At NUTC Industry Technical Workshop, Leaders Explore AI in Transportation from Visibility to Orchestration

The event panelists examined real-world AI deployments in transportation, along with the operational barriers and near-term opportunities shaping its adoption across complex networks.

On May 13, the Northwestern University Transportation Center (NUTC) hosted the Industry Technical Workshop: "AI in Transportation: From Insight to Action to Outcome; How Visibility, Execution, and Asset Networks Are (or Aren’t) Delivering Real Results." Chaired by Bret Johnson, interim director of the NUTC, the event explored the growing role of AI in managing transportation assets, operations, and systems and was held before the annual Patterson Lecture.

Moderator Jonathan Shaver, co-founder and chief business officer of Loop, a logistics data platform, said the transportation industry is reaching a turning point as AI shifts from experimentation into practical, day-to-day operations. After years focused on digitization and improving visibility, companies are now applying AI to automate processes, strengthen decision-making, anticipate disruptions, and improve real-time performance. 

Shaver emphasized that this shift matters because transportation underpins global commerce, making AI more than a technology trend—it has growing influence on costs, service quality, safety, workforce productivity, and the customer experience.

“This workshop was valuable because it brought together very different perspectives across the transportation ecosystem—from visibility platforms and brokerage networks to rail safety and asset operations—to discuss not just what AI could do, but what it is actually delivering today,” Shaver said.

Shaver moderated a discussion with panelists that included Ryan Hammett, director of market intelligence and Insights, C.H. Robinson; Larry Jordan, CEO and president, Wi-Tronix; Nick Ruggiero, director of product management, project44; and Jay Silberkleit, chief information officer, XPO. Each speaker shared their own perspective on the transportation industry’s relationship with AI.

That relationship is evolving.

AI is already delivering measurable results across transportation operations, including predictive visibility and ETA forecasting, workflow automation, pricing and network optimization, exception management, safety and risk monitoring, and customer communications.

At the same time, challenges remain in turning insights into action. Transportation networks are highly fragmented and operationally complex, so even when AI flags a problem or suggests a solution, companies often face obstacles related to aligning processes, integrating systems, and driving operational adoption.

“There’s also still an important balance between automation and human judgment,” Shaver said. “The most successful companies today are not replacing people entirely—they are augmenting experienced operators with better intelligence and faster workflows.”

Many of the strongest near-term opportunities for AI, Shaver said, are centered on practical operational improvements. Areas gaining traction include AI-supported workflows, automated exception handling, predictive ETA and network risk analysis, intelligent appointment scheduling, dynamic pricing and routing, AI-driven customer communications, and safety and compliance monitoring using computer vision and edge AI.

A key theme that emerged from the panel was the shift from visibility to orchestration. Rather than simply monitoring freight movement, the industry is increasingly adopting systems designed to recommend actions, automate decisions, and coordinate work across complex transportation networks.

“That shift—from seeing problems to actively resolving them—is where I believe AI will create the most meaningful operational value over the next several years,” Shaver said.

Shaver hopes participants would come away with a more grounded, practical understanding of where AI is already delivering real value. A key takeaway from the discussion is that AI in transportation is no longer theoretical—it is actively improving safety, speeding up operations, strengthening visibility, and enabling better decision-making.

At the same time, the conversation highlighted that effective AI adoption depends on more than technology alone; it requires embedding AI into actual workflows, organizational structures, and day-to-day business processes involving people and operations.

“I hope attendees leave recognizing that the future of transportation AI is that there is an opportunity for them,” Shaver said. “Where the physical world coincides with the AI world will continue to be a great place to be a working professional.”