The transport and logistics industry is a complex space not known for tech innovation, but that’s why it’s ripe for change. Automation and AI are giving companies new ways to connect with customers and win more business in a competitive market.
In a vertical that relies on quoting to attract and gain customers, AI agents can analyze historical data, customer behavior, and patterns to optimize pricing and speed.
“You have customers who are going to make a decision off of how fast you return those quotes,” Foster Kaman, VP of Industry, Transportation & Logistics at consultancy Bridgenext told CX Today in an interview. “A lot of times it will come down to people losing [the business] because they’re too slow to the draw.”
T&L organizations can set up AI-based quoting agents that automate the process to respond as soon as a request comes in. Kaman said:
“You’re taking it from losing business because you weren’t the first to the table to now getting opportunities you’ve never had the ability to get.”
“As it learns your customer, it will be able to help make those quotes more accurate so that you’re not having those billing disputes.”
In addition to automating quotes, AI is helping sales teams spend less time on prep and more time on actual customer conversations, providing them with actionable insights and structured guidance for each customer interaction.
“We’ve had customers that have put call plan creation into action to where it eliminates the time that those great reps are having to spend on prep… and for those reps that have not ever done it, it teaches them how to be better at a sales call,” Kaman said.
AI is also making it easier for teams to pull together documents and quickly get summaries of accounts, opportunities, and cases, Kaman said. “If we can speed that process up and make it easier for everybody, that’s going to change the way people work in the market.”
How Logistics Firms Can Tackle Siloed Systems
The first issue to address for T&L firms to get started with AI is preparing their data strategy to get the most out of it, as they tend to struggle with cleaning up data that is scattered across systems, siloed and inconsistent.
“One of the big things that I found for T&L companies is being able to take their data out of silos and truly pull their data into one spot so that they can take action upon it,” Kaman said.
“Yet for AI, clean data is the foundation of every decision it makes. The reality is that AI is only as powerful as the data and processes behind it. If your data is fragmented and your processes are broken, your insights will be too.”
When various departments are working independently with their own datasets, “it’s very hard to put a complete 360 of a customer together to where you can move forward,” Kaman added.
“The good news? You don’t need to be perfect to begin. If perfection were the starting point, we would never get started. What matters is taking that first step with intention and as you move forward—improving your data and processes with each step you take—will uncover more value. This is because with every improvement in data quality, your AI becomes more insightful and impactful.”
For enterprises concerned about letting AI loose on customers—particularly given high-profile instances of customer-facing agents making costly mistakes—Kaman advises starting with internal use cases to speed up processes, so that teams for more comfortable.
“Internal agents are a great way to get started, so that you recognize what’s working well, what’s not working well, [and] what needs to be tweaked so that you can take that and continue to move it forward.”
Focus on Outcomes, Not Just Use Cases
Some T&L companies are making the mistake of taking disjointed approaches to AI projects, in part because they’re being sold a plethora of solutions based on use cases rather outcomes.
“There are a lot of players in the AI world right now,” Kaman noted. “We all know that, and some are going with—instead of doing it as an overall tool itself and looking at it and saying we need something that’s going to pull all of our data together… and then be able to give us insight into our customers—they’re going based off of what the use case is, instead of what the outcomes are that they’re looking for.”
But vendors are responding by adapting their approach, to help tech buyers identify the right tools to achieve the outcomes they’re targeting, Kaman said.
“Some of the leaders in the AI space are coming back and recognizing that maybe that was not the best approach to take, because they’re recognizing now that when we ask you, ‘what is your use case, what are you wanting to solve for?’ at the end of the day, it’s, ‘what are you wanting to accomplish, and how is it going to help you accomplish your goals?’ And if you don’t know… then how are you going to get there?”
Enterprises need to avoid the trap of assuming that AI agents are out-of-the-box solutions, “that you just put an agent in there in the first day, and then it’s just miraculously going to it’s going to change your business.”
“AI is not going to be something that you put in, and then you don’t ever get to touch again. It is going to continue to evolve,” Kaman advised. “There’s going to be tweaks that you need to make, because you didn’t go in there and give it the directions the way that you should have, or you may not have put the guardrails on.”
“It’s going to take a whole team, and it’s going to have to take a focus on,’ how are we truly going to get to that outcome?’ And it may not be in the very first time that you’re introducing that AI agent.”
Proving ROI on customer experience can be tricky. Everyone agrees that better service matters, and logistics leaders know that improving service often pays off in loyalty and efficiency, but putting a number on that isn’t always straightforward.
The trick, Kaman said, is to “start building backwards.” In that way, leaders can avoid the mistakes of some companies that have been unable to produce measurable results.
“A lot of them are putting solutions in place, but don’t even understand what they’re trying to measure against,” Kaman noted. “You’ve got to truly understand… what does success look like, and ultimately, how is it going to help you hit your goals? What are you expecting out of it?”
Turning Compliance from Burden to Advantage
For industries like T&L, where compliance is critical and time consuming, there are also opportunities to integrate automation and AI into compliance processes in ways that can enhance customer interactions as well as business performance.
Leaders need to work together to understand how to modernize their systems without increasing complexity. “Start with a phased approach that allows for a gradual transformation,” Kaman said. “You use AI or automation to reduce the manual errors and improve the audit readiness.”
In the process of ensuring systems are compliant, enterprises can create systems that provide actionable insights that feed into improved customer experience.
“Centralizing your data so that you can get that better visibility, that better reporting, and it allows you to take action on it quickly, is so very important,” Kaman said.
Working with teams throughout the process of introducing AI is critical for projects to be effective.
“Change management and training is essential, because if you’re not doing that, you’re not ensuring a sustainable adoption.”
Communicating with and training employees on AI use is crucial, because they are likely to use AI tools in some form even if there is no corporate policy providing guidelines.
“The longer you wait, the more unsecure your company is,” Kaman warned. “People use AI whether or not they recognize that they’re using AI.”
“For you to think that your teams aren’t using it, you’re probably kidding yourselves, because they are. They’re not using it under a secured arena.”
For logistics leaders, AI isn’t a question of if but how. Embracing it strategically, and securely, can mean the difference between keeping pace and getting left behind.
