The artificial intelligence boom is poised to expand into 2025, bringing with it exciting new solutions for fleet operations. Improved large language models will be able to tackle significantly more complicated tasks, boosting safety and efficiency.
Jeremy Wolf
Artificial intelligence—especially machine learning—has already transformed the transportation industry in a short few years. As carriers look ahead to 2025, they can expect the information revolution to continue.
The nature of AI’s usefulness for transportation, however, will remain the same: quicker, simpler data and insights.
“The biggest benefit of AI—applied to fuel purchasing, route optimization, equipment utilization, and safety—is the immediacy of information,” Adam Kahn, chief business development officer at Netradyne, told FleetOwner. “Can you get more data-driven decisions around what’s happening with your operation?”
AI tools today already help carriers process and utilize vast amounts of information. For 2025, AI’s capabilities and influence will likely grow even further.
AI in transportation today
What is AI, machine learning?
The term artificial intelligence is vague. Any machine that uses perception, learning, or decision-making can be called AI. This means nearly every computer built in the last several decades contains programs that fit some loose definition of AI.
A more precise term is machine learning, a form of AI where computers learn from existing data to make inferences without explicit instructions. Autonomous vehicles, large language models, and predictive analytics are all examples of machine learning applications.
Machine learning solutions make up a vast majority of the AI market, causing many people to use the terms interchangeably.
The greatest uses of AI in transportation today are for data processing and analysis. Carriers can leverage machine learning to automatically draw inferences from existing data—or to quickly digitize existing paperwork.
“AI is the aggregation of a lot of data and creating useful workstreams around that data,” Kahn explained. “If you can take a lot of information and apply some reasoning against it, then there should be growing opportunities around where to buy fuel or what routes to assign to a vehicle.”
When technology leaders discuss the potential of AI applications for carriers, there are six areas that stand out: data analysis, fuel efficiency, route optimization, asset management, safety, and autonomous driving.
The number of industry solutions with the word “AI” attached to them are too many to count. Here are just a few examples of recent AI solutions for each area:
- Data analysis: Geotab Ace, launched in February, is a large language model AI chat platform that helps customers retrieve and process their telematics data. Users can ask conversational data questions to draw quick conclusions from their operational data. Magnus Technologies in March started an AI-powered truckload planning service that pulls from various information sources to build a five-day market forecast.
- Fuel efficiency: Isaac Instruments announced a Fuel Equivalent service in October that uses AI to process customers’ operational data. The company suggested its AI tool could draw more useful fuel efficiency comparisons across trucks’ varying operations.
- Route optimization: QCD developed a single platform for its drivers’ needs, including AI-powered route engineering.
- Asset management: AssetWorks and Pitstop started testing AI-powered predictive maintenance in 2023. Fleet maintenance software platform Uptake uses AI to turn TMS data into predictive maintenance suggestions.
Netradyne
- Safety: Samsara launched AI-powered drowsiness detection for its inward cameras in October.
- Autonomous driving: Autonomous trucking is already transforming the industry and is poised to revolutionize transportation. The technology uses sophisticated image detection and automated decision-making to haul goods—often with greater fuel efficiency and other benefits.
How AI will shape trucking in 2025
Artificial intelligence will continue its boom next year. The chip manufacturing market has a bright future, and large language models show a steady rate of improvement. What’s more, AI might unlock new solutions for fleets in 2025.
Improving chip market, AI capability
The AI hype cycle
Only a few years ago, people commonly discounted AI applications as “not real intelligence” once the applications became popular. This changed, however, after extremely charismatic generative AI tools entered the scene. Image generation with DALL-E and text generation with ChatGPT skyrocketed the popularity of AI in 2022.
Now, AI is a fashionable marketing buzzword sometimes used to mislead and deceive customers. Some firms predict AI is at the peak of its hype cycle and about to burst. Others predict the valuation of AI will continue to grow at a breakneck pace.
Valuations of chip manufacturing exploded with the AI boom, skyrocketing the worth of companies like Nvidia and AMD. The semiconductor market is soaring and projected to continue growing with demand for AI.
A booming semiconductor market suggests greater access to—and demand for—AI tools over time. However, some businesses predict a global chip shortage could bottleneck access to AI solutions.
Large language models are still improving at an exponential rate. They are continually able to process more complex tasks as their context windows grow.
AI tools also face a slimmer chance of government regulation next year. An incoming Trump administration means AI regulation is less likely than under a Democratic administration, Kahn suggested.
“Restrictions around regulating AI will maybe open up to allow for more independent commerce to figure out what it really means—versus having the government tell you what it’s not going to be,” Kahn told FleetOwner.
New AI applications for fleets next year
AI technology will likely continue to improve in 2025, and AI solutions will become more widely available. But what improvements are important for fleet operations?
Paul Pallath, VP of applied AI for Searce, pointed to three areas where AI could change transportation in 2025—AI agents, multimodal data management, and video generation.
AI agents
Large language models ((LLMs) are one of the most potent AI developments in recent years. LLM developers are looking to make the technology even more impactful.
Agentic AI, sometimes called robotic process automation, may be the next major development for large language models. This technology places a single AI chatbot in front of the user for commands—but behind the chatbot lies an intricate stack of multiple AI tools.
Users can ask the agent to perform a data-centric task in a similar conversational way that they would ask an employee. Layers of LLMs could perform, review, and automate the processes required for the overall task. Getting an invoice number or reconciling multiple invoices could be done much more quickly—and simply—with the help of agentic AI.
AI giants like Google with Gemini 2 and Nvidia with Blueprints are already developing agentic AI to solve more complicated tasks.
“The premise of Gemini 2 is that it enables agentic AI at scale that allows us to now have autonomous bots … that manage multiple different intelligent bots that are task-specific large language models,” Pallath said.
Multimodal data management
One application already growing in popularity is using AI to consolidate disparate forms of data. Paper forms and video feeds contain helpful data—but joining them in a single spreadsheet for a fleet’s TMS can involve a lot of manual labor. AI-powered image and language processing could automate the process.
“LLMs are multimodal, which means that it can connect with data, which is textual information, audio information, and video information, and process it in one go,” Pallath said. “If I’m asking for something, and if this information is in three disparate sources, LLMs now have the capability to summarize all of these, take the entire context, and give me a response.”
AI is already turning video feeds into automated truck logging and driver distraction detection.
“There are pockets where AI is thriving. Video-based telematics is moving very aggressively in that space,” Pallath said. “There has been a monumental shift in the results of that, where you see increased accident reduction and increased driving risk reduction.”
In addition to videos, AI tools could also consolidate paperwork such as bills of lading or audio recordings of customer transactions into more accessible formats. Paired with AI agents, using and accessing several types of information could become much easier.
Video generation
Pallath also suggested that AI video generation could become much more useful in 2025.
Similar to large language models, AI video generators have entered an arms race. Companies like Google and OpenAI continue to launch competing AI video generators that appear more and more convincing.
Paired with technical documentation, Pallath said AI video generation could soon create training videos for maintenance operations.
ID 324969265 © Doberman84 | Dreamstime.com
AI image generation fails to make accurate depictions of sophisticated technology, like engines. Depicted here is what AI thinks an engine bay looks like.
AI-generated video content is still far from accurate, especially when it comes to technical subjects. Even AI-generated images fail to depict advanced machines accurately, but Pallath believes the technology will continue to improve quickly.
“It is weak, but the rapid pace at which it is becoming better is absolute,” Pallath said. “I’m sure that in the first half of the year, this technology will be robust enough for us to leverage towards the second half of the year.”
Ultimately, however, Pallath stressed that companies should keep ethics top-of-mind when they consider implementing AI.
“When we think about creating these AI interventions across businesses, thinking through responsible and ethical use of AI—and what should I as a company do or not do with AI—is going to be paramount,” Pallath said.
