Dive Brief:
- C.H. Robinson Worldwide reported a 6.6% jump in year over year profit of $141.3 million in Q4 from its North American Surface Transportation segment, the brokerage said in a Jan. 28 earnings release.
- The company credited the gain to a 3% increase in truckload volume, which was the result of productivity gains through the widespread use of artificial intelligence that allowed its workers to quickly locate higher-margin shipments, said Michael Castagnetto, president of NAST, on a call with analysts.
- “Because freight experts get information faster, it enables “them to make better decisions and to capture the optimal freight for us,” Castagnetto said.
Dive Insight:
Although the brokerage’s largest business segment finished the year on a high note, the company reported that its overall Q4 revenue fell 6.5% YoY to $3.9 billion, while income from operations fell 1.3% in the quarter to $181.4 million. The declines were primarily driven by the sale of its Europe surface transportation business, as well as lower pricing in both ocean and truckload services and lower ocean volume.
Nonetheless, executives pointed to the effectiveness of increasing AI usage and the results it achieved. In a prolonged weak freight market, the company has prioritized operating lean and tapping technology to help its workforce be more efficient, Castagnetto said.
He discussed how AI simplified the process of quickly resolving missed LTL pickups. For example, instead of staff addressing these issues, AI agents were used to track down missing shipments and determine the best ways to keep freight moving, according to Castagnetto.
“As a result, shippers' freight moves up to a day faster and return trips to pick up missed freight have been reduced by 42%,” he said.
About 95% of the brokerage’s checks on missed LTL pickups are now automated, which has saved over 350 hours of outsourced manual work per day, Castagnetto added.
He said these results “give us confidence in our ability to handle a sustained spot rate inflection better than we have in the past.”
