What questions should be considered for AI governance and management?

• A. Performance/ROI, business alignment, organizational readiness, risk management, regulatory compliance, data privacy/security, bias mitigation, framework adoption, and human resources
• B. Only ROI and cost
• C. Only regulatory compliance
• D. Only data privacy

Answer: (A)

AI governance and management require an integrated, cross‑functional approach that balances delivering value with managing risks and obligations. Considering performance and ROI alongside business alignment ensures AI efforts contribute to strategic goals and deliver measurable benefits. Organizational readiness matters because people, processes, and culture must support ongoing development, deployment, monitoring, and governance of AI systems. Risk management is central to identifying issues such as model drift, operational failures, security threats, and unintended consequences, and it requires built‑in controls throughout the lifecycle. Regulatory and legal considerations ensure compliance with laws and industry rules, while data privacy and security address how data is collected, stored, and protected, safeguarding sensitive information. Bias mitigation helps prevent discriminatory outcomes and builds trust in AI decisions. Framework adoption establishes standardized methods, governance structures, and accountability for repeatable, auditable processes. Human resources focus on the needed skills, roles, training, and ongoing governance capabilities to sustain the program. This broad, interconnected set of questions is essential for effective AI governance, rather than focusing narrowly on cost, compliance, or data privacy alone.