AI tools strengthen supply-chain resilience only when paired with an innovation culture: communication and creativity deliver flexibility, speed and efficiency, whereas learning primarily yields process improvements.

Main Finding

AI-powered tools improve firms’ innovation culture (communication, creativity, learning), but only when those cultural capabilities—particularly communication and creativity—are developed do firms achieve broad supply chain resilience (flexibility, efficiency, velocity). Learning, while boosted by AI, mainly improves efficiency and does not significantly enhance flexibility or velocity. Thus AI adoption alone is insufficient for resilience unless paired with innovation-oriented cultural capabilities.

Key Points

• Theoretical framing: Dynamic Capabilities Theory (DCT) — resilience depends on firm-level capabilities that reconfigure resources in volatile environments.
• AI effects on culture:
  • AI-powered tools significantly enhance communication, creativity, and learning.
• Cultural capabilities → resilience:
  • Communication and creativity each have positive, significant effects on all three resilience dimensions: flexibility, efficiency, and velocity.
  • Learning has a significant positive effect only on efficiency; it does not significantly affect flexibility or velocity.
• Interpretation: Learning enabled by AI is being channeled toward process improvement (efficiency) rather than rapid adaptation or speed (flexibility/velocity).
• Managerial takeaway: Investments in AI must be complemented by deliberate cultural development (especially communication and creativity) to realize resilience gains—this is especially important in resource-constrained manufacturing contexts.

Data & Methods

• Sample: Survey of 270 supply chain and operations managers in Jordanian manufacturing firms.
• Design: Quantitative, cross-sectional survey.
• Analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM).
• Hypotheses: Twelve direct hypotheses tested linking AI tools → cultural dimensions (communication, creativity, learning) → resilience dimensions (flexibility, efficiency, velocity).
• Measures: AI-powered tool adoption; innovation culture decomposed into communication, creativity, learning; supply chain resilience decomposed into flexibility, efficiency, velocity.
• Main statistical outcomes: Significant paths from AI to all three cultural dimensions; significant paths from communication/creativity to all resilience outcomes; significant path from learning only to efficiency (non-significant to flexibility and velocity).

Implications for AI Economics

• Complementarity and complementarities matter
  • Organizational capital (innovation culture) is a necessary complement to AI for generating resilience benefits. Returns to AI investment are conditional on complementary cultural capabilities—an important consideration for models of adoption and diffusion in developing economies.
• Allocation of scarce resources
  • In resource-constrained firms, prioritizing investments that foster communication and creativity (training, incentive design, cross-functional teams, knowledge-sharing platforms) may yield higher resilience payoffs per dollar of AI spending than AI alone.
• Direction of productivity gains
  • AI-driven learning appears to be channeled toward process efficiency rather than agility. Economic analyses should distinguish between efficiency gains and adaptive capacity when projecting productivity and welfare gains from AI.
• Policy design
  • Subsidies or grants for AI hardware/software should be paired with support for organizational development (training, managerial practices) to avoid underutilized tech and low social returns.
  • Programs in developing economies should emphasize human capital and cultural change to capture resilience and broader economic benefits.
• Labor-market and firm strategy implications
  • Since learning improves efficiency predominantly, firms may realize cost and throughput gains but remain vulnerable to shocks unless creativity and communication capabilities are cultivated—affecting hiring, retention, and managerial priorities.
• Research and evaluation
  • Economic evaluations of AI adoption should incorporate complementary investments (organizational capabilities) as endogenous variables; failure to do so risks overestimating the standalone impact of AI.
• Externalities and diffusion
  • Heterogeneity in cultural capabilities across firms may create dispersion in AI returns, affecting aggregate productivity gains and inequality among firms within sectors and regions.

(Recommendation: Future economic analyses and policy interventions should treat AI adoption and organizational-capability development as joint investments to accurately assess costs, returns, and resilience outcomes.)