70% of US vehicle recalls could have been detected earlier if it used connected vehicle data, a new report found.
Upstream, a provider of AI-powered Cloud-based data management platform, released a report, ‘Under Pressure: Why After-Sales Quality Strategies Must Evolve in the Age of Connected Vehicle Data and AI,’ in which it highlights that connected vehicle data is crucial to after-sales quality strategies.
These strategies allow OEMs to extract timely, actionable insights to prevent widespread quality issues. The report also advises car manufacturers to better leverage data-driven detection and analysis to reduce the risk of high-impact recalls, warranty costs, and customer dissatisfaction.
As software-defined vehicle (SDV) architectures are redefining the automotive industry, the pace and complexity of innovation requires a shift from traditional and reactive after-sales quality frameworks to proactive and AI-powered strategies. Global OEMs are racing to match the rapid development cycles of Chinese electric vehicle (EV) manufacturers, who release models up to 30% faster than global competitors. What began in the EV segment has accelerated software integration across all vehicle categories. These compressed timelines reduce validation windows, increasing the risk of unresolved software-related issues, including quality regressions, incompatibilities, and unintended cybersecurity vulnerabilities.
“The automotive industry is navigating a perfect storm of rising warranty costs, growing recall volumes, and increasing system complexity, particularly as EVs and SDVs become the new standard,” said Yoav Levy, CEO and Co-Founder of Upstream. “Traditional quality analytics, which rely on retrospective claims and service data, are no longer sufficient. After-sales quality teams need to leverage connected vehicle data and invest in the right AI tools to enable scalable, proactive quality monitoring and detection. The opportunity is clear: with the right machine learning foundation and modern data infrastructure, automakers can detect issues earlier, pinpoint root causes faster, and ultimately reduce recall scope while protecting budgets and customer satisfaction.”
Upstream’s report, which analyses more than 5,000 recall campaigns and over 30,000 consumer complaints from the US National Highway Traffic Safety Administration (NHTSA), demonstrates that the causes of 70% of all recalls since 2020 and almost 90% of those involving EVs could have been detected earlier using connected vehicle signals. The share of recalls with detectable early warning signals has steadily increased across vehicle types from 69% in 2020 to 75% so far in 2025. Yet many OEMs still struggle with fragmented telemetry, siloed systems, and reactive investigation processes, all of which hinder timely detection.
EVs are a clear example of both the growing quality risk and the opportunity for early detection. While they represent just 10% of new US vehicle registrations in 2024 (up from 1.6% in 2020), their recall share has risen much faster. EV platforms, which rely heavily on software-defined, data-driven systems, face more large-scale quality recall campaigns: high-impact recalls account for up to 18% of EV campaigns, while massive recalls now comprise 8%. At the same time, EVs generate a continuous stream of operational insights that can be used for quality assurance: nearly half (49%) of EV-related recalls could have been identified early through diagnostic trouble code (DTC) monitoring alone, compared to 37% for all recalls.
The report explores the broader landscape reshaping after-sales vehicle quality and offers clear insights on the need to modernise after-sales quality programmes by integrating AI, connected vehicle data, and cross-functional signal monitoring frameworks. The take-home message is that car manufacturers that act now, stand to reduce recall exposure, lower warranty liability, and strengthen customer trust in an increasingly digital, autonomous, and software-defined mobility landscape.
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