How family enterprises use AI to drive sustainable innovation: regulatory restrictiveness and family governance effectiveness as resource passageways

Purpose This study aims to examine how artificial intelligence (AI)-driven innovation enhances sustainability performance in family enterprises and why perceived regulatory restrictiveness and family governance effectiveness shape the conversion of AI resources into innovative capability. Design/methodology/approach Drawing on conservation of resources (COR) theory, AI-driven innovation is conceptualized as a firm-level resource caravan, while perceived regulatory restrictiveness and family governance effectiveness are treated as caravan passageways that constrain or enable resource conversion. A two-study quantitative design is used. Study 1 is a 2 × 2 randomized vignette experiment (n = 200) manipulating regulatory restrictiveness and family governance effectiveness. Study 2 is a three-wave time-lagged survey of senior managers from 240 family-controlled firms in Karachi, Pakistan. Measurement validity is assessed through CFA/SEM, while conditional effects are estimated with validated observed composites in Hayes PROCESS using 5,000 bootstrap resamples. Findings AI-driven innovation positively influences innovative capability and sustainability performance, with innovative capability providing a significant but partial mediation. This partial mediation indicates that AI contributes to sustainability both directly, through efficiency, monitoring and optimization, and indirectly, through innovation routines. Perceived regulatory restrictiveness weakens the AI-driven innovation → innovative capability link and reduces the indirect effect on sustainability, whereas family governance effectiveness strengthens this link and amplifies the indirect effect. Practical implications Family-firm leaders should treat AI as a strategic resource bundle rather than a stand-alone technology purchase. Predictive maintenance, ESG analytics, automation and generative design applications require different capability investments, including data quality, analytics literacy, cross-functional experimentation and formal governance oversight. Policymakers should design AI and sustainability regulations that are clear, predictable and enabling rather than primarily punitive. Originality/value This study extends COR theory to AI-enabled sustainable innovation in family enterprises by showing that AI resources produce sustainability value through both direct optimization and capability-building pathways. It clarifies why similar AI investments generate different outcomes across family firms by identifying perceived regulatory restrictiveness and family governance effectiveness as boundary conditions in an emerging-economy context.