Bridging the Hype and Reality of Generative AI: Insights from Real-World Enterprise Implementations

Generative AI (GenAI) has rapidly gained traction as a transformative technology, promising to revolutionize business processes across various sectors. However, the gap between theoretical potential and real-world implementation remains substantial, with many enterprises struggling to transition from experimental pilots to scalable applications. This study critically examines 31 GenAI projects from different industries evaluating the strategic, technical and operational factors that influence their success or stagnation. To ensure a methodologically rigorous and comparable analysis, we adopt a mixed-methods approach that integrates primary data—collected through direct project involvement and stakeholder feedback— with secondary data from project documentation, industry reports, and academic research. Our case selection is based on practical exposure to enterprise GenAI implementations, focusing on projects where organizations have actively engaged in experimentation and deployment efforts. Rather than applying rigid selection criteria, we leverage first-hand access to real-world implementations to extract patterns, challenges, and success factors across different organizational contexts. This approach allows us to identify both industry-specific challenges and broader cross-sectoral trends in GenAI adoption. Our findings indicate that most GenAI projects remain stalled at the PoC phase, with organizations facing significant obstacles in scalability, predictability, and enterprise-wide integration. The primary barriers include: (1) Unpredictability of AI outputs, limiting reliability in mission-critical applications; (2) Regulatory, ethical, and legal concerns, particularly in relation to data privacy, intellectual property, and compliance with evolving AI governance frameworks. (3) Fragmented AI strategies within enterprises, leading to siloed innovation efforts and a lack of alignment between technical development and business objectives. Despite these challenges, early indicators of business value are emerging, particularly in process automation, knowledge augmentation, and customer interaction enhancement. However, without robust governance structures, clear strategic alignment, and adaptive technical strategies, enterprises risk failing to realize the long-term benefits of GenAI. This research contributes to both academic and industry discourse by providing a nuanced, multi-sector examination of GenAI implementation. By identifying critical success factors and common failure points, we offer actionable insights for enterprises aiming to transition from experimentation to sustainable deployment. The study ultimately highlights the conditions under which GenAI can move beyond hype to deliver tangible enterprise value, emphasizing the importance of human-AI collaboration, regulatory foresight, and business-driven AI adoption strategies.