This article examines the global efforts to govern and regulate Artificial Intelligence (AI) in response to its rapid development and growing influence across many parts of society. It explores how governance takes place at multiple levels, including international bodies, national governments, industries, companies, and communities. The study draws on a wide range of official documents, policy reports, and international agreements to build a timeline of key regulatory and standardization milestones. It also analyzes the challenges of coordinating across different legal systems, economic priorities, and cultural views. The findings show that while some progress has been made through soft-law frameworks and regional partnerships, deep divisions remain. These include unclear responsibilities, uneven enforcement, and risks of regulatory gaps. The article argues that effective AI governance requires stronger international cooperation, fair and inclusive participation, and awareness of power imbalances that shape policy decisions. Competing global and commercial interests can create obstacles to building systems that prioritize the public good. The conclusion highlights that future governance models must be flexible enough to adapt to fast-changing technologies, yet consistent enough to protect rights and promote trust. Addressing these tensions is critical for building a more just and accountable future of AI.

Background: Fulfilling a recognized need in data skills training for academic librarians, the National Library of Medicine and the National Institutes of Health All of Us Research Program collaborated to enhance academic library workers’ skills in biomedical and public health data, as well as their library's research capacity, through the awareness and use of the All of Us Researcher Workbench. Case Presentation: The All of Us Data Training and Engagement Program for Academic Libraries (ALP) blended professional development training, hands-on learning, and peer-to-peer networking that focused on increasing knowledge of the All of Us Researcher Workbench. Activities were designed to build institution-wide, interdisciplinary awareness and interest in using the All of Us Researcher Workbench. A series of ongoing activities ensured sustained skill-building and collaboration, and included training for R, a program language used for statistical computing and data visualization. Program activities were intentionally designed to help grow institutional research capacity, enhance skills in biomedical and public health data, and promote meaningful use of the Researcher Workbench to campus communities. Conclusion: The ALP helped participants overcome barriers to data access and improve research infrastructure and successfully empowered 115 library professionals to leverage the All of Us Researcher Workbench for meaningful biomedical and public health research. Measured outcomes validate the success of the program and demonstrate how the ALP has positioned participating institutions for long-term success in biomedical and public health research. Institutions can build upon the foundation established through this case report to advance equitable, data-driven health research across academic landscapes.