Companies often face challenges in deploying AI at production levels, with many projects failing due to unclear goals, poor planning, or unrealistic expectations rather than technology issues. Six main pitfalls were identified from analyzing various AI proof of concepts (PoCs) that either succeeded or failed. These lessons include the importance of having a clear and measurable vision using SMART criteria; prioritizing data quality over quantity with proper preprocessing and validation; avoiding overly complex models by starting simple and focusing on explainability; planning for deployment realities with scalable, reliable infrastructure; maintaining models through monitoring and automated retraining to adapt to changes; and ensuring stakeholder buy-in through transparency, user training, and engagement. Following these best practices helps build resilient AI systems that are robust, trusted, and capable of scaling effectively in real-world applications.
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