While the excitement around Generative AI (GenAI) continues to grow, significant challenges are surfacing that may hinder the success of these projects, according to a newly released research report from Enterprise Strategy Group (ESG) and Hitachi Vantara. The report, which surveyed 800 IT and business leaders across the United States, Canada, and Western Europe, underscores the critical importance of robust data infrastructure in the successful implementation of GenAI.
Key Findings and ChallengesThe survey reveals that 97% of organizations currently implementing GenAI view it as a top-five priority, with U.S. companies 35% more likely to consider it the top priority compared to their European counterparts. Despite this high level of interest, several risks threaten to derail these initiatives:
Less than half (44%) of organizations have established well-defined policies for GenAI.Only 37% believe their current infrastructure and data ecosystem is ready for GenAI solutions, although C-level executives are 1.3 times more likely to claim their infrastructure is highly prepared, indicating a disconnect within organizations.61% of respondents agreed that most users lack the know-how to leverage GenAI effectively, and 51% reported a shortage of skilled employees with GenAI expertise.40% acknowledged they are not well-informed about planning and executing GenAI projects.
Expert InsightsAyman Abouelwafa, chief technology officer at Hitachi Vantara, emphasized the necessity of a strong foundation for GenAI: “Enterprises are clearly jumping on the GenAI bandwagon, which is not surprising, but it’s also clear that the foundation for successful GenAI is not yet fully built to fit the purpose and its full potential cannot be realized. Unlocking the true power of GenAI, however, requires a strong foundation with a robust and secure infrastructure that can handle the demands of this powerful technology.”
Building the Foundation for GenAIThe report highlights the active pursuit of lower-cost infrastructure options by organizations, with privacy and latency also being top considerations. An overwhelming 71% of respondents agreed on the need for infrastructure modernization before pursuing GenAI projects. Furthermore, 96% of survey participants prefer non-proprietary models, 86% plan to leverage Retrieval-Augmented Generation (RAG), and 78% cite a combination of on-premises and public cloud solutions for GenAI development. Over time, however, the use of proprietary models is expected to increase six-fold as organizations seek competitive differentiation.
Mike Leone, principal analyst at Enterprise Strategy Group, noted the importance of data accuracy: “The need for improved accuracy shows organizations prioritizing the most relevant and recent data gets incorporated into a Large Language Model, followed by the desire to keep pace with technology, regulations, and shifting data patterns. Managing data with the right infrastructure will not only enable greater levels of accuracy but also improve reliability as data and business conditions evolve.”
Drivers and Barriers to AdoptionThe report identifies key drivers and barriers to GenAI adoption. Among the primary drivers are use cases like process automation and optimization (37%), predictive analytics (36%), and fraud detection (35%). These priorities align with the goal of improving operational efficiency, yet only 43% of organizations have seen tangible benefits thus far.Top concerns include ensuring data privacy and compliance (81%) and addressing data quality issues (77%) before trusting GenAI outputs. These challenges highlight the need for organizations to focus on robust data management and infrastructure modernization to realize the full potential of GenAI.
Challenges Threaten the Success of Generative AI Projects, Reveals New Report
