Green-digital technologies—from AI to electrification—are already present in German carmaking but adoption is uneven across occupations; while engineers and technical staff report higher exposure and upskilling opportunities, many frontline workers see limited access and fear job disruption, underscoring gaps in worker voice and policy support.

Main Finding

At Volkswagen, the dual transformation driven by digitization and e-mobility is already “in the making” and experienced by employees as routine, not as a one-off shock. Rather than being passive, many employees proactively shape their own transitions through internal mobility, information-seeking and training. Trust in corporate management and works councils is relatively high (less so in politics), and substantial — and often underestimated — human and organizational resources exist within the firm to enable ongoing technological change.

Key Points

• Study focus: qualitative and quantitative snapshot (autumn 2021–mid 2022) of how employees experience and cope with the dual transformation of digitization and e-mobility at Volkswagen.
• Transformation clusters: employees were classified into three clusters that differ in exposure and trajectories:
  • Changing Champions: groups historically central to production/value creation, predicted to be relatively negatively affected by the dual transformation.
  • Challenged Stars: groups expected to gain or be upgraded (e.g., UX/UI, IT security, data science).
  • Masters of Transition: employees who already pursued longer qualification paths or major internal professional shifts.
• High internal dynamism and agency:
  • Proactive information-seeking about short courses is widespread (Challenged Stars 88%; Changing Champions and Masters of Transition ~75–76%).
  • Masters of Transition: 71% informed themselves about potential job changes in last 12 months.
  • Changing Champions show strong internal application activity (46% applied internally several times), despite long tenure.
• Change is perceived as “normality”: many workers see work content changing but not disappearing; transformation often reframed as task reorientation rather than pure job loss.
• Trust patterns:
  • Management and works councils enjoy majority-positive trust on both strategic/economic competence and on job security/training.
  • Trust in political decision-making is notably lower.
• Outlook and sentiment:
  • Employees are more optimistic about Volkswagen (Group: ~71% optimistic) and their own plant/workplace (plant ~62%; workplace security ~65%) than about the automotive industry overall (industry optimism ~45%; 18% pessimistic).
• Underestimated resource: employees’ experience, adaptability and willingness to retrain are important latent assets for managing transitions.
• Hurdles (summarized in full report): living environment and lack of information are barriers to re-/upskilling; motivation is often content-driven.

Data & Methods

• Mixed-methods, bottom-up design combining qualitative and quantitative approaches:
  • Qualitative: 11 work-sociological case studies; over 100 qualitative interviews and numerous workshops; almost 200 employees/managers/experts/stakeholders engaged in in-depth formats.
  • Quantitative: company survey with 3,520 Volkswagen employees covering transformation experience, resources and readiness; an online survey with 600+ employees from the broader automotive industry; industry contextualization using BIBB/BAuA Employment Survey 2018.
• Sampling and cluster construction: professional groups were pre-classified into transformation clusters based on predicted exposure to digitization and e-mobility (following prior employment-projection work, e.g., Bauer et al. 2020).
• Timeframe caveat: surveys were conducted between autumn 2021 and early summer 2022 — a snapshot in an evolving process; some results/recommendations may have since changed.

Implications for AI Economics

• Worker agency and internal reallocation matter for AI adoption models:
  • Models of labor market adjustment to AI should incorporate strong firm-level internal mobility, active employee reskilling, and endogenous human-capital investment rather than assuming passive displacement.
• Heterogeneity of exposure calls for targeted policies and firm programs:
  • Distinct clusters (Changing Champions, Challenged Stars, Masters of Transition) imply that one-size-fits-all reskilling or automation forecasts will misstate impacts. Tailored retraining pathways and role re-designs are required.
• Importance of trust and institutional design:
  • High trust in management/works councils facilitates implementation of disruptive technologies. Economists and policymakers should treat co-determination, social dialogue and corporate governance as key levers that affect the pace and distributional outcomes of AI-driven transitions.
• Training design and information provision:
  • Employees seek content-driven, modular and practically relevant training. Effective AI-reskilling programs should prioritize accessibility, clear career pathways, and information on opportunities (addressing "information frictions").
• Local frictions and unequal access:
  • Living environment (e.g., commuting, regional training availability) and information gaps are real constraints. Spatial and infrastructural considerations should be included in economic assessments of AI-related labor market transitions.
• Policy vs firm-level roles:
  • Lower trust in political actors suggests public policy must be better integrated with firm-level initiatives (e.g., co-funded retraining, certification portability) and communicated through trusted intermediaries (works councils, management).
• Reframing displacement forecasts:
  • Empirical evidence from VW indicates that technological change often reshapes tasks rather than eliminates work wholesale. AI-economics research should emphasize task reallocation, augmentation, and job-content transformation in quantitative projections.
• Measurement recommendation:
  • Bottom-up, firm-level mixed-methods are valuable complements to macro/occupation-based automation forecasts. They reveal latent capacities (motivation, informal learning, internal vacancy channels) that change aggregate adjustment dynamics.

Suggested practical actions for firms and researchers: - Firms: invest in modular, content-oriented training; strengthen internal mobility platforms; integrate works councils in transition planning; improve information dissemination on retraining and career paths. - Researchers/economists: incorporate internal labor-market dynamics, trust and institutional arrangements into models; use firm-level panel data and qualitative insights to refine predictions of AI impacts.