Enterprises struggling to implement GenAI

In spite of growing interest and enthusiasm for generative AI, significant challenges are emerging that threaten the success of projects, according to a new report.

The study, from Enterprise Strategy Group (ESG) and Hitachi Vantara, surveyed 800 IT and business leaders across the US, Canada, and Western Europe and finds only 44 percent of organizations have well-defined and comprehensive policies regarding GenAI.

In addition just 37 percent believe their infrastructure and data ecosystem is well-prepared for implementing GenAI solutions. However, C-level executives are 1.3 times more likely to indicate that their infrastructure and data ecosystem is highly prepared, highlighting a disconnect between levels of the organization.

The survey shows 61 percent of respondents agree that most users don't know how to capitalize on GenAI, with 51 percent reporting a lack of skilled employees with GenAI knowledge. Also 40 percent of respondents agree they themselves are not well-informed regarding planning and execution of GenAI projects.

"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," says Ayman Abouelwafa, chief technology officer at Hitachi Vantara. "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."

Among other findings 71 percent of respondents agree that their infrastructure needed to be modernized before pursuing GenAI projects. An overwhelming 96 percent of survey respondents prefer non-proprietary models, 86 percent will leverage Retrieval-Augmented Generation (RAG) and 78 percent prefer some mix of on-premises and public cloud for building and using GenAI solutions.

In terms of what's driving enterprise investment in GenAI, the most cited use cases center around process automation and optimization (37 percent), predictive analytics (36 percent), and fraud detection (35 percent). Operational efficiency is the area most named for where businesses are seeing results; however, less than half (43 percent) have realized benefits up to this point.

The top concerns and challenges being faced include concern around ensuring data privacy and compliance when building and using applications that leverage GenAI (81 percent), while 77 percent agree that data quality issues needed to be addressed before accepting the results of GenAI outputs.

"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," says Mike Leone, principal analyst at ESG. "Managing data with the right infrastructure will not only enable greater levels of accuracy, but also improve reliability as data and business conditions evolve."

You can read more on the Hitachi Vantara site.

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