A recent survey of over 150 developers and technical leaders reveals a significant gap between AI experimentation and the development of production-ready systems, resulting in costly time and revenue losses due to AI system fragilities such as LLM inconsistencies and orchestration failures. While 49% of teams are still in the experimental phase, only 38% have achieved mature adoption of AI, with a mere 13% confident in observing and debugging AI workflows at scale. Enterprises need AI systems that run reliably, handle failures gracefully, maintain visibility, and scale predictably, yet current AI tools prioritize iteration speed over production durability. The concept of "Durable Execution" is highlighted as a missing element in today's AI frameworks, essential for ensuring robust orchestration, state tracking, failure recovery, and comprehensive visibility. Companies like Replit, ZoomInfo, and Gorgias have successfully transitioned to systems designed for production reliability, resulting in faster deployment and fewer failures. Looking ahead, 88% of respondents anticipate moderate to significant efficiency or revenue boosts from AI in the next 12-24 months, with a focus on improving reliability, scalability, and integration with existing systems to build lasting AI solutions.