Eight ways AI is changing the shop

Artificial Intelligence is no longer a concept or a technology for the future. It’s being used across the transportation industry right now, with plenty of solid maintenance use cases to boost shops’ efficiency quotient. From predictive maintenance and troubleshooting to budgeting and record-keeping, AI is rapidly improving how fast trucks and trailers are serviced and repairs are managed.

AI is a broad term, and many applications fall under the subset of machine learning. But even in these nascent cases, the data on AI’s transformative effect is compelling—while future iterations that could take over shop management are both exhilarating and terrifying.

Either way, the technology demands a closer look, so here are some of the primary areas where AI is making its mark.

1. Smarter scheduling

Today’s predictive maintenance tools, based on historical and real-time repair data, focus on providing more profitability and uptime, identifying which assets, systems, and components are likely to fail and when.

“Fleets and service shops using these tools already see time savings through automated uploads of service records that extract asset details, repair types, and costs from any format,” noted Brianna Perry, product marketing manager at Fleetio. “Beyond convenience, richer data becomes the foundation for smarter service practices.”

At the heart of these AI systems is the ability to merge telematics, service histories, and diagnostic and inspection results to deliver more complete repair recommendations before issues become expensive breakdowns. But they stop short of prescribing next steps, Perry said.

“The next generation of AI tools will go further by interpreting unstructured data like scanned invoices or technician notes to pinpoint why failures occur,” she added.

AI models can also analyze fault codes and identify issues before they cause downtime, leading to the opportunity to fix them before they happen, explained Paul Maida, sr. product manager at Geotab.

For example, Geotab’s predictive measurement tool Electrical System Rating (ESR) scores the health of a vehicle’s electrical system on a scale from 0 to 100 every day, and assigns a value of Good, Fair, Caution, or Poor to predict potential battery failures. This allows managers to proactively schedule maintenance or repair. Geotab said one fleet reported a 28% reduction in on-road battery replacements using ESR.

2. Data entry in a flash

In traditional workflows, much of the repair process is bogged down by the need to create, review, and authorize repair orders. AI is streamlining these tasks by digitizing and interpreting service documents automatically.

Trimble’s Brian Mulshine, senior director of product management, is spearheading two AI initiatives that illustrate this shift. The first, Advanced Road Call, is an AI chatbot that gathers basic information, codes the issue (using VMRS System codes such as a 017 for tires), and can integrate the information, including vehicle location, into a live agent’s workflow.

“Large carriers can have thousands of road call events monthly,” Mulshine said. “Our AI chatbots retrieve critical information and find the nearest approved vendor automatically, reducing administrative work and getting the driver back on the road faster.”

Trimble’s second project, an invoice-scanning AI solution, eliminates the manual entry of outside repair invoices into the TMT Fleet Maintenance and TMT Service Provider systems. This eliminates data entry errors and greatly enhances the accuracy of cost reporting and warranty tracking, he said.

“Fleets spend countless hours entering invoice data for outside repairs,” Mulshine said. “With AI, you can drag a multi-page invoice into the system, and it automatically captures totals, vendor names, VINs, unit numbers, parts, and labor. The system can even interpret handwritten invoices.”

The bigger the fleet, the larger the gains. Mulshine posited that a fleet with 3,000 tractors—each averaging four service events annually—and 12,000 trailers with two events per unit totals 36,000 total shop visits per year. Figuring that 30% are outsourced, this means a fleet processes 46 external vendor invoices a day.

“Capturing this data is time consuming or all data is not captured; each invoice takes about five to six minutes each,” said Mulshine, who added automating the process improves on that time by 80%.

3. Rapid-fire troubleshooting

Of all the AI use case low-hanging fruit, diagnostics is arguably dangling on the bottom branch. Traditional fault code analysis provides limited insight, requiring technicians to manually interpret issues in context. AI can now combine that data with historical repairs, vehicle usage, and other conditions to suggest root causes, as well as perform fault code analysis and use machine learning to identify risks and trends. AI-enabled diag tools can also forecast when components and systems will likely fail.

Geotab Ace, the company’s generative AI chatbot, takes it further by allowing users to query real-time fault code data in plain language. “We’re finding new ways to use AI to summarize and consolidate diagnostic data,” Maida said. “The key is to keep humans in the loop. The system can rank the top fault codes by severity, but a technician still reviews them before making the final call.”

Opus IVS is also pioneering diagnostic intelligence with its Blueprint Copilot, an AI-enhanced repair planning assistant built on the company’s ADAS MAP platform. “For the first time, shops can generate repair blueprints that account for complex vehicle systems,” said Frank Terlep, VP of ADAS Solutions at Opus IVS. “It ensures safe, profitable, and correct repairs.”

Brian Herron, CEO of Opus IVS, added, “Our vision is to simplify complex vehicle repair with AI that assists, not replaces, technicians.”

4. Anticipating inventory

AI’s role in parts inventory management is quickly becoming one of the most practical benefits for maintenance operations. The same insights used to manage repairs can anticipate parts needs.

“Over time, AI insights will feed inventory models that adjust stocking levels automatically,” Fleetio’s Perry said. “The shop still makes the call, but AI keeps the shelves and workflows one step ahead.”

As AI learns from past repairs, it can forecast demand for filters, sensors, or components associated with specific fault codes or maintenance intervals. By linking telematics data to parts usage, AI ensures that critical components are available before they’re needed, eliminating delays waiting for shipments and the cost of emergency sourcing.

For shops managing hundreds of part numbers and multiple vendors, AI-driven automated inventory forecasting is not just a convenience—it’s a strategic advantage that helps prevent downtime and keeps technicians productive.

5. Machine mentoring

As experienced technicians retire, the knowledge gap widens. AI offers a way to capture and replicate the best practices of veteran techs across entire organizations.

“The shortage of qualified technicians in shops has been a serious challenge for years,” Pitstop CEO Shiva Bhradwaj said. “AI can help address that core problem.”

Bhardwaj likens AI to a digital mentor that can “guide a technician step-by-step through diagnostics and repairs,” he said. “It offers entrenched knowledge, mimicking a human mentor but at a much faster pace. Each job becomes part of a continuous learning process. That’s how you build efficiency across every bay.”

The Pitstop fleet management platform deploys AI agents to put this in practice. Features include:

• Mechanic Assistant to leverage diagnostics and sensor data to pinpoint root causes, guide repair decisions, and predict failures.
• Data Entry Assistant uploads documents in minutes.
• Maintenance Optimizer tracks PM effectiveness by analyzing service visits and enables more effective maintenance strategies.
• Analytics AI delivers actionable reports on costs, parts, and vehicle availability.

Bhardwaj said Pitstop’s AI has a 95%+ accuracy rate. The technology also reduces complex vocational fleets’ downtime (measured as unplanned repeat service visits) by 74% on average. The platform also improves time to repair by 30% because of vehicle repair insights gleaned prior to intake, allowing for higher throughput.

6. Better budgeting

Maintenance budgeting has traditionally relied on spreadsheets and historical averages. AI is turning that process into a dynamic, data-driven exercise.

Motive’s AI Answers platform shows how large language models can revolutionize fleet analytics. The tool allows managers to ask plain-language questions such as, “What was our average vehicle downtime last month?” and receive instant visualizations drawn from maintenance, safety, and fuel datasets.

“Something that used to take months to put together is now something we can do instantly,” said Michael O’Neil, project manager at Reliable Carriers, an auto hauler using the solution. “There’s no longer that pain point of having to dig through spreadsheets. Anybody can access and understand the data.”

Hemant Banavar, chief product officer at Motive, added, “Truck service operations need fast, actionable insights but static reports and hard-to-reach data across disconnected tools make that nearly impossible. AI Answers delivers those insights instantly, so data isn’t a bottleneck.”

By eliminating manual data searches, Motive said AI Answers allows fleets to act faster on key insights, improving productivity and operational efficiency. For shops managing multiple systems and datasets, it offers a simpler, unified way to keep both performance metrics and maintenance decisions aligned.

7. Bridging communication gaps

While AI adoption is growing, in a multi-national and multi-cultural industry like trucking, that often comes with the need to translate documents, notes, video and audio files, and other information. Smartcat, a provider of AI enterprise translation software, is already seeing results in the automotive market, where it is providing AI translation, AI voice-over, AI dubbing, instantaneous subtitle translation, and more, in any language.

“The impact of AI translation on speed is substantial,” said Alex Conza, senior strategic marketing manager at Smartcat. “A well-trained workforce is essential for efficiency and product understanding. Localizing technical documents, user manuals, and industry-specific eLearning is critical. AI translation and localization platforms make this process faster and more cost-effective.”

8. Potential for the future

Each of these AI innovations points toward a more connected and autonomous future for fleet maintenance operations.

Current and developing technologies connect systems, automate workflows, and ensure information flows where it’s needed most.

At its core, AI is about enabling people in truck and trailer service operations to do what they do best—solving problems, making informed decisions, and keeping trucks moving.

The coming  years will define how deeply these systems reshape shop operations. But one thing is clear: the era of the AI-powered shop is here.