Most AI initiatives fail not because the technology does not work, but because the organization misaligns architecture, economics, and business value. This article, part of the AppScale Executive AI Series, examines the six most common failure patterns — unrealistic ROI timelines, infrastructure cost explosion, poor data readiness, organizational resistance, accuracy-to-value mismatch, and pilot scaling without operational foundations. It provides enterprise architecture context, early warning signals for leadership to monitor, and a practical recovery framework for organizations whose AI programs have lost momentum. The central argument: AI transformation requires patience, architectural discipline, and a long-term platform mindset — and the organizations that succeed are those that detect failure early and treat AI as a durable organizational capability, not a short-term experiment.