Deploying Generative AI (GenAI) at an enterprise scale presents various challenges and opportunities. To successfully deploy GenAI, businesses need to consider performance optimization, cost management, model selection, and robust evaluation frameworks. Traditional metrics are no longer sufficient for large language models, and a shift in developer mindset is required to fully harness AI's potential. A strong evaluation framework impacts both performance and cost management, ensuring that AI systems are reliable, perform well, and stay secure and compliant. Businesses need to address the maturity of GenAI tools, cost, and security challenges head-on, and leverage synthetic data for testing and refining AI models without interfering with live systems. Successful GenAI integration requires a gradual, step-by-step approach, making data-driven decisions, following best practices, and understanding different strategies for optimizing LLM performance. The future of GenAI in enterprises is promising, with advancements set to transform how AI integrates into business operations, including improved reasoning abilities and cost reduction.