August 27, 2019 • by Deborah Lockridge
Read more here
Tom Curee says artificial intelligence can help transportation companies deal with the “analysis paralysis” of overwhelming data.
Photo by Deborah Lockridge
If the words “artificial intelligence” conjure up an image of Skynet in the Terminator movies, it’s time to think again. There are real-world transportation companies using AI to do their work better, faster, and more effectively – and they could be your competition.
Artificial intelligence is not building computers in such a way to wipe out the human race, said Tom Curee, vice president of strategic development at Kingsgate Logistics, during a session on using AI to solve problems held during McLeod Software’s 2019 user conference in Denver.
“Really, it’s just the development of systems to perform tasks that normally require human intelligence,” he explained. In fact, AI is a concept that’s been around since the 1950s. It only has gained steam in the last few years with the availability of big data, he said.
“I’m getting data from so many sources, I can’t keep up with them. What has brought AI back to the forefront is that you can train it and teach it and code it to evaluate the data for you and make decisions.”
There are subsets of artificial intelligence called machine learning and deep learning. In machine learning, algorithms and software can automatically learn and improve from experience without being explicitly programmed. Deep learning goes beyond that and gets into artificial neural networks, algorithms inspired by the human brain, that learn from large amounts of data.
Matching trucks and loads is a major way artificial intelligence can be used in transportation and logistics.
Photo by Deborah Lockridge
Curee cited as an example how AI can be used by brokers or shippers what want to find the best truck to move a particular shipment. The computer can be taught to make a prediction for which carrier is the best match, based on factors such as location, type of equipment, profitability, driver hours of service remaining, driver preferences, and so on. With machine learning, the algorithm also learns over time and adjusts its recommendations based on which carrier or driver you chose.
“Over the years, we’ve made so many decisions off of our gut, and now we have data that may tell us something different,” Curee said.
Real-World Examples of AI in Transportation
Kingsgate Logistics is a non-asset-based freight broker, working with both truckload and less-than-truckload carriers. Curee notes, however, that he actually first got interested in AI when trying to address a serious driver turnover problem for a refrigerated carrier.
Kingsgate, which has 90 people in its brokerage business, has in the past year made a serious investment in leveraging technology. In April, Curee said, the technology team consisted of three people. Today, it’s 14, “because we see the need of what we’re trying to do with technology to keep up.”
The company is already using AI in six areas and has three more it has identified to work on. Curee shared examples of some of the ways Kingsgate is using artificial intelligence and machine learning, many of which would apply to asset-based trucking fleets as well as to brokerage operations.
Recruiting: It’s not just truck drivers who are hard to recruit. With the tight labor market, all types of positions are a challenge to fill – and sifting through hundreds of applications or resumes is time consuming. In fact, by the time you find someone who looks like the perfect fit, he or she may already have taken a job elsewhere. With AI, you can tell it what parameters you’re looking for, such as the types of previous job titles, skills, experience, etc.
Think about how this could work: Look at the people who have been most successful in your organization, who worked best with the team, at their skillsets and backgrounds, and teach it what you’re looking for. In addition, you also give a thumbs-up or thumbs-down to the recommendations it comes up with to help it learn further.
Sales: One of the ways Kingsgate is leveraging AI in sales is simply by figuring out the best time of day to call a prospect. It pulls data such as social media activity, when the prospect opened an email from McLeod, when they have visited the website, etc.
Ai is similarly being leveraged to identify new customer prospects that are a good match for Kinggate. And it’s being used to create a prediction score on how likely customers are to be a fit for the company. “How much value would you have if you could stop your sales reps two months earlier on an account that just isn’t ever going to be a fit?” Curee said.
Kingsgate is also using AI to analyze recorded calls that have been transcribed via a speech-to-text program. They are analyzing what the company’s most successful sales reps do and coaching less-successful reps in these areas. For instance, it’s finding that the most successful reps spend less time on small talk and more time talking about market insights.
Social Media: In its marketing efforts, Kingsgate uses the technology to choose the best times to publish its social media posts based on the audience it is trying to reach.
Personalized Websites: Still in the early stages is dynamic personalization of its website. When people come to the website, technology tracks their IP addresses and can often cross-reference to the company’s CRM (customer relationship management) data to identify what industry they’re in. So a flatbed carrier would get shown a different version of the website than a refrigerated carrier, each optimized to appeal to that specific market.
IT Support: Although it started as an internal function, Kingsgate is in the process of making this customer-facing as well. The AI can process tech support requests and make a suggested answer based on the company’s knowledge base as well as on previous similar tech requests. Right now it still takes a human in the support department to OK that answer and send it to the person needing help, but in the future, Curee said, it will handle some of these questions automatically.
The Future is Now
“There’s virtually no major industry that modern AI hasn’t already affected,” Curee said. “We have so much data flowing at us now, and everyone is asking, ‘How do I mobilize this data, how do I make decisions from it? AI makes your data actionable. It’s not always making a decision for you; it’s helping you be able to make the decisions you need to make.”
In fact, he said, your company’s ability to adopt and adapt to this technology will be crucial to your success in the next five to 10 years.
A big part of that is going to be “reskilling” your workforce, he said. “It’s not good enough that the tech team can implement it.” Front-line workers don’t need to know how to program it, any more than they need to be able to build a computer in order to use it, but they do need to understand what artificial intelligence is and how to use it to improve their day-to-day experience.