K2view Finds that Just 2% of U.S. and UK Businesses are Ready for GenAI Deployment

PALO ALTO, CA – October 23, 2024 – K2view, a global leader in data management for the AI era, today released the results of its enterprise data readiness for GenAI in 2024 survey, which examines the top challenges enterprises are facing as they work toward generative AI (GenAI) implementation. The report unveils the most significant roadblocks to realizing GenAI’s full potential lie in organizations’ existing data infrastructure, particularly in the areas of data accessibility and latency, data privacy, and security.

2023 was the year the world discovered GenAI, and 2024 is the year organizations started to put it to use. However, businesses realize that returns on GenAI investments are taking much longer than previously anticipated. Many companies even abandon projects after the proof-of-concept stage due to inadequate data guardrails, a lack of real-time access to fresh, multi-source data, and escalating costs, underscoring the critical role of data in the success of GenAI initiatives.

“Data is critical to realizing AI’s true potential, but organizations are struggling to close the GenAI data gap and move their GenAI projects to production,” says Ronen Schwartz, CEO of K2view. “We conducted this survey to get to the heart of the issues that business and IT leaders are running into when implementing GenAI, and that prevent them from delivering sustained business value from GenAI production deployments.”  

Key findings include: 

• Most organizations are moving beyond GenAI exploration to pilots, but not to production. While 43% of businesses are involved in department-level pilots, only 13% are conducting organization-wide data-driven GenAI pilots. And only 2% of respondents, have advanced to production deployment, even lower than recently reported by industry analysts.

• Enterprise data is at the crux of the biggest challenges for AI deployment. 48% of respondents cite data security and privacy concerns, and 33% cite enterprise data readiness as roadblocks to deployment. The difficulty lies in the fragmented nature of enterprise data, which is often spread across multiple systems and analytical data stores, making it hard to integrate, govern, and make accessible in near-real time, under stringent guardrails, to GenAI applications. 

• Retrieval Augmented Generation (RAG) is widely adopted as a framework for grounding LLMs with internal data, but enterprise application data is largely underutilized in initial RAG implementations. 60% of respondents are already piloting RAG, while 24% are in the planning stages, and 12% have moved to full production. The survey reveals a unique set of challenges specific to enterprise application data, which hinders its use in making GenAI and RAG initiatives successful, even when the data is stored in data lakes and data warehouses.

• 54% of respondents say customer operations is a crucial GenAI use case. Organizations across industries recognize the value of GenAI in improving customer engagement and satisfaction, reducing response times, improving first contact resolution, and increasing cross and upselling.