AI governance establishes organization-wide policies to regulate AI development and use while mitigating risks. Traditionally, it focused on technical teams ensuring transparency in machine learning (ML) models. However, the rise of Large Language Models (LLMs) has expanded AI governance into an enterprise-wide initiative. Generative AI has democratized access to AI, but many users lack training to assess outputs critically. This shift requires a socio-technical approach to governance that spans technical and non-technical teams across HR, sales & marketing, and operations. AI governance professionals face challenges like stakeholder resistance and lack of executive sponsorship, making organizational Buy-in crucial. Buy-in operates at two levels: top-down and bottom-up. Executive support legitimizes governance efforts, but mandates alone are ineffective without grassroots adoption. Conversely, grassroots engagement requires education, collaboration, and trust-building. Drawing from Participatory Design (PD) and Socio-Technical Systems (STS) Theory, this research presents an HCI-informed approach to embedding governance in workflows. While AI ethics and technical tools are widely studied, few address sustainability of governance adoption. This paper provides actionable, HCI-inspired strategies to design governance processes that are user-friendly, seamlessly integrated, and encourage responsible AI adoption across diverse teams.