Challenges and Strategies Used In Implementing AI Governance: A Systematic Literature Review

Artificial Intelligence (AI) is reshaping decision-making by enabling automation and innovation, but it also introduces risks such as bias, opacity, and ethical concerns. Organizations recognize the need for effective AI governance to ensure fairness, transparency, and legal compliance. Translating these principles into everyday practice remains challenging. Complexity in regulations, rapid technological change, and conflicting stakeholder priorities compound these difficulties. This thesis investigates the question: What are the challenges and strategies involved in implementing AI governance? A systematic literature review using the PRISMA framework analyzed peer-reviewed publications from 2019 to 2025. Studies were sourced from academic databases, including Scopus, Springer Link, Web of Science, IEEE Xplore, and ACM. Thematic analysis was then used to identify recurring patterns, challenges, and strategies across the selected literature. The results highlight several key challenges organizations encounter when implementing AI governance, including regulatory fragmentation, limited transparency of AI systems, ethical and data governance issues, organizational integration barriers, and gaps in AI literacy among staff. To address these challenges, organizations have adopted strategies such as improving transparency, conducting ethics audits, engaging stakeholders, strengthening data governance, implementing risk management frameworks, and using monitoring tools. These approaches integrate governance into organizational processes, build trust in AI, and support regulatory compliance. In the literature reviewed, regulatory fragmentation emerged as the biggest obstacle to effective AI governance. Among these, the ethical integration of AI principles stood out across sectors as the most consistently emphasized strategy to guide responsible and trustworthy implementation. This thesis offers a holistic, cross-sector synthesis of AI governance implementation. Specifically, each major challenge - regulatory, technical, organizational, and ethical - is outlined with appropriate strategies for overcoming them, providing practitioners with a ready-to-use roadmap. By addressing the identified barriers through targeted measures, organizations can foster the trustworthy and responsible use of AI technologies. The findings provide a foundation for advancing AI governance in a complex technological environment.