Supply‑chain visibility delivers value only when firms build managerial systems to act on signals; interoperable data, clear exception‑routing, and institutionalized learning — not analytics alone — determine whether information yields coordinated action.

Main Finding

Technology-enabled visibility in supply chains only produces coordinated, timely action when paired with managerial microfoundations that convert information into decisions and execution. The authors define these microfoundations as an adaptive decision-making capability (ADMC) — composed of interoperable data architectures, conversion routines that filter and route exceptions to accountable owners, and learning stabilizers that preserve reliable adjustment — which in turn enables adaptive supply chain capability (ASCC) (agility, resilience, alignment, learning velocity).

Key Points

• Visibility-to-action problem: firms collect more signals than they can process; information often fails to produce coordinated outcomes because it is not routed, prioritized, or executed fast enough.
• ADMC (Adaptive Decision‑Making Capability) is the proposed higher-order managerial capability that links information, judgment, and action into self‑reinforcing learning cycles.
• Three microfoundations of ADMC:
  • Interoperable data architectures (technical and semantic integration to make signals usable).
  • Conversion routines (procedures that filter, prioritize, and route exceptions to accountable decision owners).
  • Learning stabilizers (processes that preserve and institutionalize reliable adjustments over time).
• ADMC is theorized to enable ASCC (Adaptive Supply Chain Capability) manifested as increased agility, resilience, alignment across units, and faster learning/adjustment.
• Advantage depends less on acquiring tools than on intentionally designing architectures, governance, and routines that convert visibility into coordinated action.

Data & Methods

• Qualitative, theory‑elaborating study.
• Methods: theories‑in‑use approach combined with the Gioia methodology for systematic inductive qualitative analysis.
• Data: 25 interviews with Chief Supply Chain Officers (CSCOs) across manufacturing, retail, logistics, and technology sectors.
• Output: conceptual specification of microfoundations linking technology-enabled visibility to coordinated action in data‑intensive SCM contexts.

Implications for AI Economics

• Complementarities matter: Economic gains from AI and visibility technologies in supply chains are mediated by organizational capabilities (ADMC). Models of AI-driven productivity should include managerial and governance complements, not just tool deployment.
• Investment returns: Firms that invest in interoperable data architectures and conversion routines (rather than only in standalone analytic tools) will likely realize higher returns from AI/visibility investments. This suggests heterogeneity in realized productivity gains across firms due to differences in ADMC endowments.
• Measurement and empirical strategy: Researchers should seek proxies or instruments for ADMC microfoundations (e.g., data interoperability indices, presence of formal exception-routing routines, metrics for institutionalized learning processes) when estimating causal effects of AI/visibility tech on performance.
• Market dynamics and inequality: If ADMC is costly or hard to develop, first movers or firms with stronger managerial capabilities could capture disproportionate gains from AI-enabled supply-chain visibility, amplifying concentration and competitive gaps across firms and sectors.
• Labor and organizational design: Effective AI deployment requires reallocation of decision rights, role definitions for exception ownership, and upskilling to support judgment and learning stabilizers — implying complementarities between automation and human governance that affect labor demand and wage structures.
• Policy and standards: Standards for data interoperability and governance practices could lower frictions to converting visibility into action, increasing the social returns to AI investments and reducing barriers for smaller firms.
• Future research directions: Quantify ADMC and ASCC, test their mediating role between AI/visibility technology adoption and firm-level outcomes, and evaluate interventions (e.g., governance changes, training, platform standards) that foster the microfoundations.

Suggestions for researchers/practitioners: - When evaluating or deploying AI in supply chains, diagnose the three microfoundations (data architecture, conversion routines, learning stabilizers) and prioritize fixes there before scaling analytic models. - In empirical work, control for or instrument managerial capability when estimating effects of visibility/AI on productivity or resilience.