Gartner Predicts 80% of GenAI Business Apps Will Be Developed on Existing Data Management Platforms

Gartner predicts that organizations will develop 80% of Generative AI (GenAI) business applications on their existing data management platforms by 2028. This approach will reduce the complexity and time required to deliver these applications by 50%, according to the market research organization.

“Building GenAI business applications today involves integrating large language models (LLMs) with an organization’s internal data and adopting rapidly evolving technologies like vector search, metadata management, prompt design and embedding,” said Prasad Pore, Sr Director Analyst at Gartner. “However, without a unified management approach, adopting these scattered technologies leads to longer delivery times and potential sunk costs for organizations.”

Retrieval-augmented generation (RAG) is becoming a cornerstone for deploying GenAI applications, providing implementation flexibility, enhanced explainability and composability with LLMs, according to Gartner. By integrating data from both traditional and non-traditional sources as context, RAG enriches the LLM to support downstream GenAI systems.

“Most LLMs are trained on publicly available data and are not highly effective on their own at solving specific business challenges,” said Pore. “However, when these LLMs are combined with business-owned datasets using the RAG architectural pattern, their accuracy is significantly enhanced. Semantics, particularly metadata, play a crucial role in this process. Data catalogs can help capture this semantic information, enriching knowledge bases and ensuring the right context and traceability for data used in RAG solutions.”

Gartner recommends that channel partners and customers evaluate whether current data management platforms can be transformed into a RAG-as-a-service platform, replacing stand-alone document/data stores as the knowledge source for business GenAI applications. They should further evaluate and integrate RAG technologies such as vector search, graph and chunking, from existing data management solutions or their ecosystem partners when building GenAI applications. Enterprises should also leverage not only technical metadata, but also operational metadata generated at runtime in data management platforms to help protect GenAI applications from malicious use, privacy issues and intellectual property leaks.

Gartner clients can learn more in “Predicts 2025: 4 Ways AI Will Disrupt Data Management Markets and Solutions.”

Channel Impact^®
As organizations develop GenAI-centric solutions, data management platforms must evolve to integrate new capabilities or services for GenAI development, ensuring AI readiness and successful implementation.