LAS VEGAS — At its annual Geotab Connect conference, the Canadian telematics giant unveiled a sweeping set of technology upgrades designed to harness artificial intelligence across every layer of its connected vehicle platform. With more than 4.5 million connected vehicles worldwide, Geotab is making a calculated bet that AI will fundamentally reshape how fleets operate, maintain their assets, and protect their drivers — and it intends to be at the center of that transformation.

CEO Neil Cawse took the stage at the Las Vegas event to lay out a vision in which machine learning models, generative AI tools, and predictive analytics become embedded in the daily workflows of fleet managers. The announcements, reported by Transport Topics, signal an acceleration of the company’s strategy to move beyond basic GPS tracking and data logging toward a platform that actively interprets, recommends, and even automates fleet decisions.

From Data Collection to Decision Intelligence: The Core of Geotab’s Strategy

For years, telematics companies have excelled at collecting vast quantities of data — vehicle location, engine diagnostics, driver behavior, fuel consumption, and more. The challenge, as Cawse and other industry leaders have acknowledged, is that most fleet operators are drowning in data but starving for actionable insight. Geotab’s latest product announcements are a direct response to that gap. The company is embedding AI capabilities into its MyGeotab platform to surface patterns, predict failures, and generate recommendations that previously required teams of analysts to produce.

Among the most notable upgrades is the integration of generative AI assistants that allow fleet managers to query their data using natural language. Rather than navigating complex dashboards or building custom reports, a dispatcher could simply ask, “Which vehicles are at highest risk of a breakdown this week?” and receive an immediate, data-driven answer. This approach mirrors trends across enterprise software, where companies like Salesforce, Microsoft, and ServiceNow have raced to embed large language model interfaces into their products.

Predictive Maintenance and the Economics of Uptime

One of the most commercially significant areas of Geotab’s AI push is predictive maintenance. For commercial fleets, unplanned downtime is extraordinarily expensive — industry estimates suggest that a single Class 8 truck sitting idle can cost an operator $1,000 or more per day in lost revenue, not including the cost of emergency repairs. Geotab’s platform now uses machine learning models trained on billions of data points from its global fleet to identify early warning signs of component failure, including battery degradation, engine faults, and transmission anomalies.

Cawse emphasized during his keynote that Geotab’s scale — processing data from millions of vehicles across diverse operating environments — gives it a structural advantage in training these models. The more vehicles that feed data into the system, the more accurate the predictions become. This network effect is a powerful competitive moat, and it helps explain why Geotab has invested heavily in expanding its OEM integration partnerships with automakers and truck manufacturers, as noted by Transport Topics.

Safety, Compliance, and the Regulatory Dimension

AI-driven safety features also figured prominently in the Geotab Connect announcements. The company is enhancing its driver behavior analytics to provide more nuanced risk scoring, moving beyond simple metrics like hard braking events to incorporate contextual factors such as road conditions, time of day, traffic density, and route complexity. The goal is to give safety managers a more accurate picture of which drivers need coaching and what specific behaviors pose the greatest risk.

This is particularly relevant as federal regulators continue to scrutinize commercial vehicle safety. The Federal Motor Carrier Safety Administration has been exploring updates to its Safety Measurement System, and fleets that can demonstrate robust, data-driven safety programs may find themselves better positioned in compliance reviews. Geotab’s upgraded risk-scoring tools are designed to help fleets not only reduce accidents but also build a defensible record of proactive safety management.

Electric Vehicle Fleet Management Gains New Urgency

The transition to electric vehicles is another area where Geotab is deploying AI to solve real operational headaches. Managing a mixed fleet of internal combustion and electric vehicles introduces complexity around charging schedules, range optimization, energy cost management, and infrastructure planning. Geotab’s platform now includes AI-powered tools that help fleet managers determine which routes are best suited for EVs, predict charging needs based on historical usage patterns, and optimize charging times to take advantage of lower electricity rates.

With major carriers and private fleets under pressure from both regulators and corporate sustainability mandates to electrify portions of their operations, the demand for intelligent EV fleet management tools is growing rapidly. Geotab’s early investments in EV data analytics — the company has been tracking electric vehicle performance data for several years — position it to capture a significant share of this emerging market segment.

The Competitive Arena Heats Up

Geotab’s aggressive AI push comes at a time of intense competition in the fleet telematics industry. Rivals including Samsara, Motive (formerly KeepTruckin), and Trimble’s transportation division are all investing heavily in AI and machine learning capabilities. Samsara, which went public in 2021, has been particularly vocal about its AI ambitions, marketing features like AI-powered dash cameras and real-time incident detection. Motive, meanwhile, has leaned into automation of compliance workflows and back-office operations.

What distinguishes Geotab, according to Cawse, is the sheer breadth and depth of its data assets. With vehicles connected across more than 160 countries and partnerships with dozens of OEMs, the company argues that its data foundation is unmatched. The quality and diversity of training data is a critical differentiator in AI, and Geotab is leaning into this advantage as it builds out its next-generation platform capabilities.

OEM Partnerships and the Embedded Telematics Trend

A key element of Geotab’s growth strategy involves deepening its relationships with vehicle manufacturers. As automakers build more connectivity into vehicles at the factory level, the traditional aftermarket telematics hardware model is evolving. Geotab has pursued an approach that combines its own hardware devices with software integrations that tap into OEM-embedded connectivity systems. This hybrid model allows the company to serve fleets operating a wide range of vehicle makes and model years — from older trucks that require aftermarket devices to newer vehicles with built-in telematics modules.

At Geotab Connect, the company announced expanded integrations with several major OEMs, though specific new partnerships were not all disclosed publicly. The direction is clear: Geotab wants to be the software intelligence layer that sits on top of whatever connectivity hardware a vehicle happens to have, making it agnostic to the underlying platform. This is a strategically sound approach in an industry where fleet operators typically run heterogeneous vehicle populations and cannot afford to be locked into a single manufacturer’s ecosystem.

What Fleet Operators Should Watch Next

For fleet executives and technology leaders, the Geotab Connect announcements underscore a broader truth about the telematics industry: the value proposition is shifting decisively from hardware and data collection to software, analytics, and AI-driven automation. The companies that win in this next phase will be those that can translate massive data sets into tangible operational improvements — fewer breakdowns, lower fuel and energy costs, safer drivers, and more efficient routing.

Geotab’s scale gives it a formidable starting position, but execution will be everything. Fleet managers will be watching closely to see whether the AI tools announced in Las Vegas deliver measurable ROI in real-world operations. As Cawse told attendees at the conference, according to Transport Topics, the age of AI in fleet management is not a future promise — it is arriving now, and the companies that adapt fastest will gain a durable competitive edge.

The stakes are high, and not just for Geotab. The entire commercial transportation sector is watching to see whether AI-powered telematics can deliver on its considerable promise, or whether it will join the long list of technologies that generated more hype than operational value. Based on the depth of the product announcements and the scale of the data behind them, Geotab appears to be making a serious and substantive play — one that the industry’s largest fleets will be evaluating in the months ahead.