A study of skilled-trade workers indicates that 52% of automotive and fleet respondents experience vehicle callbacks or rework at least sometimes, while 36% already utilize consumer artificial intelligence applications—such as ChatGPT, Gemini, or Claude—to assist with daily workplace tasks.
“This survey shows where AI can help tradespeople most on the job, and reducing mistakes and callbacks comes out at the very top,” stated Dave Dickson, founder of Bourne AI.
Bourne AI commissioned the survey, which sampled 1,000 workers across the United States, the United Kingdom, Germany, and France between May 28 and June 14, 2026. The automotive cohort comprised 44 respondents, combining collision repair technicians with other auto and fleet service occupations.
Within this automotive subset, 48% of respondents reported that locating technical guidance typically requires a minimum of 16 minutes. Furthermore, 64% stated that an AI tool would shorten this lookup window, and 57% indicated the technology would improve their on-the-job efficiency. Near-term adoption is also anticipated, with 61% of the automotive respondents expecting their employers to invest in workplace AI solutions within the next two years.
The broader multi-industry data revealed parallel operational challenges across the general skilled trades. Across all surveyed sectors, 49% of workers reported that callbacks or reworks occur at least sometimes.
The data shows that a majority of technicians must interrupt active workflows to search for technical data. Direct consultation with a colleague or supervisor remains the primary source of assistance, utilized by 60.5% of respondents. Online search engines are used by 41.7%, while 32.7% employ consumer AI assistants.
For those requiring workflow guidance, more than half stated the inquiry required 16 minutes or longer, with approximately 25% reporting retrieval times exceeding 30 minutes. In instances where immediate data was unavailable, 28% of respondents proceeded using their own judgment despite the risk of error, while 21% applied a temporary workaround to return to the task later.
When evaluating potential features for dedicated corporate AI platforms, workers identified the mitigation of errors and subsequent rework as the highest priority. Accelerated access to mechanical specifications ranked second, followed by automated support for compliance and customer documentation, and the onboarding of apprentice technicians. Notably, among respondents with existing access to specialized enterprise software, 60% still searched external sources to find required information.
Dickson noted that the primary objective of specialized industrial AI should be providing verified, real-time documentation directly at the point of repair. “When AI closes that guidance gap, the return is concrete and measurable: fewer jobs come back for rework, and newer workers reach full productivity faster,” he stated.
The published study measured the baseline experiences, perceptions, and expectations of the workforce. The research parameters did not actively test or verify whether an AI platform reduces actual cycle times, error frequencies, repair durations, or training timelines in a live shop environment.
