Desire for AI Exists, Yet Structural Legacy Issues Restrict Progress

The automotive and industrial manufacturing sectors are investing heavily in AI. However, legacy issues, resistance, and a lack of skills are slowing down the transition. 

No other sector invests so strategically in AI while simultaneously struggling so much with legacy issues and resistance as automotive and industrial production. This is the finding of the Agentic AI study conducted by the consulting firm BearingPoint. The study focuses on automotive and industrial production, analyzing their AI strategies, challenges, and priorities in direct comparison with other industries. The results show that automotive and industrial companies "differ significantly from the cross-industry average in several areas, both in their approach and in the structural hurdles they face", according to BearingPoint.

Desire for AI Innovation Meets System Rigidity

According to the study, automotive and industrial production struggle with outdated IT systems and production processes significantly more often than other industries. 60 % of automotive and industrial production executives surveyed by BearingPoint would consider integration with legacy systems to be the biggest hurdle – compared to only 29 % in other industries.

Automotive and industrial production also show a different profile at the cultural level: Organizational resistance to change is cited by 51 % of companies in automotive and industrial production, compared to 20 % in other industries. Executives in automotive and industrial production report deep-rooted routines and hierarchies significantly more often. The willingness to change is noticeably lower compared to other industries.

AI Creates Overcapacity

In addition, the study shows that automotive and industrial production expects significantly higher AI-induced overcapacity compared to other industries, both today and by 2028. "Overcapacity is already being reduced today, but it often affects the wrong skilled workers. This exacerbates the already significant skills gaps in dealing with AI", says BearingPoint. By 2028, the executives surveyed in automotive and industrial production expect a significantly higher personnel surplus than their counterparts in other industries.

At the same time, however, the study found that AI-related skills gaps are perceived as significantly more pronounced than the cross-industry average, both today and in the future. The speed of AI adoption exceeds the ability to retrain and redeploy workers.

AI Approach: Resilience Instead of Risk

In contrast to other industries, automotive and industrial production (67 %) is more likely than average to pursue an AI approach that focuses on balance and resilience, compared to 37 % in other industries, according to the study. This is due, among other things, to strict regulatory requirements, high costs in the event of failure, and complex IT and organizational structures.

At the same time, automotive and industrial manufacturing companies invest significantly less in traditional retraining programs than other industries: Only 27 % focus on reskilling, compared to 46 % in other industries. The focus is therefore less on specific qualifications and technical skills and more on building resilience, both structurally and at the employee level.

This is a partly automated translation of this German article.