Adopting Artificial Intelligence: Cross-Sector Analysis of AI Adoption Risks

Artificial intelligence (AI) is rapidly being adopted across public and private sectors. This offers significant gains in efficiency, decision making, and access to information. At the same time, AI introduces complex risks related to cybersecurity, privacy, bias, transparency, accountability, and equity that existing governance and security frameworks do not fully address. This paper presents a cross-sector literature review and comparative analysis of AI adoption risks and mitigation strategies across four critical domains: the federal government, libraries, K–12 education, and healthcare. Drawing on peer-reviewed research, institutional frameworks, and policy guidance, the study identifies sector-specific challenges alongside shared systemic gaps, including insufficient long-term evaluation, inconsistent disclosure and provenance mechanisms, limited AI literacy, privacy vulnerabilities introduced by third-party tools, and inequities driven by organizational capacity. The analysis highlights that many AI failures are socio-technical in nature, arising from interactions between model limitations and human oversight rather than solely from technology. Based on these findings, the paper proposes targeted recommendations emphasizing governance frameworks, training integration, lifecycle-based risk management, and scalable best practices adaptable to both well-resourced and under-resourced organizations. By synthesizing risks, mitigations, and unresolved research gaps across sectors, this work contributes a structured foundation for developing responsible, secure, and equitable AI implementation strategies that balance innovation with ethical and operational accountability.