RAG structured data is structured data retrieved from your enterprise systems and augmented into your LLM for more accurate and context-aware responses.
RAG structured data challenges
Now you can integrate your structured data into your enterprise LLM (Large Language Model) with generative AI (GenAI) frameworks like advanced Retrieval-Augmented Generation (RAG). Although challenges exist, injecting your private, trusted data into your LLM is essential to ensuring GenAI success. This article discusses the aspect of RAG for structured data from our recent survey on enterprise data readiness for GenAI.
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As organizations rush to implement GenAI solutions, a critical challenge is emerging – leveraging your structured enterprise data effectively.
While much attention has been focused on LLMs and their capabilities, the success of enterprise AI initiatives increasingly depends on the role structured data plays in active retrieval-augmented generation.
The current state of RAG adoption
According to our recent survey of 300 senior professionals involved in GenAI initiatives in their organizations, RAG structured data implementation has gained significant traction.
Today, 60% of organizations are actively piloting the technology, while 24% are in the planning stages, and 12% have moved to production.
Status of RAG adoption
The survey also revealed which industries have embraced RAG the most. Health and pharma demonstrated the highest rate of piloting the technology (75%), followed by financial services (61%) and retail and telecommunications (52%).
These adoption rates reflect the recognition that generic LLMs alone are insufficient for enterprise needs since only 14% of organizations use generic LLMs.
Top challenges in deploying GenAI applications
Cost is the biggest obstacle in GenAI deployment, closely followed by data security and privacy concerns, and the reliability of LLM responses. Data readiness for GenAI, indicated by 33% of the respondents, is another significant hurdle.
Top challenges in deploying GenAI applications
3 of the top 4 challenges relate directly to the LLM’s ability to use enterprise structured data. Why is that? Because enterprise data is scattered across disparate systems and formats, creating barriers to integration, governance, and real-time accessibility.
The primary stores for customer data are CRM, customer support, and finance systems, in addition to campaign management tools and ERPs, according to the survey.
Such fragmentation creates significant obstacles for organizations attempting to leverage enterprise data. For example, to effectively integrate your customer data in your GenAI apps, you must make sure it’s unified from each of these data stores and then governed for secure use.
Key obstacles in leveraging RAG structured data
The survey identified a unique set of challenges in RAG adoption, specific to enterprise data. With scalability and performance at the top, other significant milestones include assuring AI data quality and consistency, real-time data integration and access, governance, compliance, and security.
Top concerns about RAG structured data
With the top challenges all relatively close percentage-wise, it’s clear that focusing on just one or two of these issues is not enough to enable effective generative AI adoption. Instead, a holistic approach that would address each of these core challenges is necessary.
Maximizing data readiness for RAG structured data
Organizations are leveraging diverse data sources for their RAG GenAI implementations, combining traditional structured sources like operational systems and data warehouses with newer technologies such as vector and graph databases.
This multi-source approach reflects the understanding that successful GenAI initiatives require a balanced data strategy. While operational systems lead adoption at 54%, the significant use of vector databases (44%) shows that organizations recognize the value of both structured and unstructured data sources.
The key to success lies not in choosing one type over another, but in developing an integrated strategy that maximizes the unique advantages of each data source according to the specific use case.
The road ahead for RAG and structured data
As organizations progress in their GenAI and RAG implementations, the focus must shift from simple pilot projects to comprehensive data strategies that support production-scale deployments.
Success requires a holistic approach that balances rapid data access with security, quality, and governance requirements. Organizations are tasked with developing robust integration capabilities, implementing effective governance frameworks, and maintaining security and compliance standards while ensuring scalability.
The survey shows that organizations that can effectively address these challenges while building comprehensive data infrastructure will be best positioned to leverage RAG structured data and gain a competitive advantage. This means investing in both technical capabilities and organizational processes to make enterprise data truly AI-ready, ultimately transforming pilot projects into sustainable, production-grade implementations.
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