Global News Freightwaves AI moving from back office
to driver’s seat in trucking operations

Noi Mahoney, Thursday, April 16, 2026
Original Article: https://www.freightwaves.com/news/ai-moving-from-back-office-to-drivers-seat-in-trucking-operations
Articles Reproduced by Permission of FreightWaves

AI tools are beginning to execute more core trucking functions — from document processing to load booking — marking a shift from decision support to operational control. (Photo: Jim Allen/FreightWaves)

Datatruck, Magnus and project44 signal shift as AI automates dispatch, paperwork and load decisions

The trucking industry’s slow shift from manual workflows to digital operations is entering a new phase — one where artificial intelligence is no longer just analyzing freight data but beginning to execute core tasks traditionally handled by dispatchers and back-office staff.

For carriers and brokers, the challenge is no longer whether to adopt AI, but how quickly they can integrate it into workflows without losing control — or falling behind competitors that do.

From manual paperwork to real-time automation

For Datatruck co-founder and Chief AI Officer Ulugbek Ergashev, document processing is the clearest example of AI’s immediate impact.

Datatruck’s recent update to its TruckGPT platform highlights the trend. The AI-powered tool can now read and process key freight documents — including rate confirmations, bills of lading and proofs of delivery — in seconds, eliminating manual data entry and flagging discrepancies before they reach accounting or factoring.

“Every rate confirmation, every BOL, every POD — our system reads that automatically and extracts the data,” Ergashev told FreightWaves in an interview.

Irving, Texas-based Datatruck is an all-in-one transportation management system (TMS) built to automate back-office operations for trucking companies and freight brokers.

The company’s platform is now processing about 10,000 documents daily, he said, replacing manual workflows that once took several minutes per load.

“Before, it took about three to five minutes. Today it takes about 10 to 15 seconds,” Ergashev said.

That automation is particularly critical in back-office functions like invoice validation, where missing or incorrect documentation can lead to rejected payments.

“If your POD is not qualitative enough... factoring companies may reject your invoice,” he said. “Today, this routine task is eliminated.”

Related: project44 takes key step toward an AI-native supply chain AI begins assisting dispatcher tasks

Beyond paperwork, AI is increasingly handling tasks traditionally managed by dispatchers, including check calls, status updates and load searches.

Datatruck’s platform automates broker communications and provides real-time shipment updates, answering questions such as location and estimated arrival times without human involvement.

“We automated broker communication... status updates, check calls, responding where my truck is,” Ergashev said.

Still, he emphasized that full autonomy remains limited.

“I believe that AI is not ready yet to talk to brokers and negotiate the loads,” Ergashev said, citing trust and communication challenges.

Instead, Datatruck is focusing on assistive AI rather than fully autonomous systems.

“Our goal is not to replace the role of dispatchers... our goal is to create such AI which will be assistive,” he said.

Roles evolve, not disappear

As AI takes on repetitive work, industry executives say the biggest change may not be job losses but role transformation.

“That role doesn’t disappear. It evolves,” Ergashev said. “That same person now manages 15 trucks... while before they were maintaining five.”

Matt Cartwright, CEO of Magnus Technologies, sees a similar shift driven by improved workflows rather than labor replacement.

Austin, Texas-based Magnus Technologies provides a transportation management platform designed to help carriers and brokers streamline operations, improve visibility and boost profitability.

“The first thing you have to do is define a problem... define a process that has inefficiency,” Cartwright told FreightWaves in an interview.

AI, he said, is most effective when applied to inconsistent or variable processes — not already optimized ones.

“I don’t need AI to improve a process that’s 100% efficient,” Cartwright said.

Instead, AI can identify and correct inefficiencies in real time.

“AI can detect patterns of variance... and work to self-heal,” he said.

Pricing and load matching seen as next frontier

While document processing and communication are early wins, more complex functions — particularly pricing and load selection — are emerging as the next major opportunity for AI.

“Pricing is one of the most interesting areas,” Cartwright said.

He noted that human decision-making often fails to account for network complexity, such as deadhead miles, freight density and route optimization.

“It’s really hard for the human brain to go, ‘How does this one load fit?’” he said.

AI systems, by contrast, can evaluate those variables in real time and optimize decisions across entire networks.

Industry shift toward AI-native systems

The rise of AI-driven execution is also exposing limitations in legacy transportation management systems, which were not designed for automation at scale.

Datatruck’s platform was built as an AI-native system, meaning its automation capabilities are embedded at the core rather than layered on top.

“We do have all the data today... and based on that, our AI can act properly,” Ergashev said.

Cartwright said legacy systems may struggle to adapt.

“A lot of legacy systems just weren’t built that way,” he said, noting that many were designed to solve isolated problems rather than enable full operational visibility and automation.

Rise of AI agents signals next phase

The broader industry is moving in the same direction.

Earlier this month, project44 unveiled a new class of AI agents designed to not only identify issues in freight operations but resolve them in real time — from procurement and scheduling to exception management.

The company said the goal is to collapse the traditional workflow of “truth, decision, action” into a single automated process, reducing tasks that once took days into seconds.

What’s next: trust and full automation

Despite rapid progress, both executives say widespread adoption of fully autonomous systems will depend on trust — particularly between brokers and carriers.

“I think the next is... when the broker side will start trusting AI,” Ergashev said.

He expects that milestone could come soon, unlocking new levels of automation in load matching and booking.

Cartwright echoed that AI alone is not the solution.

“AI by itself is not a solution... AI pointed at a problem where you’ve got a process… that’s where you unlock the value,” he said.