Research on Commercial Applications of AI and Data Policy

Generative artificial intelligence (AI) has rapidly permeated commercial and social domains, from personalised recommendations in digital marketplaces to medical diagnostics and urban management systems. The expansion of AI into everyday life brings opportunities and challenges, particularly regarding data governance, ethical oversight, and sustainable technological integration. This paper employs a literature review approach, synthesising academic research, policy frameworks, and case studies to examine the contradictions between AI innovation and data rights protection. Specifically, it analyses the effectiveness of current regulatory regimes such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Chinas Personal Information Protection Law (PIPL). The study highlights core governance dilemmas, including privacy breaches, algorithmic bias, monopolistic data practices, and environmental costs of AI-driven computation. The paper identifies policy gaps by comparing global policy practices and industry self-regulation. It explores the potential of collaborative governance frameworks that integrate government oversight, corporate responsibility, and public participation. The findings suggest that future digital governance must prioritise innovation and accountability, balancing technological empowerment with social equity. Ultimately, this research provides theoretical and practical contributions toward constructing a responsible, sustainable, and rights-based data governance framework for AI applications in daily life.