Grant Goodale has spent over two decades chasing what he calls “interesting hard problems.” In an interview with FreightWaves, Goodale spoke about tackling everything from military contracts requiring security clearances to bank firewall software to running a game studio. But logistics, he said, might finally be the industry that sticks.
One reason? Logistics can encompass just about everything. “I used to tell people up until Convoy I had a hard time explaining to anybody what I did. It was all very abstract,” said Goodale, chief product & technology officer of Ryder Technology. “And here it was like, no, literally everything in every room, it doesn’t matter what room you’re sitting in, I point to something — that’s been on five to seven trucks by the time it gets to you.”
Goodale co-founded Convoy in 2015 after watching Amazon’s two-day Prime delivery reshape consumer expectations while working on the retail website side of the business. Now at Ryder, where he leads the Baton innovation lab acquired in 2022, he sees artificial intelligence as the fourth major technology wave to hit the freight industry. It is also potentially the most transformative.
“By making transportation more efficient, you can affect pretty much any chunk of GDP you care to because transportation touches almost all of GDP,” Goodale said.
The Four Waves of Logistics Technology
Goodale breaks down technology’s assault on freight into distinct eras, each defined by a breakthrough that promised to reshape how the industry operates.
The first wave arrived around 2010 to 2012, led by companies like Coyote Logistics, which focused on “getting data in order and putting dashboards in front of people that help them make smarter decisions on a regular basis.”
The second wave came in 2015 with smartphones. “We don’t have to ship a GPS puck to every broker or carrier in the country when we need them to do a load,” Goodale said. “They have an app in their phone. That’s a sensor platform. I can use that to my advantage.”
Wave three emerged in 2018 around blockchain. “There was a lot of energy and capital poured into ‘can we find ways to make blockchain improve transportation’ — chain of custody, contracts for payment,” he said. “I think that the impact there wound up not being as high as most people had hoped despite there being a lot of promising use cases.”
The fourth wave — the current AI revolution — represents something fundamentally different, one that Goodale believes could finally deliver on the promise of more efficient logistics operations.
LLMs and the Death of the Inbox Problem
Goodale draws a critical distinction between broad AI applications and the capabilities of large language models (LLMs). Machine learning has already embedded itself into forecasting, planning and estimated time of arrival (ETA) calculations across the industry. Large language models bring something new: the ability to process messy, unstructured input into predictable output.
“AI, first and foremost, is a very broad term that encompasses a wide variety of technical approaches to solving problems,” Goodale said. “So that goes from machine-learned models that can help provide more accurate ETAs all the way up to the new hotness, which are sort of the large language models and the ability to process plain English into mostly deterministic outcomes.”
The practical application becomes clear in everyday brokerage operations.
“I might have a team that monitors an inbox that gets all sorts of questions about the status of a load,” Goodale said. “Somebody’s gonna send me a spreadsheet with 75 loads in it. Somebody’s gonna just paste some load IDs in there. Somebody’s just gonna say, ‘Tell me the status of all my loads.’”
Previously, companies faced a choice: build expensive electronic data interchange connections or hire more humans. Now, Goodale said, “you can feed those emails to an LLM with some training. And if you give it access to your system, it can pretty reliably give the results to the customer and in a fraction of the time.”
This eliminates much of the integration cost that traditionally slowed business relationships. The old model required value-added networks sitting in the middle, translating how one company understands EDI messages into how another company understands them.
“But customer A and customer B think about the world in different ways. They send different values. There’s a lot of money to be made in sitting in the middle there and translating everybody into your language and vice versa,” Goodale said. “But now all of that kind of goes away because we have this ability to put something that’s vastly more capable in that flow of information to handle the inbound message.”
The bottom line: “I don’t need an API. I don’t need EDI. What I need is something that can handle whatever you send to me in any format, understand it, comprehend it, and then go take action based on it or hand it to a human to take action.”
Humans Still Matter in an Automated World
Despite the efficiency gains, Goodale pushes back against AI replacement narratives. At Convoy, the vast majority of staffing remained operations personnel “solving problems every day — damaged goods, over, short and damaged (OS&D) or detention at the facility.”
“The ability for humans to exercise judgment in exceptional situations is pretty unparalleled even today,” he said. “You can have policy, but policy has to work in the real world, and humans are generally better at figuring out how to adapt.”
Could he have built a lot less technology from the ground up if today’s AI existed then? “Yes. Absolutely,” Goodale said. “Frankly, many of the [US based] startups over here are started by ex-Convoy people who saw something we had to build because nobody built it and then turned that into a company because other companies had the same need.”
The real impact he sees isn’t about replacing workers wholesale: “The technology is enabling us to do something that a human would have taken four hours to do in a few minutes because we can run it in parallel. We can do 10 phone calls at a time instead of one. We can sort of scale out horizontally more effectively.”
The pattern echoes historical technology adoption. One example: the high watermark for bank tellers came decades after the ATM arrived. History suggests AI will likely evolve jobs rather than simply eliminate them.
“So it’s not about replacing the person,” he said. “The person’s just not having to go through a lot of grunt work in order to get to the point where they add the most value, which is that judgment point.”
The Data Foundation That Can’t Be Skipped
For companies eyeing AI implementation, Goodale offers a blunt assessment: get your data house in order first.
“If you don’t have great clean data and it’s not in a place where it’s accessible for these systems to operate on, then anything that you do — whether it’s a process change, whether it’s a people change, or whether it’s a technology implementation — you’re gonna get worse results than you could have otherwise,” Goodale said. “If you have data silos that you need to break down, you should probably focus on that first. You have to lay the groundwork and invest if you’re gonna get to the point where you’re able to actually create impact.”
He warns against chasing first-mover advantage in a landscape where change happens quickly.
“I don’t know that being a first mover generates lasting advantage in an industry or in an environment where change is happening this quickly,” Goodale said.
What’s Next for Ryder’s Innovation Lab
Baton operates as Ryder’s innovation lab, leveraging what Goodale called “probably the broadest transportation product portfolio in the industry” to identify where technology creates the most leverage.
“We are focused on solving some of the hardest problems in freight, really given the sort of breadth of Ryder’s businesses,” Goodale said. “We get customers with incredibly complex needs, and so we have the opportunity to sort of look across all of those solutions and say, you know, where does technology generate the most leverage?”
While specific projects remain under wraps, the timeline for announcements is clear.
“Over the course of the next few years, you’re going to see Ryder’s software and services evolve in ways that reflect the new AI tools we’re mastering today,” Goodale said.
The focus: “How do we help our customers get more actionable data? How do we help our customers make decisions more intelligently? How do we help them plan and forecast more effectively? How can we be their best partner in the world of logistics using technology?”
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