(This article was originally published on Forbes.com)
Artificial intelligence (AI) in the automotive space is often associated with self-driving capabilities or digital assistants. But a quieter transformation is underway that’s reshaping a core part of vehicle ownership: service and maintenance.
Today’s vehicles generate gigabytes of data every hour, yet much of it remains untapped. By pairing real-time vehicle data with AI-driven insights, automakers and service providers can enhance diagnostic accuracy, streamline repair workflows and proactively prevent failures. In practice, this often leads to reduced lost usage, lower costs and more seamless ownership.
As the chief technology officer and co-founder of Sonatus, I’ve spent more than two decades developing advanced technologies across cloud, mobile and embedded systems, including AI-based tools that enhance commercial fleet safety. My experience leading high-performance teams across the data center and automotive domains has shown me how foundational data (and the ability to act on it in real time) can be. At Sonatus, we’ve worked with global OEMs to implement intelligent diagnostics at scale, and I’ve seen firsthand how AI-powered insights can elevate the entire vehicle ownership experience.
A Shift From Reactive To Predictive Service
Traditional service models are reactive: Something breaks, a warning light turns on and a technician begins troubleshooting. However, issues are not always easy to reproduce. This often results in repeated visits, delayed repairs and customer frustration.
AI introduces a smarter alternative: With continuous health monitoring and contextual data capture, vehicles can now provide timely, situation-specific insights. Instead of wading through general log files, service teams receive only the data tied to a particular issue—right when it happened. Consider logs from electronic control units, fault codes or even brief sensor feeds recorded in the moments leading up to an event.
This shift enables faster root-cause analysis, fewer repeat visits and more effective triage across service bays.
Getting The Right Data, Not Just More Of It
While connected vehicles are already data-rich, the challenge isn’t volume; it’s precision. By 2030, a single car could generate more than 25GB per driving hour, with advanced systems pushing raw data rates even higher.
But data is only useful when it’s targeted and relevant. Modern AI platforms can dynamically trigger data collection based on specific in-vehicle events, such as brake pressure anomalies, rather than passively logging entire drives. This can not only save bandwidth, but it can also accelerate time to insight.
The result is high-quality, low-latency information that fuels real-time analytics and reduces diagnostic guesswork.
Fleets And OEMs Benefit From Early Detection
Predictive maintenance is especially valuable for commercial fleets, where downtime translates directly into revenue loss. By analyzing patterns across vehicle populations, AI systems can flag outliers, identify emerging risks and even recommend fixes before a breakdown occurs. Notably, 57% of automotive professionals my company surveyed ranked vehicle data monetization, including diagnostics, as the highest-value opportunity for OEMs.
For OEMs, these insights can be incorporated into software update cycles, helping engineers resolve issues earlier in the vehicle’s lifecycle. According to McKinsey, automakers that embrace real-time feedback loops can reduce recall costs by up to 30%.
Beyond lowering service costs, this kind of intelligence can help preserve customer trust and brand loyalty long after the point of sale.
A Foundation For Lifecycle Innovation
As vehicles become increasingly software-defined, the utility of AI in the post-sale phase will continue to grow. Automakers are increasingly looking beyond the initial transaction to offer value across the ownership experience, whether it’s smoother diagnostics, faster service or fewer unplanned visits.
AI plays a key role in enabling these outcomes. By embedding analytics directly into the heart of vehicle systems, rather than relying solely on the cloud, manufacturers can deliver real-time decision making capabilities even in bandwidth-constrained environments. It also opens the door to more scalable, modular service architectures that support continuous improvement across fleets and regions.
Challenges To Keep In Mind
While the benefits of AI in vehicle diagnostics are clear, implementing these systems isn’t without obstacles. One of the biggest challenges is data collection at the right level of fidelity and relevance. Collecting too much irrelevant data can overwhelm storage and bandwidth limits; too little, and insights are lost. AI systems must be carefully tuned to dynamically capture data tied to specific vehicle events—something we’ve focused on heavily when deploying solutions for OEMs.
Privacy and consent are also critical considerations to keep top-of-mind. Collecting and transmitting vehicle data requires robust consent mechanisms and secure handling across both the edge and cloud. Regulations vary by region, so OEMs must design systems that can adapt to different legal and consumer expectations.
Finally, there’s the human factor. AI-enhanced diagnostics don’t replace technicians—they support them. Adoption hinges on building tools that provide clear, actionable insights without adding complexity. At Sonatus, we’ve found that empowering service teams with contextual, event-specific logs can reduce diagnostic effort and accelerate issue resolution.
The Road Ahead
AI may not yet be a commonly used term in the service bay, but its influence is growing. By turning vehicle data into actionable intelligence, it has the power to transform how problems are diagnosed, how repairs are made and how vehicles evolve over time.
In the years ahead, automakers that invest in this quiet revolution will be better positioned to deliver not just smarter cars, but also smarter ownership experiences.