Advanced technologies and greater engineering and design complexity can also mean more potential failure points and costly vehicle recalls for the OEMs. Yoav Levy, CEO and Co-Founder, Upstream – a cybersecurity and data systems specialist – explains recall drivers and measures that companies can take to reduce recall exposure risk.
The auto industry has seen a number of notable recalls lately. Why do you think there seem to be more recalls these days?
Discover B2B Marketing That Performs
Combine business intelligence and editorial excellence to reach engaged professionals across 36 leading media platforms.
Recalls are rising because today’s vehicles are more complex, more connected, and developed on faster timelines. EVs and software-defined vehicles bring powerful new features, but they also introduce more failure points, while shared global platforms and supply chain issues can spread a defect across hundreds of thousands of vehicles at once.
We discuss the structural shifts driving this trend from Upstream’s recent report:
1. Innovation pressure: more complexity, less validation
Electric and software-defined vehicles rely on advanced battery systems, integrated electronics, and frequent over-the-air updates. These technologies improve performance and flexibility but also create new failure points. At the same time, automakers are under pressure to match the rapid pace of innovation set by new EV entrants, particularly from China. This has shortened development and validation cycles, so features reach customers faster but with compressed quality checks. Together, these forces increase the risk of incidents, warranty claims, and, in some cases, large-scale recalls.
US Tariffs are shifting - will you react or anticipate?
Don’t let policy changes catch you off guard. Stay proactive with real-time data and expert analysis.
By GlobalData2. Supply chain and platform interdependence
Vehicles today are built on shared global platforms with components sourced from a wide supplier base. A defect in a shared battery module, sensor, or electronic control unit can cascade across multiple models and brands, expanding the scale of a recall.
Underlying all of this, recalls are also more visible. Regulators have strengthened oversight, data from connected vehicles surfaces issues earlier, and consumer complaints spread quickly. Combined, these factors make recalls appear more frequent and larger in scope than in past decades.
How can automakers use AI tech to reduce the need for costly recalls that potentially damage brand reputations?
Automakers can use AI to lower the frequency and cost of quality issues by moving from a reactive, campaign-based approach to a proactive, data-driven one. Today’s connected vehicles continuously generate massive streams of operational data: diagnostic trouble codes (DTCs), telematics, ECU logs, sensor readings, battery performance data, and more. When combined with dealership and service data and analyzed by advanced AI models, this information can reveal early warning signs of defects long before they escalate into widespread failures.
Upstream’s report shows that nearly 70% of all recalls, and almost 90% of EV recalls, could have been detected earlier through connected vehicle data analysis. Signals such as abnormal charging patterns, thermal inconsistencies in batteries, or repeated fault codes often appear well before a defect becomes critical. AI systems trained to detect these anomalies can flag them in real time, enabling automakers to act early.
AI also helps to accelerate root-cause analysis. Traditional methods rely on warranty claims, dealer service records, or consumer complaints, which surface problems only after they affect many vehicles. AI can instead correlate data across thousands of vehicles instantly, narrowing down likely causes and helping automakers identify which populations are most at risk. This enables faster responses and more precise corrective actions once a recall is required, helping to contain cost and disruption.
The report also notes that as software-defined vehicles become more common, over-the-air (OTA) updates are becoming an important tool for fast countermeasures. OTA capabilities allow automakers to deliver software patches, feature enhancements, and even safety-critical fixes without requiring a dealership visit. This reduces both costs and customer inconvenience. In one recent incident, for example, an OEM relied on a software update to the battery management system, adjusting charging and discharging parameters to reduce cell stress and mitigate risk while defective components were scheduled for replacement.
Finally, AI creates a closed feedback loop. Data from the field can easily flow back into engineering and production, giving design teams concrete evidence of how systems behave in the real world. This improves product validation, reduces repeat defects, and strengthens overall quality processes.
In short, AI transforms vehicle after-sales by shifting focus to the pre-claim phase, where issues can be identified and contained before they spread widely. While recalls will remain a necessary safeguard whenever safety is at risk, proactive detection enables automakers to narrow scope, act faster, and minimize disruption. By addressing problems earlier, OEMs can reduce warranty costs, protect brand reputation, and maintain customer trust in an era where recalls are increasingly high-profile and expensive.
Upstream’s platform is already monitoring more than 40 million vehicles in real time. Its AI-powered Proactive Quality Detection (PQD) solution accelerates after-sales investigations by 30% and reduces costs by 5–10%, enabling automakers to improve efficiency, strengthen reliability, and protect brand reputation.
Are the OEMs following through and does it mean recalls will trend down over the next five years?
It’s important to note that recalls are issued whenever a vehicle problem poses a safety risk. Proactive detection can ensure vehicles already on the road are brought in earlier for fixes, and those still in production are repaired before release. But until it becomes standard practice for automakers to integrate data and AI for live vehicle monitoring (with feedback loops into manufacturing) large-scale recall events are likely to keep rising. Supplier-related defects remain especially hard to catch. The tools for AI-driven detection exist (like Upstream’s Proactive Quality Detection, which is already proven to improve root cause analysis investigation time by 30%) and adoption is underway, but the impact will depend on how quickly and consistently automakers embed these technologies into their after-sales quality strategies.
