Evans Transportation says AI agents answered 100,000+ inbound carrier calls in months

Evans Transportation says AI agents have answered more than 100,000 inbound carrier calls over the past several months. Matt Huckeba, the company’s chief strategy officer, shared the results in an August 2026 Inbound Logistics feature on AI supply chain use cases.

From missed calls to “nearly all”: capacity coverage as an AI metric

Inbound carrier calls are a messy, high-frequency control point for freight execution. Huckeba told Inbound Logistics that, before the agents, the business missed roughly half of inbound carrier calls. With AI agents taking the first pass, he said Evans now answers nearly all of them, identifying carriers by MC number, confirming safety and setup status, screening spam and bad actors, and running an initial rate qualification against the market.

Inbound Logistics reported Evans Transportation previously missed roughly half of inbound carrier calls and now answers nearly all of them. The company said its AI agents handle the calls by identifying the carrier by MC number, confirming safety and setup status, screening out bad actors and spam, and running an initial rate qualification against the market.

Order entry is where small automation compounds fast

The second Evans example is less glamorous and often higher impact: order intake. Huckeba said staff used to manually key 100 to 120 orders per person per day. Now, integrations and AI agents extract shipment details from emails and PDFs and place cleaned data into Evans’ transportation management system, leaving the team to touch only one or two orders per day when a human decision is needed, according to Inbound Logistics.

For shippers, that kind of “touchless” intake typically shows up downstream as fewer billing disputes and a shorter order-to-tender cycle. For the provider, it changes staffing models: fewer people doing repetitive keying, more people working exceptions and shipper relationships.

Benchmarks that procurement and ops teams can pressure-test

Inbound Logistics’ broader feature also points to outside benchmarks that can help operators calibrate ROI expectations. Nicolai von Bismarck, a McKinsey & Company partner writing in the piece, cited McKinsey research indicating AI in distribution operations can reduce inventory by 20% to 30%, logistics costs by 5% to 20%, and procurement spend by 5% to 15%.

Von Bismarck also described a last-mile operator with more than 10,000 vehicles that realized $30 million to $35 million in savings from AI-powered virtual dispatcher agents, describing it as a 15x return on a $2 million investment, as reported by Inbound Logistics. That’s a helpful reference point because it ties AI spend to a fleet scale and a payback outcome, rather than to model accuracy.

Why the same tools stall in exception handling

The Inbound Logistics article draws a clear line: AI delivers the most value in structured, rules-based environments with repeatable workflows and measurable outcomes, according to McKinsey partner Nicolai von Bismarck. He wrote that AI tends to fall short in judgment-based work such as exception handling on damaged or misdirected freight, complex customs brokerage, and supplier relationship negotiations.

Supplier risk monitoring is “measurable” for 51% of CEOs, but scaling breaks on data

One concrete data point in the Inbound Logistics package comes from The Global Supply Chain Resilience Outlook report by Proxima, a procurement and supply chain consultancy that is part of Bain & Company. Inbound Logistics reported that 51% of CEOs surveyed said AI is delivering measurable value in supplier risk monitoring.

The same survey highlights why many teams can’t turn that into broad adoption: CEOs cited data quality (38%), lack of skills (30%), and clarity around ROI (29%) as barriers to scaling AI use in the supply chain, according to Proxima. The report surveyed more than 500 CEOs at companies with more than $500 million in annual revenue across the United States, UK, Australia, Singapore, and Germany, as reported by Inbound Logistics.

Where this lands in broker, 3PL, and shipper evaluation cycles

• In carrier operations: validate how AI call agents identify carriers (for example, by MC number) and what they check before presenting capacity or a quote, as described by Evans Transportation in Inbound Logistics.
• In data readiness planning: plan for data quality remediation and skills development, since Inbound Logistics cited a Proxima report that listed data quality and lack of skills among barriers to scaling AI.