Earlier this summer, Busline magazine asked me to write about artificial intelligence and how it is beginning to influence the bus and motorcoach industry.
It was a timely assignment. Artificial intelligence has become one of those terms that seems to be everywhere. Technology companies are talking about it. Operators are experimenting with it. Conference agendas are filled with it. Scroll through LinkedIn for a few minutes, and you probably won’t have to wait long before someone tells you how AI is going to transform business.
But the more I thought about the assignment, the more I realized I didn’t want to write another article about the promise of AI.
I wanted to understand what it actually means for our industry.
So I started having conversations.
I spoke with technology leaders developing tools for transportation companies and operators running businesses of different sizes and complexity. Some were already deeply engaged with AI. Others were still trying to understand where it might fit. Their perspectives weren’t identical, nor should they have been.
What interested me was where those perspectives began to intersect.
The resulting Busline article had to fit within roughly 1,000 words. But the conversations left me with far more material—and more questions—than I could possibly explore there.
This is the conversation that didn’t fit into 1,000 words.
We’ve Been Here Before—But This Feels Different
Over the course of my career, I’ve watched technology evolve from rotary phones to smartphones. I remember when caller ID, call waiting, and dial-up internet were revolutionary. I remember life before personal computers, email, GPS navigation, and the internet itself.
Each innovation reshaped the way we worked.
The transportation industry evolved in its own way. Dispatch and reservation systems became increasingly sophisticated. GPS and telematics gave operators visibility into their fleets that would once have been unimaginable. Mobile technology changed how we communicate with drivers and customers. Technology transformed nearly every operational function.
Some operators embraced those changes quickly. Others took a more measured approach, waiting until the technology proved itself. Eventually, each innovation simply became part of operating a modern transportation business.
AI feels like the next chapter, but with an important difference.
It isn’t simply changing the tools we use.
It’s beginning to change what those tools can do for us.
And the pace of that change appears to be accelerating.
First, We Have to Get Beyond the Hype
One challenge with talking about artificial intelligence is that the term itself has become so broad that it can mean almost anything.
AI can describe the camera system identifying unsafe driving behavior. It can describe software analyzing years of reservation data. It can describe a chatbot answering customer questions. It can describe ChatGPT drafting an email. And increasingly, it can describe agents capable of taking actions across multiple business systems.
Put all of that under one label, and it’s easy to understand why people are simultaneously excited, skeptical, curious, and confused.
Louis Bookoff, co-founder of Busie, offered an interesting perspective when I asked where he sees AI creating the most value. He actually started by talking about where it doesn’t.
Writing emails and generating marketing content have become what he described as almost “table stakes.” He also pointed to the growing amount of obvious AI-generated content—what he called “AI slop”—and the resulting fatigue it can create. His larger point was that people still crave authentic human interaction.
That struck me because so much of the public conversation about AI begins with generative tools. Ask someone how they’re using AI and the answer is often some version of writing emails, creating social media content, summarizing documents, or conducting research.
Those are useful applications. I use many of them myself.
But they’re only the beginning.
Louis described work around pricing, dispatch, fleet allocation, accounting, and even identifying events that could create new sales opportunities for operators. In the latter case, AI can identify relevant events, find potential contacts, and help manage a process that would be extremely time-consuming to perform manually.
His distinction was important: the closer AI gets to critical workflows, the higher the stakes become. General AI has value, but use-case-specific applications make the potential easier to understand—and demand greater thoughtfulness.
That may be one of the first lessons for operators trying to make sense of all of this.
Don’t start with AI.
Start with the work.
The Opportunity Is Already Inside the Business
My conversation with Niall McKeever of Zaui added another dimension.
Niall spent many years as an operator in his native Northern Ireland before moving to the technology side of the industry. That background gives him an interesting perspective because he understands both what the technology can do and the realities of running a transportation business day after day.
Much of our discussion centered on something transportation companies already possess in abundance: data.
Think about how much information an established operator accumulates.
Trips. Customers. Routes. Pricing. Seasonal demand. Maintenance. Driver activity. Sales history. Cancellations. Utilization.
For years, much of that information has existed primarily as a historical record. AI makes it possible to use it much more dynamically.
Niall described systems capable of examining years of past performance around a holiday, for example, and using that history to forecast schedules and routes. He also discussed the potential for that same intelligence to inform dynamic pricing.
That’s a fundamentally different value proposition from asking an AI tool to write an email.
The information already exists.
The opportunity is to understand it better.
And that idea surfaced elsewhere.
Bill Adams of First Class Charter talked about wanting greater integration between dispatch, maintenance, drivers, and finance. His vision was a system that could monitor maintenance intervals and help the organization become proactive before a mechanical issue turns into what he called a “chaos situation.” Importantly, he immediately connected that operational visibility back to protecting margin and profitability.
Bill also saw opportunity on the other side of the business: customers.
First Class has hundreds of customers in its database. As Bill pointed out, no one can manually stay in meaningful contact with all of them or continuously monitor who traveled six months ago, much less two years ago. He sees AI helping identify customer patterns, determine how individual customers prefer to be contacted, and maintain relationships at a scale that would be difficult to accomplish manually.
He also connected customer history to pricing—using patterns in demand and travel behavior to become more confident about peak and off-season rates rather than defaulting to discounting to win business.
That’s not replacing sales.
It’s giving salespeople better information about where to focus their attention.
AI and Automation Aren’t the Same Thing
Another important distinction came from Ben O’Day of Initek Consulting.
For years, companies like Initek have helped organizations connect systems, move and normalize data, and automate processes. None of that began with generative AI.
What’s changing is how quickly and flexibly that work can now be done.
Ben described how building an adapter to connect a new telematics platform might previously have required multiple days of software development. With AI-assisted development tools, that work can happen much faster. But he raised an even more interesting question: in some situations, does a traditional software integration need to be built at all, or can an AI agent perform the work another way?
That distinction matters because AI didn’t invent automation.
What it is doing is lowering barriers that once made sophisticated automation difficult, expensive, or inaccessible—particularly for smaller organizations.
Ben also sees an interesting divide in how operators are approaching the technology. Larger organizations may have internal resources that allow them to experiment and develop applications themselves. Smaller operators, meanwhile, may know they need to understand AI but not know where to begin, increasingly looking to their technology partners for guidance.
That may be one of the most important challenges surrounding AI right now.
The technology is advancing faster than many organizations can reasonably absorb it.
Get on the AI Bus—or Get Run Over by It?
Niall described a pressure to learn AI that I suspect many people are feeling.
The message can sometimes sound like: AI is here. Get on board.
For those of us in transportation, there’s probably an even more appropriate metaphor.
At times, it can feel like get on the AI bus or get run over by it.
I don’t think those are the only options.
Niall compared some of the anxiety surrounding AI with earlier technology transitions. There was a time when businesses were being told they needed a website. Later came mobile technology and other digital tools that created new expectations for how companies operated and interacted with customers. Each transition created its own uncertainty and its own pressure to keep up.
AI may be moving faster, but the leadership challenge is familiar.
Operators don’t have to automate their entire back office tomorrow.
They also can’t afford to simply ignore what is happening.
Somewhere between chasing every new AI application and dismissing the technology altogether is a much more practical path: understand what is becoming possible, identify the problems in your own organization worth solving, and begin experimenting where the potential value is clear.
Operators are already showing just how far that experimentation can go.
