Freight AI Isn’t Replacing Brokers — Here’s the ROI

Freight AI is getting sold like a job killer. Will Bewley says the real ROI is much simpler: give brokers, dispatchers and ops teams time back. In this FreightWaves Today interview, the WoFlow co-founder breaks down where AI is actually working inside freight workflows, why we’re still early, and how carriers and 3PLs can use it to automate low-value tasks without losing the human side of the business. If your team is buried in email, paperwork, TMS work and load board responses, this is the practical AI conversation worth hearing.

Artificial intelligence is not eliminating freight broker jobs — it is redirecting them. That is the central argument Will Bewley, co-founder and CEO of Woflow, made in a recent appearance on FreightWaves Today, pushing back on what he called a distorted public conversation driven by high-profile tech executives making sweeping workforce predictions.

Woflow has spent roughly 10 years deploying machine learning across supply chain enterprises of varying sizes. Over the past six to nine months, Bewley said, the company has concentrated specifically on freight and logistics customers, working alongside 3PLs, brokerages, and carriers to identify where automation produces measurable returns — and where it does not.

The ROI case Bewley makes is granular. If a dispatcher previously spent 15 minutes completing a load build in a TMS, automating that task does not eliminate the position — it returns 15 minutes to the employee’s day. “They get to spend that 15 minutes maybe making new connections, maybe new business growth, business development with their existing shippers or carriers,” Bewley said. Brokerages, he added, consistently tell Woflow they want more time for customer service and carrier relationship management, not less time — the manual work in their systems is what crowds that out.

“My bet still is that we’re still years and years away from actually completely replacing a human end-to-end for what they actually do as a specific job.”

Bewley described Woflow’s approach as deliberately non-templated. His team shadows operations staff on the floor before recommending automation targets, because, he argued, the last 20% of any workflow — the part that diverges from the written SOP — represents 80% of the business value. That insight shapes which processes get automated and which stay human-led. He noted that attendance at AI-focused sessions at a recent TMS user conference in Nashville was standing-room only, signaling that freight operators are “very AI curious” even if public-facing messaging around the technology remains cautious.

On the job displacement question, Bewley pointed to software engineering as an instructive data point. Despite AI becoming most capable first in coding, the number of software engineer job postings has grown, not shrunk, he said, because the productivity multiplier makes it economically viable for companies that never previously employed engineers to hire them. He expects a similar dynamic to play out across freight roles.

The data center construction wave reinforces that view. Bewley cited figures showing U.S. data center capacity currently stands at 37 gigawatts and is forecast to reach 80 gigawatts by next year — nearly doubling in 12 months. Facilities of that scale require continuous inbound freight: servers, GPUs, copper wiring, and cooling equipment, with GPU refresh cycles meaning the loads are not one-time moves. “Transportation partners that can deliver on that is critical for that buildout,” Bewley said, pointing to projects already underway in the Memphis area as an example of activity visible to regional carriers today.

• Woflow’s Will Bewley says AI automates manual freight tasks — such as load builds and inbound email replies — to return time to brokers and dispatchers, not replace them.
• U.S. data center capacity is projected to grow from 37 gigawatts to 80 gigawatts by next year, creating sustained freight demand for equipment and GPU refresh cycles.
• Bewley argues AI adoption in freight is still in early innings, with productivity gains not yet showing up broadly in GDP, and full end-to-end job replacement still years away.

This Summary is generated thanks to a transcription of the interview, for the full interview please enjoy the video above.