What best practices should be followed for stakeholder engagement in AI?

• A. Stakeholder mapping, prioritization, engagement/communication, ethical AI development, and monitoring
• B. Ignore stakeholders
• C. Only executive briefings
• D. Solely technical metrics

Answer: (A)

Effective stakeholder engagement in AI is a structured, ongoing process that ensures those affected by an AI system are identified, their needs prioritized, and they are involved throughout the lifecycle. Start with mapping who has interest or is impacted by the AI, so you know who should be included. Then prioritize these stakeholders and their concerns so resources focus on the most critical risks and opportunities. Engaging them through clear communication and feedback loops keeps expectations aligned and helps catch issues early. Embedding ethical AI development—covering fairness, accountability, transparency, privacy, and safety—ensures the system reflects shared values and legal obligations. Finally, continuous monitoring of performance, impact, and unintended consequences allows you to adapt as the environment and stakeholder needs evolve. This combination addresses governance, risk, and social implications, not just technical soundness.

Choosing to ignore stakeholders ignores critical inputs and can lead to unforeseen risks. Relying only on executive briefings misses frontline users and communities affected by the AI. Focusing solely on technical metrics neglects governance, ethics, and real-world impact.