Governance is crucial for pushing AI development forward, ensuring accuracy, reliability, and meeting essential standards. Regulatory measures are tightening, requiring businesses to govern their data and processes carefully amidst increasing regulatory challenges. Effective governance involves managing data lineage, tracing data origins, and maintaining compliance with evolving regulatory standards. Trustworthiness in AI comes from thorough evaluation and real-time monitoring, leveraging function calling to create modular systems that are easier to optimize and fine-tune. Industry-specific solutions often involve integrating domain-specific tools and models, ensuring AI systems are accurate and comply with legal and regulatory requirements. To achieve production-grade AI, companies must invest in both technology and operations, focusing on key performance metrics and adopting monitoring best practices. The industry is expected to move towards "evaluation-driven development," where best practices in AI system evaluation are crucial, and by 2025, companies should expect to have clearer strategies to align AI systems with their specific goals, significantly improving their ROI.