Firms that report greater AI use also report stronger governance and CSR—and higher performance—with human–AI collaboration and big‑data knowledge management amplifying these links; however, the evidence is correlational and based on a single-region manager survey.

Main Finding

Use of AI in manufacturing firms positively influences corporate governance (CG) and corporate social responsibility (CSR), and these in turn improve firm performance. Human–AI collaboration (AI‑HI) strengthens the effects of AI on CG and CSR, while big‑data knowledge management (BDKM) strengthens the contribution of CG and CSR to firm performance.

Key Points

• Theoretical framing: resource‑based view and stakeholder theory.
• Sample and setting: survey of 404 manufacturing firm managers in Punjab, Pakistan.
• Method: Partial Least Squares Structural Equation Modeling (PLS‑SEM).
• Direct effects:
  • AI use → Corporate governance: β = 0.606 (positive)
  • AI use → CSR: β = 0.535 (positive)
  • Corporate governance → Firm performance: β = 0.294 (positive)
  • CSR → Firm performance: β = 0.237 (positive)
• Moderation effects:
  • AI‑HI collaboration moderates AI use → CG (β = 0.164) and AI use → CSR (β = 0.108), i.e., human–AI synergy amplifies AI’s governance and CSR benefits.
  • BDKM moderates CG → Firm performance (β = 0.115) and CSR → Firm performance (β = 0.113), i.e., strong big‑data knowledge management enhances the governance/CSR-to‑performance link.
• Overall argument: integrating AI into governance and CSR frameworks, supported by human–AI collaboration and BDKM, yields better and more sustainable firm performance.

Data & Methods

• Data: cross‑sectional survey data from 404 managers in manufacturing firms (Punjab, Pakistan).
• Analysis: PLS‑SEM to estimate direct and moderated relationships among AI use, AI‑HI collaboration, CG, CSR, BDKM, and firm performance.
• Limitations (implicit from design): single region and sector, cross‑sectional/self‑reported measures → limits causal inference and generalizability.

Implications for AI Economics

• Firm strategy and investment:
  • AI adoption generates positive firm‑level returns not only via operational gains but by improving governance and CSR outcomes that translate into performance.
  • Investments in human capital (to enable AI‑HI collaboration) and in big‑data knowledge management are complementaries that amplify AI’s payoff.
• Productivity and capability economics:
  • BDKM and human–AI collaboration act as complementary intangible assets (akin to organizational capital) that increase the productivity of AI investments — underscoring complementarities between technology and organizational capabilities.
• Labor and skills policy:
  • Economic gains from AI are conditional on human skills; policies and firm strategies should emphasize upskilling to realize governance/CSR benefits.
• Regulatory and institutional design:
  • Effective AI‑enabled governance and CSR depend on data governance and knowledge management; regulators and industry bodies should support standards, data infrastructure, and incentives for responsible AI use.
• Research and measurement:
  • Future empirical work should use longitudinal designs, diverse sectors/countries, and objective performance measures to quantify general equilibrium effects and distributional consequences (e.g., labor market impacts) of AI adoption mediated through governance/CSR.

Recommendations for practitioners: adopt AI with a parallel focus on human–AI collaboration and BDKM to maximize governance and CSR benefits and thereby firm performance.