How AI (not automation) will revolutionize commercial trucking

Beset with labor shortages and the whims of markets and new technologies, truckers are getting a boost from digital brains.
Written by Greg Nichols, Contributing Writer

The AI market for the transportation industry is big and getting a lot bigger. In fact, it's projected to grow at a compound annual rate of nearly 18% from 2017 to 2030, with its size increasing to $10.3 billion by 2030.

Commercial trucking, beset with labor shortages and safety concerns, stands to benefit enormously, but the adoption curve is steep. While the buzz around autonomous trucks has made headlines, the reality is that AI will have a much larger impact for the foreseeable future, but it's implementation will also present challenges -- challenges that have echoes in just about any industry or sector confronting a major digital transformation. 

I caught up with Avi Geller, CEO of fleet management company Maven (Machines) and MIT alum, who believes that AI is central to solving some of the logistics industry's most pressing problems and creating efficiencies not possible before. His insights are a master class on the changes and opportunities confronting one of the sectors that's become a canary in the coal mine for AI implementation.

GN: Heading into 2021, what are the most pressing problems facing the trucking and logistics sectors? Another way to phrase this, where are the opportunities for innovation?

Avi Geller: The opportunities for innovation within the trucking and logistics sectors are endless. As an industry that has been transforming with the advent of digital solutions, companies are now reaping the benefits of technology. Some of the most important examples of digital transformation and technological implementation relate to core operational capabilities in trucking. For example, planning, route optimization, and mobile workflow tools that are used by fleets via software applications have already started demonstrating the benefits of innovation and solving some of the most pressing problems, and they will continue to do so in the future.

Route optimization software, strengthened by ongoing advancements in AI and machine learning, will continue to provide fleets with an abundance of knowledge and efficiency gains. The ability to automatically plan and optimize routes significantly better than before — all while taking the data and variables into account that only route planners and dispatchers typically know, like driver skillset and which routes are the most challenging — will give planners and dispatchers more time to focus on the unique cases that require advanced planning experience.

The concept of a "workflow" isn't new. However, truck drivers haven't always been enabled with a mobile-first workflow experience to guide them through the right steps for each stop they make on a trip. Opportunities exist to enable drivers with technology that makes their lives easier so that they can focus more on driving. Improving the driver experience has become an increasingly important initiative for fleets as they look for ways to combat the national driver shortage and retain their drivers. In turn, these cloud-based software solutions also keep fleet managers abreast of driver productivity in real time. This is a win-win for both fleet managers and drivers.

GN: What are some ways that AI, as opposed to full autonomy, can help address these problems/opportunities? Are we talking about in-truck solutions, dispatch solutions, or both?

Avi Geller: AI can be used for both in-truck and dispatch solutions. From a driver's perspective, we can use AI to build a better route for them, and increasingly, positively impact the kind of decisions that they make. It goes deeper than determining when a driver should arrive at a destination on their route though. AI algorithms can help predict the ideal time of day to schedule a delivery, taking into account a variety of factors, such as when the shipper is the least busy so that a driver is less likely to have to wait in line at a loading facility. Fleets can use AI to help drivers be more productive, while also increasing fleetwide efficiency.

GN: Autonomous trucking has been getting a lot of ink. Why do you believe AI solutions can be deployed on a more realistic timeframe than automation? What is that timeframe?

Avi Geller: We're already seeing AI solutions deployed at a rapid rate. Fleets have started to prioritize AI-assisted route planning solutions in order to better meet demand, speed up processes, and enhance the driver experience. Fleets can now take performance-based assignments and multiple variables — like traffic, weather, and road conditions — into account.

Regarding autonomous trucking solutions, I believe we'll see the adoption of remote-controlled trucking before we see fully autonomous trucks hit the market. In remote-controlled trucking, trucks are driven remotely by individuals in another location, assisted by sensors and cameras on the truck. It's an interesting use case, as remote-controlled trucking could be the bridge to getting to fully autonomous trucking, or at least a major piece of the puzzle. Most likely though, this technology will not become standard until 5G and remote driver training are more widely adopted. Both are necessary components to the success of autonomous and remote trucking.

GN: Are we seeing a market willingness to adopt these solutions? How are customers responding to the rapid technology shifts in the industry, and how are AI developers in logistics making their pitch?

Avi Geller: The trucking and transportation industries have an unfair reputation of being unable and unwilling to change when it comes to processes and technology. In my experience, fleets are looking for automation and technologically advanced solutions to help streamline their operations, allowing them to boost efficiency and profitability.

Demand is high throughout most sectors of the trucking industry. The industry is also experiencing a driver shortage, which is causing a strain on fleets as they work to meet customer demand. Many fleet executives and managers realize that the way to navigate these circumstances involves using AI, data, and analytics to their fleet's advantage. Fleets need software that decreases planning time and optimizes operations so that freight moves seamlessly. The demand for AI is only increasing as on-time pickups and deliveries become more critical and the bar for fleets to compete successfully gets higher.

GN: What do you see the industry's technological trajectory looking like over the next 5-10 years? What are the changes we can expect to see, and what kinds of adoption patterns can we expect?

Avi Geller: The big technology talking points in trucking for a while now have been autonomous and remote driving. While important and critical to the development of the industry, I believe that one of the more impactful developments over the next few years will also be in the realm of AI-powered, cloud-based fleet management solutions and automation gains. In terms of adoption patterns, we can expect to see drivers, managers, and other fleet employees become more comfortable with, and even feel empowered by, the added operational management capabilities and the seamless user experience that AI-based solutions can offer within the next 5-10 years. We will also see more cloud-based software deployments happening remotely, and more fleets will adopt digital planning, billing, and management tools as they move towards a paperless work environment.

There's still a lot of work to be done to maximize the potential of AI and machine learning though. The incorporation of predictive algorithms into these solutions will allow fleets to take a giant leap. By using historical and real-time data, instead of relying primarily on unwritten tribal knowledge, fleets can more accurately predict demand, plan shipments, and optimize routes going forward. Equipped with the predictive technology and business insight afforded by AI, fleets can gain a competitive edge by properly preparing for internal and industry-wide changes well in advance.

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