Exploring the Challenges of Generative AI Adoption
With nearly all organizations acknowledging Generative AI (GenAI) as a pivotal focus, only 44% have established comprehensive governance policies to guide its implementation. Security concerns, infrastructure limitations, and effective data management stand out as significant hurdles preventing the widespread adoption of this transformative technology. This insight emerges from a joint research effort by the Enterprise Strategy Group (ESG) and Hitachi Vantara, which surveyed around 800 IT and business leaders across North America and Western Europe.
The findings underscore a tangible disconnect between enthusiasm for GenAI and the preparedness to deploy it effectively. A staggering 97% of participants with ongoing GenAI projects indicated it’s among their top five priorities, with U.S. organizations being noticeably more likely to rank it as their foremost initiative compared to their European counterparts. Visit Hitachi Vantara for in-depth findings from the report.
Use Cases and Implementation Gaps
Almost two-thirds of the responding organizations reported having identified at least one use case for GenAI. Yet, the majority face significant risks that could derail their projects. In particular, less than half (44%) of organizations possess well-defined governance policies for GenAI, which raises concerns about compliance and data privacy during implementation. Moreover, only about 37% believe their infrastructure is adequately prepared for GenAI, leaving many to contend with a substantial readiness gap.
The survey also highlighted a knowledge gap within organizations. While 61% of respondents felt that most users lack the ability to leverage GenAI effectively, over half (51%) admitted to a shortage of skilled personnel with the requisite GenAI expertise. Furthermore, 40% conceded they are not sufficiently informed about the planning and execution aspects of GenAI initiatives.
The Importance of Robust Infrastructure
According to Ayman Abouelwafa, chief technology officer at Hitachi Vantara, the foundation necessary for successful GenAI implementation is still under construction. “Unlocking the true power of GenAI requires a robust and secure infrastructure that can meet the demands of this powerful technology,” he stated.
As organizations pursue lower-cost infrastructure alternatives, factors like privacy and latency remain critical in decision-making. Seventy-one percent of respondents believed their infrastructure needs modernization ahead of GenAI initiatives. Interestingly, while 96% favor non-proprietary models, there is a future expectation of a six-fold increase in proprietary model usage. This shift may occur as businesses gain expertise and seek competitive differentiation in their GenAI applications.
Driving Forces and Concerns Surrounding GenAI
The report outlines several key drivers for companies investing in GenAI, with prevalent use cases including process automation and optimization (37%), predictive analytics (36%), and fraud detection (35%). Operational efficiency improvements emerged as the primary area where businesses anticipate positive outcomes from GenAI adoption. However, only 43% reported realizing tangible benefits thus far.
Conversely, significant concerns remain prevalent among organizations. Over 81% of respondents highlighted the importance of ensuring data privacy and compliance when developing GenAI applications, while 77% stressed the need to address data quality issues before placing trust in GenAI outputs. These findings underscore a cautious approach towards leveraging GenAI capabilities in a secure and efficient manner.
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