Firms with rigorous quality-management systems are substantially more likely to deploy AI, and AI deployment yields measurable performance gains. The effects concentrate in larger, non‑state firms and where CEOs have IT expertise, highlighting organizational and managerial complements to AI diffusion.

Main Finding

Enterprise quality management (QM)—internalized standardized processes and data governance—significantly increases firms’ likelihood of adopting AI, and AI adoption in turn improves firm performance. The causal relationship is supported by multiple identification strategies using panel data for Chinese A‑share listed firms (2007–2023).

Key Points

• QM is a previously underappreciated internal antecedent of AI adoption: firms with stronger QM practices are more likely to implement AI.
• AI adoption mediates performance gains: QM → higher AI adoption → improved enterprise performance.
• The positive QM → AI effect is stronger when:
  • Firms maintain high innovation sustainability,
  • CEOs have IT backgrounds.
• Heterogeneous effects:
  • Larger firms show bigger QM-driven AI adoption effects than small firms.
  • Non-state‑owned firms exhibit stronger effects than state‑owned enterprises.
  • Effects are more pronounced in highly competitive industries and in eastern regions of China.
• Robustness: results hold under fixed‑effects regressions and alternative causal identification methods (instrumental variables, difference‑in‑differences, event studies).

Data & Methods

• Data: Panel of Chinese A‑share listed companies spanning 2007–2023.
• Primary empirical approach: firm fixed‑effects regression modeling AI adoption as a function of QM and controls.
• Endogeneity/addressing causality:
  • Instrumental variables (IV) approach to isolate exogenous variation in QM.
  • Difference‑in‑differences (DiD) analyses exploiting policy/temporal variation.
  • Event studies to trace dynamics around shocks or policy changes.
• Mechanistic interpretation: QM likely lowers organizational frictions for AI by standardizing processes and improving data governance, making AI deployment feasible and effective.

Implications for AI Economics

• Adoption determinants: internal organizational capital (QM) is a key complement to technological investments; diffusion models should incorporate managerial and process‑quality variables alongside firm size and industry.
• Complementarities: managerial practices and leadership skills (CEO IT expertise) interact with digital technologies to determine returns to AI—policy and firm strategy should account for these complementarities.
• Heterogeneity matters: productivity gains from AI are not uniform; they depend on firm type (size, ownership), region, and industry competition. Economic models of AI’s aggregate effects must account for uneven adoption driven by QM differences.
• Policy implications:
  • Policies that encourage QM practices (standards, certifications, data governance) can accelerate AI diffusion and raise overall productivity.
  • Leadership training and incentives to build IT expertise among top management amplify adoption benefits.
  • Targeted support for smaller, state‑owned, and less-developed regional firms can reduce adoption gaps and improve inclusive growth from AI.
• Research directions:
  • Quantify the contribution of QM to aggregate AI-driven productivity gains.
  • Disentangle micro‑mechanisms (which QM components matter most).
  • Test external validity beyond Chinese listed firms and explore cost‑benefit dynamics for SMEs and nonlisted firms.