Generative Artificial Intelligence (GenAI) is rapidly transforming business operations by enabling advanced capabilities in content generation, decision-making, and process innovation. While organizations are increasingly experimenting with GenAI technologies, a significant gap persists between pilot-stage experimentation and enterprise-level implementation. Existing studies primarily focus on the applications and benefits of AI, with limited attention to structured approaches for scaling GenAI in a responsible and sustainable manner. This study addresses this gap by examining the role of GenAI as a catalyst for business transformation and process innovation, with a particular focus on pilot-based implementation strategies. Adopting a narrative literature review methodology, the study synthesizes insights from academic research, industry reports, and case studies published between 2019 and 2025. The analysis identifies key challenges in GenAI adoption, including governance issues, data quality concerns, ethical risks, and workforce resistance, which hinder large-scale implementation. Drawing on theoretical perspectives such as Dynamic Capabilities Theory, Resource-Based View (RBV), Institutional Theory, and risk management frameworks, the study proposes a structured and integrative framework for GenAI adoption. The framework emphasizes a phased approach involving pilot testing, risk assessment, governance integration, and scalable deployment. The findings highlight that successful GenAI implementation requires not only technological readiness but also alignment with organizational strategy, robust governance mechanisms, and continuous workforce development. This study contributes to the existing literature by offering a practical and actionable pathway for transitioning from experimentation to enterprise-scale adoption. It also provides valuable implications for business leaders and policymakers seeking to leverage GenAI responsibly while maximizing its transformative potential.