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How TMS Vendors Are Bringing AI to Trucking’s Back Office
AI Enabling Faster, Better Data Capture and Strategic Decision-Making
Key Takeaways:
- TMS providers are embedding AI to automate data capture and execution tasks, shifting platforms from passive systems of record into systems that actively support decisions and workflows.
- AI improves speed and profitability by enabling real-time data extraction, predictive pricing and automated dispatching, with companies reporting higher win rates, fewer errors and better margins.
- Execution-focused AI agents are expected to expand further, automating routine tasks and reshaping roles toward decision-making and relationships while enabling fleets to scale without adding back-office staff.
Back-office functions in trucking and freight brokerage have historically involved numerous manual processes, fragmented data and time-intensive workflows, but that is changing as artificial intelligence moves deeper into transportation management software.
TMS providers are embedding AI capabilities directly into their platforms to create systems that not only capture data but also act on it.
“Classic TMSs are systems of record. They’re not systems of action,” said Hans Galland, CEO of BeyondTrucks.
That distinction is narrowing as AI enables faster, more complete data capture and earlier strategic decision-making that improve profitability, such as what freight to accept at what margin.
“The back office has been running on gut instinct and spreadsheets for too long,” said Mark Hill, CEO of PCS Software. “Most fleets aren’t suffering from a lack of data. They’re suffering from data that shows up too late or in the wrong place.”
Across the industry, several core AI use cases are emerging within TMS platforms, starting with data extraction and extending into execution.
On the extraction side, AI-powered document processing tools can read bills of lading, invoices and proof-of-delivery documents, pull the information into the TMS and automatically populate data fields, reducing manual entry.
McLeod Software has developed tools to ingest and automatically process emails and other written text that sit outside the TMS while keeping humans in the loop to validate the information, said Doug Schrier, McLeod’s vice president of growth and special projects.
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Carrier Logistics uses AI to extract data from BOLs to automatically create shipments. That speed has downstream benefits, especially in less-than-truckload operations that have multiple pickups and deliveries, said Ben Wiesen, the company’s president.
“Without AI’s processing speed, sometimes the data was arriving too late to allow the optimization to run,” Wiesen said. “Now the data is captured much sooner, allowing operational workflows to start sooner.”
Bill Cain, director of product management for TMW.Suite at Trimble, said AI can also ingest telematics data, hours-of-service information, traffic conditions and historical performance to recommend or automate dispatch decisions.
“Dispatchers and planners are making hundreds of micro-decisions every day — which driver, which load, what route and how to handle exceptions when something changes,” Cain said.
PCS Software’s Cortex AI engine embeds intelligence directly into dispatch and planning workflows.
“When a dispatcher is assigning a load, the recommendation is right there: profitability score, driver match, backhaul potential,” Hill said. “That’s the difference between AI that’s theoretical and AI that actually changes behavior at 5 a.m. on a Monday.”
Before a dispatcher ever touches a load, Cortex has already scored it by margin potential, backhaul fit and lane history.
With the added insight, margins go up because fleets stop accepting bad freight, dispatcher productivity improves because decisions are faster and backed by data, and operational costs drop because automation handles the repetitive work, including data entry and status updates, Hill said.
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Galland
BeyondTrucks embedded coding agents into its software so users can customize the system to their needs. In the route optimization engine, for example, dispatchers can enter natural-language exceptions, such as avoiding certain metropolitan areas during a snowstorm. The system translates that into updated optimization constraints.
“You don’t manage by plan. You manage by exception,” Galland said, adding that capturing those exceptions also builds a dataset for predictive modeling.
Jeff Silver, founder of Mastery Logistics Systems, said execution agents represent a newer category of AI innovation and are pushing automation further by completing tasks rather than assisting with them.
“It allows you to automate basically every single mundane task that you never wanted to be doing,” he said.
Mastery’s voice-enabled AI agent, called Leo, is integrated directly into the company’s MasterMind TMS and can handle carrier management, driver management, booking and tendering, asset management, tracking, rating and quoting, and lane and routing guides. The system is now in beta with customers.
Dave Romanchuk, vice president of product development for Revenova, said execution-focused AI is reshaping freight workflows, such as load building, quoting and carrier matching within the TMS.
Revenova’s Artimus is an AI-powered freight execution agent that acts on inbound requests, automatically turning unstructured data, such as emails, into structured loads and quotes.
“The primary benefit is speed as a competitive advantage,” Romanchuk said. “Tasks that used to take minutes, like building a load or generating a quote, now happen in seconds, enabling brokers to respond faster and win more freight.”
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Ebrahim
Ahmed Ebrahim, senior vice president of integrations and partnerships at McLeod, said AI is now automating much of the load-booking process for brokers by qualifying carriers, generating predictive pricing and automatically booking capacity. Carrier qualification is particularly important given the prevalence of cargo theft. On the pricing side, brokers can only accept capacity when they can make a healthy margin, making accurate predictive pricing essential.
Because platforms can pull in disparate information, such as historical data and real-time market signals, they can generate rates more quickly and accurately.
“When spot quoting rates, there wasn’t enough time to look at a request for a load, go look up what the rates should be for that particular lane on that particular day and take the time to compose an email response,” said Tom McLeod, McLeod Software’s founder and CEO. “Now they can respond to a much larger percentage. They have an opportunity to grow their business.”
Walter Mitchell, CEO of Tai Software, said customers using the company’s AI tools are seeing measurable results. Customers using the company’s email processing tool have seen load win rates increase by more than 30%, which Mitchell attributes to the time savings.
“Speed is a direct advantage,” he said. “Brokers who quote faster increase their chance to win loads and build better shipper relationships.”
AI Use Cases Expand
TMS vendors are introducing an ever-growing array of AI features and capabilities to help solve industry inefficiencies.
PCS developed its Backhaul Booster product after hearing from customers that dispatchers often lacked the time to properly evaluate return-load opportunities and instead settled for less profitable freight or deadhead miles. The system combines real-time truck location data, routing information, driver preferences and historical business relationships to identify potential freight opportunities before a truck becomes available. AI agents can search load boards and reach out to brokers or shippers automatically based on specific operational and financial criteria, Hill explained.
Cora, Cortex’s AI voice agent, eliminates outbound backhaul outreach, which Hill said is one of the most labor-intensive workflows in trucking.
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“Cora makes the calls, captures the conversation, creates the opportunity in PCS TMS and hands it to a dispatcher ready for one-click conversion to a load,” he said.
Hill added that agents will become more powerful as they develop collective memory and reasoning capabilities, allowing them to share operational knowledge in much the same way human teams do.
Other vendors also are offering AI agents to streamline routine interactions.
Tai Software’s track-and-trace voice agent contacts drivers, logs updates and adjusts shipment status without human intervention. The virtual agent operates around the clock, eliminating the need for manual check calls entirely. Brokers set frequency rules and receive full call summaries directly in the TMS.
AI also can help resolve data quality problems in the back office. Many fleets lack structured, accessible data because legacy systems were not designed to capture it systematically.
“Billing errors, accessorial disputes, late invoicing … those originate at the point of load creation and quoting, when unstructured data gets manually re-keyed and errors enter the system,” said Danielle Chaffin, senior manager of industry relations for Revenova.
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Chaffin
Revenova’s Artimus AI agent parses unstructured input and applies TMS logic, ensuring the data entering back-office workflows is clean from the start.
“The result is a back office that spends far less time correcting errors that shouldn’t have existed,” Chaffin said.
A BeyondTrucks survey found that 52% of fleet respondents said they lacked the data needed to make better decisions, even when the information existed.
“When we go to a fleet, they have a TMS, but they also have 100 spreadsheets, 20 paper forms and the dispatchers who’ve been there for 40 years,” Galland said. “The tribal knowledge walks out the door when the people walk out.”
BeyondTrucks addresses the problem by using coding agents that enable dispatchers to easily enter information via natural language and by making the system configurable so that fleets can design exactly what they need. When dispatchers see the platform as something that makes their jobs easier rather than more complicated, Galland said they are more likely to encode their operational knowledge into it.
Jimmy Coleman, operations manager at motor carrier D.G. Coleman, uses AI coding agents within BeyondTrucks’ system to manage fuel surcharge calculations and update fuel surcharge rates on the first of each month.
Previously, fuel surcharge changes weren’t always posted at the right time, forcing the accounting staff to manually adjust invoices at the Commerce City, Colo.-based fleet, which specializes in bulk and aggregate hauling.
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D.G. Coleman uses AI agents within BeyondTrucks’ software to better manage fuel surcharges. (D.G. Coleman)
“It is really minimizing the clicks and minimizing the mistakes,” Coleman said. “If you’re billing hundreds of invoices at the wrong surcharge, the customer is calling and saying it isn’t right, and it can build up to big issues.”
Workforce Implications
Overall, industry leaders say the long-term impact of AI on the workforce remains uncertain.
“There is a huge social experiment unfolding before our eyes,” said Wiesen of Carrier Logistics. “The trend line show human intelligence is still important, but the routine and low-value-add work is going to the machines.”
However, AI does have its limits.
“AI isn’t human, it’s a machine, so if something truly requires a human touch, AI probably isn’t the right solution,” Wiesen said. “It’s not here to replace social interaction. We’re not going to build relationships with AI, marry it or invite it over for dinner, but a lot of supply chain, and trucking in particular, isn’t emotional — it’s functional.”
By increasing efficiency and reducing manual work, companies can grow without expanding back-office head count at the same rate.
“If you can increase size without growing back office, it unlocks economies of scale,” said Galland of BeyondTrucks, adding that AI improves decision-making and utilization so that carriers can haul more loads with existing assets.
Tasks that used to take minutes...now happen in seconds, enabling brokers to respond faster and win more freight.
Dave Romanchuk, Revenova
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Revenova’s Romanchuk said roles are evolving rather than disappearing.
“As execution becomes automated, roles are shifting from task management to decision-making and relationship-building,” he said.
Deen Albert, director of operations for refrigerated carrier Grand Island Express, said automation can free employees to focus on higher-value activities.
“Removing the things that can be done with AI gives them more time to spend on the driver,” he said. “If I can save my folks 30% of their day just by taking out the busywork, then I won on the people side.”
The Nebraska-based carrier is using AI to automate order entry and uses an ETA agent that automatically contacts drivers when a load is running behind so that fleet managers can focus instead on decisions.
Fleets historically scaled by adding people, but by improving efficiency with AI, they can now handle more freight without increasing head count, said Hill of PCS.
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He expects AI to significantly reshape dispatchers’ roles, especially as midmarket fleets gain access to capabilities that were previously only available to large enterprise carriers. “I think over the course of 2026 and early 2027, the job of the dispatcher will change pretty dramatically,” Hill said. “They’ll just have more information at their fingertips.”
While larger fleets have led AI adoption due to their resources and scale, embedding AI within the TMS is making it more accessible.
Trimble’s Cain said small and midsize fleets often see a return on investment first in use cases that reduce manual touches and speed up execution, such as automated order entry and workflow automation.
“Adoption accelerates when AI is packaged into everyday workflows with measurable outcomes — time saved, fewer errors, improved utilization — rather than positioned as an analytics add-on,” Cain said.
Carrier Logistics’ Wiesen said fleets of all sizes can benefit from AI adoption.
“There really is no such thing as ‘I’m too small to need better technology,’” he said. “A mile saved per truck is a mile saved per truck, whether there are five trucks or 5,000.”
