80% of AI projects never deliver meaningful business outcomes" (Gartner, 2023). Yet, software engineers and quality experts are flocking to AI roles, driven by fear of missing out (FOMO), leaving traditional enterprise software teams stretched thin. The migration of top talent into AI has left traditional software delivery teams in a precarious state. Engineering velocity slows, quality takes a hit, and enterprise customers are frustrated with delayed features and increasing bugs. Meanwhile, AI teams are often struggling to define clear paths to profitability, compounding the issue: FOMO is driving talent in droves to AI roles, even as the sector struggles to justify its investments. The allure of AI is hard to resist—media hype, career growth prospects, and promises of transformative innovation all play their part. However, enterprise software companies, which still generate the bulk of industry revenue, often fail to invest in making traditional software roles equally engaging. The result? Top talent opts for what appears more exciting, leaving traditional teams to grapple with understaffing and diminishing morale. This session bridges the gap between the hype and reality. Drawing on my experience in engineering management, software delivery, and AI initiatives, I will unpack why the current talent migration hurts both AI and enterprise software. Attendees will walk away with actionable strategies to: Retain top engineering and QE talent in enterprise software teams. Cultivate meaningful AI collaboration without losing focus on revenue-generating projects. Build a future-ready workforce that thrives in both AI and traditional domains.