Report Highlights Challenges in Generative AI Adoption for Enterprises

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DateJul 11, 2024

Despite the growing excitement surrounding Generative AI (GenAI), a recent co-sponsored research report from Enterprise Strategy Group (ESG) and Hitachi Vantarareveals significant challenges that could hinder the success of GenAI projects. Surveying 800 IT and business leaders across the United States, Canada, and Western Europe, the report highlights the critical role of data infrastructure in the successful implementation of enterprise GenAI.

The study found that 97% of organizations with ongoing GenAI projects view it as a top-five priority. U.S. companies are 35% more likely to rank GenAI as their top priority compared to their European counterparts. Despite this enthusiasm, several key obstacles pose serious risks to GenAI initiatives.

A major concern is the lack of well-defined and comprehensive policies regarding GenAI, with less than half (44%) of organizations having such frameworks in place. Additionally, only 37% of respondents believe their infrastructure and data ecosystem are well-prepared for GenAI implementation. This figure rises to 48% among C-level executives, indicating a disconnect between executive perception and operational reality.

The report also reveals that 61% of respondents believe most users do not know how to capitalize on GenAI, and 51% report a shortage of skilled employees with GenAI expertise. Furthermore, 40% of respondents feel they are not well-informed about the planning and execution of GenAI projects.

“Enterprises are clearly jumping on the GenAI bandwagon, but it’s also clear that the foundation for successful GenAI is not yet fully built,” said Ayman Abouelwafa, Chief Technology Officer at Hitachi Vantara. “Unlocking the true power of GenAI requires a robust and secure infrastructure that can handle the demands of this powerful technology.”

Top Concerns: Data privacy and Compliance

The report would underscore the need for modernized infrastructure, with 71% of respondents agreeing that their current infrastructure needs upgrading before pursuing GenAI projects. Additionally, 96% prefer non-proprietary models, 86% plan to leverage Retrieval-Augmented Generation (RAG), and 78% favor a mix of on-premises and public cloud solutions for building and using GenAI.

Over the long term, organizations would expect the use of proprietary models to increase six-fold as they gain expertise and seek competitive differentiation. “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,” said Mike Leone, Principal Analyst at Enterprise Strategy Group. “Managing data with the right infrastructure will not only enable greater levels of accuracy but also improve reliability as data and business conditions evolve.”

The report identifies key drivers and barriers to GenAI adoption. Leading use cases include process automation and optimization (37%), predictive analytics (36%), and fraud detection (35%). Improving operational efficiency is the most cited area where businesses are seeing results, yet only 43% have realized benefits so far.

Data privacy and compliance remain top concerns, with 81% of respondents worried about these issues when building and using GenAI applications. Additionally, 77% agree that data quality issues need addressing before fully accepting GenAI outputs.

In response to these challenges, Hitachi Vantara is developing AI solutions tailored to modern enterprise needs. The company recently introduced Hitachi iQ, an industry-optimized solution suite for AI workloads that layers industry-specific capabilities on top of the AI solution stack. Complementing Hitachi iQ, Hitachi’s Center for Excellence (COE) for generative AI supports customers on their accelerated journeys, helping to manage risks and fast-track their paths to becoming AI leaders and future market leaders.

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