- December 2025
The automotive and industrial manufacturing sectors face a strategic paradox. Companies are investing heavily in artificial intelligence (AI), yet the biggest challenges are not technological, but structural. These insights come from the BearingPoint study Resilient by design – How agentic AI is reinventing organizations, with a sector-specific analysis of the automotive and industrial manufacturing sectors. The results are clear: 60% of companies struggle to integrate AI with decades-old IT and production systems, and 51% cite cultural resistance as a critical barrier. AI can unlock efficiency and innovation, but only if organizations actively reshape the structures that hold them back.
Graphic 1: The biggest hurdles in AI transformation; automotive and industrial manufacturing n=200; other industries n=811
The biggest challenges for AI in automotive and industrial manufacturing
Legacy systems as a structural bottleneck
60% of companies in the automotive and industrial manufacturing sectors see integration with legacy IT and production systems as the biggest hurdle, compared to 29% in other industries.
The study shows that no other sector struggles as much with outdated IT landscapes and decades-old production systems. Proprietary interfaces, complex system landscapes, and costly retrofit requirements turn AI integration into a long-term undertaking. Without deliberate modernization, AI risks becoming an expensive experiment rather than a scalable solution.
Cultural resistance slows transformation
51% of leaders identify organizational resistance as a central problem.
Deeply entrenched hierarchies, established routines, and fear of change are particularly pronounced in automotive and industrial manufacturing. Introducing AI reshapes processes, responsibilities, and decision-making. Without strong change management and transparent communication, transformation efforts stall within the existing structures.
Skill gaps and overcapacity gaps
By 2028, companies in automotive and manufacturing expect significantly larger skill gaps and workforce overcapacity than other sectors.
AI-driven efficiency gains are outpacing overall market growth, creating strategic risks from structural overcapacity. At the same time, capability gaps are widening. The speed at which new skills are required exceeds the pace at which employees can be reskilled or redeployed.
How the industry responds to AI uncertainty
Limited investment in reskilling
Only 27 % of companies invest in retraining and change management, a surprisingly low figure given the growing skill gap and expected overcapacity. In automotive and industrial manufacturing, AI is currently used primarily to increase efficiency rather than to develop the workforce. Many companies expect short-term overcapacity and unclear role profiles, making it difficult to commit to targeted reskilling programs.
Focus on resilience and adaptability
Instead of investing in specific skills, companies in automotive and industrial manufacturing are disproportionately investing in resilience and organizational adaptability (75% vs. 53%). The emphasis is on the ability to endure uncertainty, manage change, and adapt structures quickly.
Graphic 2: Industry responses to AI uncertainty; Automotive & Industrial Manufacturing n=200; other industries n=811
Resilience is a strategic form of talent development—with a different focus than traditional reskilling. It is less about technical skills and more about the ability to handle uncertainty and change. Particularly in the automotive and manufacturing sectors, where the impact of AI is highly complex, companies are increasingly investing in resilience-building measures. Reskilling is useful, but not equally for all employees. The greatest leverage lies in the long-term adaptability of the organization—not in short-term efficiency.
Manuel Schuler, Global Head of Automotive and Industrial Manufacturing
Recommendations for action
To capitalize on AI opportunities in a challenging environment, clear priorities are essential. Key focus areas include:
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Don’t wait—take action: drive change proactively
Organizations need clear priorities, decisive leadership, and visible interventions in routines and structures. Without determined action, deeply rooted resistance will not break down.
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Develop employees and build core competencies
Not all current roles can be meaningfully developed. Organizations need a clear decision-making framework to identify which employees can take on new responsibilities and which roles may be phased out over time. In addition to training programs, organizations must strengthen their ability to deal with uncertainty and change. External expertise can help anchor new skills and sustainable ways of working.
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Modernize legacy systems and maximize ROI
Companies should make deliberate decisions about which systems to modernize, bridge, or retire. Legacy systems cause hidden follow-up costs and slow innovation by tying up disproportionate resources. Modernization is not optional. It is a prerequisite for scalable AI.
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Ensure future-proof AI strategies with agile decision-making and transformation models
Organizations need long-term but adaptable transformation paths that guide and accelerate progress. Traditional scenario planning reaches its limits in highly dynamic environments, which is why new roles, agile teams, and secure test environments are becoming increasingly important. These enable rapid experimentation at manageable risk and accelerate AI adoption across the organization.
