Key Takeaways
- 88% of automotive executives said the industry will face a skills shortage in the next 3 years
- 73% of employers say they have difficulty finding candidates with the right skills
- 75% of employers report skills gaps as a major hiring challenge
- 90% of jobs in automotive-related sectors will require upskilling by 2030 due to digitalization and green transition
- 44% of workers will need reskilling or upskilling by 2027 (worldwide across industries)
- 23% of jobs expected to be automated by 2027 globally
- 52% of employers offer formal training programs (all industries)
- 63% of employers provide on-the-job training
- 68% of organizations provide training for new technologies
- 70% of workers learn best through practical work-based training
- 35% of aftermarket companies planned to upskill employees as a priority in 2023
- 27% reduction in technician turnover after implementing a structured upskilling program
- 12% higher productivity reported by companies that invest in training vs those that don’t
- 4.6 million people employed in automotive repair and maintenance in the US (2023)
- 5.9% unemployment rate for mechanics and repairers (US, 2023)
Automotive aftersales faces urgent skills shortages, with most jobs needing digital and EV training by 2030.
Related reading
Skills Shortage & Hiring Demand
Skills Shortage & Hiring Demand Interpretation
Technology-Driven Upskilling Needs
Technology-Driven Upskilling Needs Interpretation
Training Programs, Certifications & Learning Pathways
Training Programs, Certifications & Learning Pathways Interpretation
Training Programs, Learning Pathways
Training Programs, Learning Pathways Interpretation
Business Impact, ROI & Workforce Outcomes
Business Impact, ROI & Workforce Outcomes Interpretation
Regional/Market Demand & Aftermarket Labor Dynamics
Regional/Market Demand & Aftermarket Labor Dynamics Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Thomas Lindqvist. (2026, February 13). Upskilling And Reskilling In The Automotive Aftermarket Industry Statistics. Gitnux. https://gitnux.org/upskilling-and-reskilling-in-the-automotive-aftermarket-industry-statistics
Thomas Lindqvist. "Upskilling And Reskilling In The Automotive Aftermarket Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/upskilling-and-reskilling-in-the-automotive-aftermarket-industry-statistics.
Thomas Lindqvist. 2026. "Upskilling And Reskilling In The Automotive Aftermarket Industry Statistics." Gitnux. https://gitnux.org/upskilling-and-reskilling-in-the-automotive-aftermarket-industry-statistics.
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