Data, disruption and weak governance block procurement’s AI payoff: a single-case study of a European carmaker shows that low-quality internal data, limited supplier data access and recurring operational shocks stop procurement from turning analytics into strategic value, and proposes six practical reforms to reposition procurement as a data-driven orchestrator.

Main Finding

A qualitative single-case study of a European automotive procurement department finds that data-driven transformation efforts are critically constrained by contextual frictions and organizational realities. Specifically, poor internal data quality, limited access to external supply-chain data, recurring operational disruptions (shortages, supplier insolvencies), and weak institutionalization of data-driven practices prevent procurement from realizing the value of data-driven initiatives. The paper proposes six actionable recommendations (e.g., structured supplier data negotiation, embedded qualification programs, sustained leadership engagement) to reposition procurement as a strategic enabler of data-driven, resilient supply chains.

Key Points

• Primary barriers to value realization:
  • Low internal data quality and readiness.
  • Limited access to timely, reliable external supply-chain data.
  • Recurrent operational disruptions (shortages, insolvencies) that complicate analytics and decision-making.
  • Insufficient institutionalization (governance, processes, roles) of data-driven activities within procurement.
• Consequence: Procurement departments that were historically cost-focused must shift toward orchestration roles but are hampered by the above frictions.
• Recommendations (high-level themes identified in the study):
• Improve internal data quality and management practices.
• Secure better access to external supplier/supply-chain data via structured agreements.
• Embed supplier qualification programs to ensure data and process standards.
• Institutionalize data-driven activities through governance, processes, and roles.
• Maintain sustained leadership engagement to foster a data-driven mindset and change management.
• Treat procurement as a strategic catalyst for enterprise-wide data management.
• Contribution: Advances a domain-specific understanding (procurement in automotive) of how data-driven transformations unfold under crisis and uncertainty, offering actionable guidance for practitioners.

Data & Methods

• Approach: Qualitative case study of a procurement department within a European automotive firm.
• Nature of evidence: Empirical, context-rich findings about frictions and organizational constraints; study emphasizes real-world operational realities rather than purely technical solutions.
• Methodological limitations (inferred from abstract):
  • Single-case qualitative design improves depth and contextual validity but limits broad generalizability.
  • The abstract does not detail specific methods (e.g., interviews, observations, documents), sample size, or analytic procedures.

Implications for AI Economics

• Data quality and availability are gatekeepers for value from AI/analytics in supply chains: poor internal data and restricted external data sharing materially reduce returns on investments in forecasting, optimization, and other ML-based tools.
• Organizational and institutional barriers matter as much as technical capability: economic models of AI adoption should incorporate frictions like governance, supplier incentives, and disruption exposure when estimating productivity gains.
• Procurement as a strategic intermediary: empowering procurement to negotiate data terms and institutionalize data practices can increase the effective supply of usable data, shifting the economics in favor of more sophisticated AI deployment across firms and supply networks.
• Crisis and uncertainty increase the premium on resilient, explainable models and on investments in data governance that enable rapid, coordinated responses—affecting cost‑benefit calculations for AI projects.
• Policy and market-design implications: incentives or standards that improve cross-firm data sharing (e.g., certification, contractual templates, data quality requirements) could raise aggregate returns to AI in supply chains and reduce systemic fragility.
• Research opportunities: quantify the economic gains from the recommended institutional changes, test cross-industry generalizability, and model how supplier-side constraints alter equilibrium adoption and welfare impacts of supply-chain AI.