This guide provides best practices for integrating AI into the software development lifecycle. The numbered list highlights maintaining a rational approach to AI adoption, ensuring human oversight, and recognizing AI limitations. It emphasizes evaluating real use cases, avoiding tool sprawl, and ensuring enterprise readiness. It also stresses creating governance policies, protecting sensitive data, and carefully managing AI training inputs. The guide encourages experimenting with different AI approaches (e.g., symbolic AI vs. LLMs) and sharing learnings to drive adoption. The key takeaway is that AI should enhance—not replace—development workflows, requiring governance, validation, and thoughtful integration into existing processes.

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