Enterprise AI faces runtime challenges, not model issues

A new survey reveals that enterprise AI adoption is hindered by runtime infrastructure problems rather than limitations in AI models themselves. Organizations struggle with stateless architectures that fail to maintain context during restarts, leading to compounded errors and increased costs. Engineering teams are dedicating significant resources to managing this 'plumbing' instead of developing core AI intelligence. The research highlights a critical need for durable execution frameworks, with many companies migrating towards more resilient systems to overcome these operational realities and avoid the pitfalls that plagued earlier automation technologies.