• Written by Muhammed Abdulla NC, CEO

Can we really say that AI has transformed the automotive industry, or is it just a lot of hype? The truth is, AI has genuinely changed day-to-day work in some areas, but not yet in others. To understand where AI has actually landed, let's look at the numbers: according to the US Census Bureau's Business Trends and Outlook Survey, 12.4% of US manufacturers, the Census sector that includes vehicle and parts makers, reported using AI in the previous two weeks.

Where AI has actually landed in the automotive industry so far

AI has largely landed in the functions whose output is text, not the ones that handle physical work. This means that office functions like sales and marketing, where 14.3% of US businesses used AI in the previous six months, have seen more change than the production floor. In the automotive industry, this translates to AI being used in areas like procurement, supply chain management, and quality control, but not yet in areas like manufacturing or assembly.

This is because AI is well-suited to tasks that involve analyzing and generating text, like contracts, reports, and emails. However, when it comes to physical work, like welding or painting, AI is not yet able to replace human workers. This is why we see AI being used in the office, but not yet on the production floor.

Supplier escalations

One area where AI has made a real difference is in supplier escalations. When a problem arises with a supplier, the people dealing with it used to have to wait for a report to be generated before they could take action. Now, with AI-powered tools, they can ask the data in MES, PLM, supplier portals, and the warranty system a direct question and get an answer in real-time.

This allows them to respond quickly to supplier issues, which can help to prevent delays and reduce costs. For example, a purchasing manager can ask the system "what is the status of the shipment from supplier X?" and get an answer immediately, rather than having to wait for a report to be generated.

Engineering change propagation

Another area where AI has made a real difference is in engineering change propagation. When an engineering change is made, it can have a ripple effect throughout the production process, and currently, the decisions that need to be made about how to implement that change often wait on a person stitching together two systems.

With AI-powered tools, the people responsible for implementing the change can ask the system a direct question, like "what components will be affected by this change?" and get an answer in real-time. This allows them to make decisions quickly and accurately, which can help to reduce delays and costs.

Warranty root cause

Warranty root cause analysis is another area where AI has the potential to make a real difference, but it is harder than it looks. To be effective, AI needs to be able to analyze large amounts of data from multiple sources, including the warranty system, the production system, and the supplier portals.

However, this requires a high degree of data integration and standardization, which can be difficult to achieve. Additionally, the data needs to be of high quality and consistent, which can be a challenge in the automotive industry where data is often fragmented and inconsistent. For more information on how to implement AI in warranty root cause analysis, contact us.

Why most adopters have not changed anything underneath

According to the US Census Bureau's Business Trends and Outlook Survey, 64.3% of US businesses that have adopted AI have made no change of any kind to adopt it, and only 6.8% have changed how they collect or manage data. This means that most businesses are simply layering AI on top of their existing systems and processes, rather than using it as an opportunity to transform their underlying operations.

This is a missed opportunity, as AI has the potential to drive real change and improvement in the automotive industry. However, to achieve this, businesses need to be willing to make changes to their underlying systems and processes, rather than just layering AI on top. For more information on how to get the most out of AI, read our blog.

What a sensible first quarter looks like for a the automotive industry business

A sensible first quarter for an automotive industry business looking to adopt AI might involve focusing on one specific question, like "what is the status of our inventory levels?" and using AI to answer that question. This would involve identifying one system, like the inventory management system, and one team, like the procurement team, to work with.

This approach allows the business to test the waters, so to speak, and see how AI can be used to drive real change and improvement. It also allows them to build a business case for further investment in AI, and to develop the skills and expertise they need to get the most out of the technology. For more information on how to get started with AI, visit our production page.

What this does not do, or when it is the wrong choice

AI is not a silver bullet, and it is not the right choice for every business or every problem. In some cases, the underlying systems and processes may not be suitable for AI, or the data may not be of high enough quality. In other cases, the business may not have the skills or expertise they need to get the most out of the technology.

In these cases, it may be better to focus on other areas, like process improvement or staff training, rather than investing in AI. Additionally, AI is not a replacement for human judgment and decision-making, and it should be used to augment and support these processes, rather than replace them. For more information on how to determine whether AI is right for your business, read our analysis of the US Census Bureau's Business Trends and Outlook Survey.