are just incredibly boring. I can see where mistakes can happen because it's just so repetitive. Your mind goes numb and you screw something up."

But that success hides a dangerous paradox: complacency. LaGore explains that the biggest threat to a sustainable AI rollout is the false sense of security that follows such easy, dramatic wins. Easy wins breed overconfidence, tempting teams to skip the rigorous testing required for any new process. That oversight reflects a challenge many leaders cite in AI adoption and a primary reason that promising initiatives fail.

• When success fails: When a tool seems to work perfectly, the temptation is to deploy it without fully vetting its edge cases. "The success is so quick and it looks so easy that people think they don't have to fully test it," LaGore explains. "Then, when something falls apart, they jump to the conclusion that AI doesn't work. The truth is, they didn't fully test it." He emphasizes that the lesson transcends AI, applying as a fundamental principle of any technological change management. "Just because it's AI and it's a computer and it's learning doesn't make it infallible. You've got to make sure that you do your testing."

So what’s the antidote? LaGore says the solution hinges on leadership that fosters a resilient and AI-literate culture. He argues the change can't be a top-down mandate; it has to be a grassroots movement. It starts by identifying and empowering employees who are already curious, and creating a safe environment for them to experiment and learn.

• Find your champions: The most successful adoption comes from the ground up, led by those with a natural inclination to tinker. "You have to dig to find the people that are very interested, who have a curiosity. Let them take the wheel. It's really trying to get people to experiment and understand that they're going to fall down, and that that's okay," LaGore insists.

Ultimately, he sees the relationship between managers and technology changing. After initial wins, his team hit a wall with their first tools, which pushed them to adopt a new generation of technology built on intuitive natural language programming. That evolution is moving so fast that he can now envision a future where his team could build its own software—a task he previously deemed unthinkable.

The constant change points toward a new management paradigm that could see leaders managing AI agents as they would a person. "You have to train people. You also have to train AI," he concludes. "It's not perfect. People aren't perfect. But you have to shift your mindset to start thinking in that direction." The biggest risk, he says, is standing still while the world moves forward.