Insider Q&A: Create a “frictionless environment” for customers and carrier partners
Brian Work, CTO at Transportation Insight and Nolan Transportation Group, discusses their efforts to enhance end-to-end visibility and create a frictionless environment for shippers and carriers using the Beon™ Digital Logistics Platform, with a focus on leveraging artificial intelligence in 2024.
Transportation Insight (TI) & Nolan Transportation Group (NTG)
Brian Work, CTO, Transportation Insight & NTG
In our fast-paced world, customer expectations are not only at an all-time high, they’re also constantly evolving. Everyone wants their orders quickly, accurately and with minimal friction. This presents unique challenges for shippers whose supply chains may be stretched across business partners, geographies and transportation modes. One challenge that continues to persist is knowing “where product is” and “where it’s going to go next.”
It’s challenging due to the fragmentation of the transportation and supply chain environment. A lot of different players at various levels of the supply chain contribute to the visibility gaps and ultimately impact a shipper’s ability to gain end-to-end visibility.
For over two decades, Transportation Insight (TI) and Nolan Transportation Group (NTG) have been working to create a “frictionless environment” for customers and carrier partners.
Over the past three years, TI and NTG have been developing technology to solve this issue. They have done so for 14,000 shippers and 80,000 carrier partners through their proprietary Beon™ Digital Logistics Platform. This platform serves as a single point of access to their mode-agnostic network, offering services from port to porch™.
In this Insider Q&A, Brian Work, CTO at TI and NTG, discusses the two companies’ primary focuses for 2024, how they’re helping shippers attain their visibility goals and the important role that artificial intelligence is playing—and will continue to play—in the logistics and transportation industries.
Q: From a technology perspective, what projects are TI and NTG working on this year?
A: We have spent the last few years building and refining our technology stack largely centered around the Beon Digital Logistics Platform along with some key integration and technology partners. Now in 2024, our focus moves towards adoption and extracting value out of our investment and platform. The most obvious reasons for digital intervention are to automate tasks and reduce the cost to serve. For example, an increase in digital load matching within our Beon Carrier application means that our brokers can spend less time finding trucks to cover loads and more time engaging with our customers.
However, digital matching is not just to the benefit of our brokers. It benefits our carrier partners because it gives them access to loads that are on our board. It lets them book, execute and get paid for hauling those loads. Then our customers benefit from the reliable on-time coverage and ultimately everyone wins.
We also extract value from the platform through integration and interoperability with other technology and freight technology innovators. We don’t build everything; we partner with
other organizations to benefit from what they’ve built. Using this approach, we built a platform with a highly interoperable architecture that’s easy to integrate with. We not only benefit from the investments that others are making in emerging technologies. We also can seamlessly integrate with third-party applications, which helps to accelerate our business operations and enhances our ability to serve our customers.
Q: What types of technology providers are you partnering with?
A: We partner with companies like Salesforce and Snowflake and other large players in the tech space. Doing so allows us to benefit from the massive investments that these companies are making in their platforms. We also work with smaller niche freight tech providers that are on the leading edge and helping to push our industry forward.
This combination really allows us to spin our flywheel faster than we could if we tried to do everything ourselves. These forces have converged to put us in a unique place and time in the history of logistics technology that’s frankly, really exciting. The possibilities are
truly endless.
Q: What other transportation technology gaps do you see companies dealing with right now?
A: Industry fragmentation and lack of investment remain inhibitors to good visibility. Many transportation providers in the U.S. are small owner-operators that can’t make major technology investments. By creating solutions for them like the ability to put devices in their trucks/trailers, they can participate in the freight economy in a slightly different way than they’ve been used to.
The good news is that visibility can coalesce some of this fragmentation. As companies continue to invest in successful technology implementations, and as new regulations emerge and force more companies to “see where things are” in the supply chain, the shippers will be the ultimate winners of those efforts.
Q: AI continues to be at the forefront of a lot of businesses, especially in logistics. How are Transportation Insight and Nolan Transportation Group using artificial intelligence?
A: Right now, everyone is thinking about AI in many different capacities. For starters, we have our own data science team whose internal expertise has helped us in different ways. One advantage we have is decades of data, which some newer market entrants may not have. Through our data science team and technology, we are leveraging data to create our dynamic pricing models within Beon Shipper and freight matching algorithms within Beon Carrier. We leverage it for activities like dynamic route optimization, which carriers can use to improve density. On the Transportation Insight side, we apply that capability on behalf of our customers’ supply chains as part of our managed transportation consultative engagements.
Right now, natural language processing (NLP) and large language models (LLMs) are converging to create opportunities for nearly seamless chat capabilities, both through text engagements, but also through voice. There are a few players in the freight technology space that are hyper-focused on solving that problem. It can be used when drivers can’t put the phone down and type while they’re driving. Instead, they can just call in and interact with a human-like voice response unit. It’s a tremendous opportunity, and one we’re closely exploring to streamline collaboration our customers and carrier partners.
Q: How are you using AI with your own internal workforce?
A: We’re using generative AI in our own coding environment, where we use GitHub Copilot to accelerate our own internal software development.With GitHub Copilot, we’ve seen a 20% to 30% decrease in development time required for features. This allows us to deploy new features and enhancements faster than ever before.
We are also developing our own internal copilots to facilitate our internal jobs. In about two weeks, we’re going to launch the first Beon copilot that we’ve developed internally for Beon Operating System, our internal backend operating system.
The Beon copilot uses NLP and LLM to interpret incoming quote requests and helps the brokers shape responses to their customers. There will still be a human in the loop, which is important because our business is based on relationships. We don’t see this as a replacement for the human element, more so giving them tools to be more productive and efficient. Then we will be able to gain feedback and learnings from our team to iterate and improve the model.
Beyond copilots, around 85% of our shipments were intelligently and digitally matched within our network of 80,000 vetted carriers over the last year. It’s a meaningful way we are seeing productivity gains and improvements across the board through AI.
Q: What role is generative AI playing in the transportation technology environment?
A: Unlike a lot of other technologies that were slow to emerge and gain traction, generative AI is a space that companies can’t afford the luxury of waiting to catch up with. Anyone that takes this approach will be left behind. Generative AI is real and impactful and it’s going to permeate our industry; there’s no doubt about that. I think companies should pay attention to it and not put it on the back burner.
Q: How can companies start leveraging the power of generative AI in their supply chains?
A: Start by thinking about what you generate. In our case, it’s relationships, data and both written and verbal communications. So we asked ourselves, how do we improve customer interactions and customer responses through generative AI backed by data science? And how can we use our pricing algorithms and matching algorithms to make the whole supply chain work more seamlessly?
We’re also using other companies’ generative AI innovations to focus on specific problems that are outside of our purview. For example, we use a modern cloud-hosted call system with built-in sentiment analysis capability. When a customer calls in, we can tell immediately by their voice or tone if their problem is urgent. We can have a supervisor intervene with a rep who is in the middle of a conversation. This is just one way we’re leveraging the outside generative AI innovations that companies are developing outside of the logistics technology realm.
Q: What else should companies know about the use of generative AI in the modern supply chain?
A: As bullish as I am on the benefits of rapid advancements in generative AI, I also want to share a cautionary note: these are extremely powerful tools, and they will become more powerful over time. Newton’s third law teaches us that for every action there is an equal and opposite reaction. That law applies here, as well. As fast as companies are developing these technologies, bad actors are developing ways to both leverage and penetrate these tools with malevolent intent.
We have to be mindful that, in many cases, generative AI developers are focused on rapid deployment of “happy path” use cases and are spending a lower proportion of their time on building protections. Negative testing, red team pen-testing and third-party white hat hacking are necessary. When selecting your generative AI partners, be sure to evaluate how these protections are being applied throughout the development lifecycle, and how you will address them yourself post-deployment.
