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Common AI project pitfalls include misaligned objectives, poor data quality, and inadequate MLOps infrastructure. When choosing between open-source LLMs (like LLaMA, Phi) and proprietary APIs (OpenAI, Anthropic), consider flexibility, cost, compliance needs, and technical expertise. Success metrics should combine technical KPIs (accuracy, latency, model drift) with business outcomes (revenue, churn, productivity). Future-proofing AI strategy requires modular architecture, automated retraining pipelines, open standards for interoperability, and robust MLOps governance frameworks with versioning and monitoring.