How AI Is Changing Automotive Quality Control

Quality failures are expensive. In 2025, U.S. vehicle manufacturers paid out $20.81 billion in warranty claims, and according to the National Highway Traffic Safety Administration, more than 29 million vehicles were recalled. Every one of these numbers came from a defective part that was overlooked or wasn’t inspected properly—whether that is a mistake made during the design and/or inspection process or something that happened to the vehicle once it was shipped to a customer.

There are many increasingly complex variables that come into play when trying to find defects on your production line. With software-driven functions that used to be mechanical, and tighter tolerances on mechanical components and EV batteries, there is little room for error. What’s more, global supply chains add variability that a single plant cannot fully control. All of which are more reasons than ever to ensure you have the right process in place to find defects on your line. 

Artificial intelligence is giving quality teams a different set of tools. Rather than checking a sample of parts after production and hoping problems don’t slip through, manufacturers can now monitor every part, in real time, and catch trends or deviations before they become defects. Having spent years working with automotive, tire, and battery manufacturers on these problems, I want to walk through what is actually changing and what it takes to make these programs work.

Catching Problems Before They Compound

The biggest shift AI brings to quality is scale. By looking at all products on your line, you can detect anomalies before they become defects. To err is human, so if you are only looking at a sample of your line and relying on humans to visually inspect your product, anomalies can be missed. Whether that be human error from not being able to visually identify a defect or fatigue setting in, causing employees to not be as detail-oriented as they were at the start of their shift.

The advancement and confluence in cameras and AI can be very advantageous to the automotive sector. Not only can you begin to detect more defects on your production line, but crucially, detect issues before they happen. By using AI to detect defects in virtual testing, manufacturers have begun using this to their advantage to not only predict possible failures but also create new tests to run on their product. What’s more, using AI on your production line can allow you to detect more defects than you ever could before. Research from McKinsey showed that manufacturers were able to detect 90% more defects by using AI on their production line.

Inspection That Improves Itself

Catching more defects is only part of the story. The second shift is that AI-driven inspection gets better over time instead of staying static. Not only are more defects being found, but an AI-powered system analyzes all of the ingested data and learns. Using AI to predict machine outputs will allow you to detect when your machines are not performing up to spec. If your machines begin to fall out of tolerance, you can alert the system and have it adjust machine values to bring output back within tolerance and products within spec.

An example of this in the tire industry would be when splicing. By using AI and vision on your splice process, you can reduce out-of-tolerance splices while improving performance. Another example in automotive would be how we are improving gap and flush within the body or weld shop. Because you have allowed the system to learn from your current and historical data, the system knows what a good splice looks like and what good geometric tolerances look like and will begin to adjust machine process variables and robotics in real-time to stay in tolerance. That learning loop is what separates modern AI inspection from earlier automation. Standards stay consistent across shifts, lines, and even plants in different regions, which matters for tire manufacturers and automakers running the same systems and models across multiple facilities worldwide.

From Safeguard to Strategic Advantage

For a long time, quality control has been treated as a safeguard, a final check meant to keep bad parts from reaching customers. AI is turning it into something closer to a strategic lever. 

Not only can you detect more defects, but you can begin to understand where they are happening. Having a better understanding of your process will allow you to detect trends and fix those issues before they become a problem. Once you can begin to understand where these defects are happening, you can increase your first pass yield and lower your scrap/rework rate, and by lowering scrap and rework rates you can begin to increase throughput.

The metrics that matter here are not exotic. First pass yield, scrap and rework rate, customer complaints, and inspection accuracy all existed before AI. What changes is how quickly and consistently manufacturers can measure and act on them. A defect caught on the line costs far less than one caught after assembly, and one caught after assembly costs far less than one that reaches a customer and turns into a warranty claim or a recall.

Why So Many AI Programs Stall

Of course, none of this happens automatically. Just because you buy cameras and AI software doesn’t mean that you are going to detect more defects. There are a couple of key factors that manufacturers who have successfully implemented these processes have done differently. They identified a pain point on their line and tried to solve it with AI, instead of jumping into AI and trying to find what they can use it for. They also made sure that they had measurable goals that they could compare their results to. Once you know that your pilot is successful, you can then look at scaling the process to other areas on your line.

Another thing that you want to make sure you have is skills on your team. Perhaps your quality team knows everything about the process, but they have never used machine learning. Or maybe your data science team knows all about machine learning but doesn’t know what goes on your line. Making sure that you have these cross-functional skills, along with experience in how to scale, will allow you to implement these processes on your line and across your operations.

Where This Is Headed

Quality has always defined reputation in automotive. A single anomaly that reaches the market can trigger recalls costing hundreds of millions of dollars and erode years of customer trust. What AI offers is a way to catch that anomaly earlier, understand why it happened, and prevent it from recurring, across every line and every plant rather than one at a time.

The manufacturers getting real value from AI today aren’t necessarily the ones with the most advanced technology. They’re the ones treating quality as a system to be understood and improved continuously, with AI as one part of that system rather than the whole of it. As vehicle architectures keep evolving and production volumes keep climbing, that discipline will matter more, not less.