AI tools often promise increased productivity for engineers, but their perceived time savings frequently do not align with actual efficiency gains due to systemic issues like inefficient workflows and organizational bottlenecks. The role of developers is evolving with AI's rise, as they are now expected to collaborate more with non-engineers and experiment with AI-generated prototypes. Organizations face pressure to adopt AI tools hastily, driven by fear of missing out, yet successful integration hinges on addressing specific problems rather than focusing on the tools themselves. While AI can abstract complexity, it introduces new layers of cognitive load and requires robust governance to manage potential errors. For AI to truly enhance developer experience, it must be integrated thoughtfully into a well-supported platform that focuses on reducing friction and inefficiencies that stem from flawed systems, rather than simply speeding up individual tasks.