“Corporate owned shops are most likely to adopt AI tools and systems first, meaning that a large number of auto service and repair workers will be affected soon.”
Artificially intelligent government surveillance tools are now enabling the use of high-tech inspection methods in the automotive service and repair industry.
Recent breakthroughs in artificial intelligence (AI), mostly generative AI products such as ChatGPT, which had 100 million monthly active users last month, prompted concerns about robots taking jobs. While the scale of robotic intrusion caused by AI in the automotive industry remains unknown, service and repair shops should consider the impact it may have on their business.
The increasing use of AI and other data analytics for finding violations has made it necessary for many companies to adopt similar technology themselves. A recent study by the McKinsey Global Institute estimates that 29.5 percent of all current worker hours will be automated by the year 2030.
AI-powered tools are resolving age-old problems in government permitting, giving jurisdictions the firepower to do a better job at processing applications, improving response times and encouraging shops to complete regulatory requirements online.
The use of data analytics has become increasingly sophisticated. Agencies like the Securities and Exchange Commission (SEC) have led the charge, applying risk-based data tools to scrutinize financial reports and stock trades. The use of Large Language Models (LLMs) has only increased the ability for regulators to recognize, generate, and sift through sprawling data sets of text to identify misconduct.
AI is starting to be used by many governing agencies to issue a Notice to Comply (NTC) or Notice of Violation (NOV), creating an enormous list of regulatory documents for a shop owner to provide when faced with a slip up.
Stanford researcher Denisov-Blanch said that owners have given his research team access to their internal code and, for the last two years, he and his team have been running an algorithm against individual employees’ code. He said that this automated code review shows that nearly 10 percent of employees at the companies analyzed do essentially nothing and are handsomely compensated for it.
Denisov-Blanch and his colleagues published a paper outlining an “algorithmic model” that essentially measures worker productivity. The paper claims that their assessment model “can estimate time with a high degree of accuracy,” essentially suggesting that it can judge worker performance as well as a human can, but much more efficiently.
The Stanford data has not yet been published in any form outside of a few graphs recently shared on Twitter. The fact that this sort of analysis is being done at all shows how much business owners have become focused on the idea of “overemployment.”
With massive layoffs over the last few years and a more competitive job market, it is no longer the case that employees can quit or get laid off and get a new job making more money. Meta and X have famously done huge rounds of layoffs and Elon Musk famously claimed that X didn’t even need those employees to keep the company running. When Denisov-Blanch was asked if his algorithm was being used by companies to help inform layoffs, he said: “I can’t specifically comment on whether we were or were not involved in layoffs [at any company] because we’re under strict privacy agreements.”
Government is now in the process of developing a National AI Strategy and a SAFE Innovation Framework that will contain a plan to address economic and job impacts from the use of AI. Regulatory agencies will have the capacity to shape the ways in which AI will affect workers — either through their action or inaction.
The scale and the way advanced AI in the automotive workplace will impact workers remains unknown. Some potential effects could include replacing workers, complementing workers, freeing workers up to do more productive tasks, or creating new jobs. As advanced AI is deployed, consolidators and corporate-owned shops are most likely to adopt AI tools and systems first, meaning that a large number of auto service and repair workers will be affected soon. Goldman Sachs estimates that “roughly two-thirds of current jobs are exposed to some degree of AI automation, and that generative AI could substitute up to one-fourth of current work.”
While new technology has enormous potential, auto shop owners need to be attuned to the limitations and ensure that AI is used with close human supervision.
Steven E. Schillinger is an accredited Professional Engineer and often speaks at auto industry meetings about EPA, OSHA and Fire Marshal regulations. He is certified for ANSI/ASHRAE/USGBC/IES Standard 189.1-2014 and works with companies to resolve and remove environmental, health and safety violations.
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