Artificial intelligence (AI), especially generative AI, has moved from pilot to production across industries, reshaping project delivery from planning to execution. Adoption is rapid, yet outcomes remain uneven: many teams report frustration when outputs sound plausible but lack accuracy or context. This guide addresses that gap by combining evidence-based practices with PMI-aligned governance. It explains how large language models (LLMs) work (probabilistic next-token prediction), why hallucinations occur, and how to mitigate them through structured prompting and risk-based controls. It defines key terms—complicated vs. complex tasks, hallucination, and sets a clear boundary: AI accelerates well-defined, low-risk work; humans retain accountability for complex, high-consequence decisions. Practical tools include the CAPTURES？ prompt pattern, a 90-day rollout plan and lightweight controls mapped to ISO/IEC 42001. The goal is to deliver measurable cycle-time and clarity gains while maintaining trust and ethical standards.
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