Adopt a balanced, risk-aware stance that acknowledges AI's productivity benefits while emphasizing human oversight, security, and correctness. Structure your answer around use cases, boundaries, safeguards, and team rollout, aligning with IBM's enterprise-grade standards and responsible AI principles.
Pro tip: Mention IBM's internal AI governance frameworks (e.g., AI Ethics Board) and tools like watsonx to show you understand enterprise constraints and can navigate them. Also, emphasize that you'd pilot guidelines with a small team before scaling, demonstrating pragmatism.
Describe where generative AI adds value, such as code generation, documentation, test creation, and debugging assistance. Highlight that it's best for low-risk, repetitive tasks that can be easily verified.
Explain where you'd avoid AI, such as handling sensitive data, security-critical code, or making architectural decisions. Mention risks like data leakage, IP contamination, and incorrect outputs.
Outline measures like using enterprise-approved tools, anonymizing data, code review, testing, and static analysis. Stress that AI outputs must be treated as untrusted and verified by humans.
Propose a phased approach: start with a pilot, gather feedback, create clear usage policies, provide training, and iterate. Emphasize cross-functional alignment with security, legal, and compliance teams.
Describe ongoing evaluation of AI tool effectiveness and risks, updating guidelines as technology and regulations evolve. Highlight the importance of fostering a culture of responsible experimentation.
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