By 2028, 80% of GenAI Business Apps Will Be Built on Existing Data Management Platforms
According to leading research firm Gartner Inc., by 2028, a staggering 80% of Generative AI (GenAI) business applications will be developed on existing data management platforms, dramatically cutting down delivery timelines and complexity by up to 50%.
The forecast was presented at the ongoing Gartner Data & Analytics Summit in Mumbai, where Prasad Pore, Senior Director Analyst at Gartner, emphasized the strategic importance of unifying GenAI capabilities within enterprise data platforms.
“Building GenAI applications currently involves stitching together large language models (LLMs), vector databases, metadata frameworks, and embedding techniques. Without an integrated approach, organisations risk longer development cycles and sunk investments,” Pore noted.
Why Data Management Platforms Are the Future of GenAI Development?
As businesses push to integrate GenAI into operations, data management platforms will need to evolve rapidly to support:
- Vector search engines
- Metadata management
- Prompt engineering
- RAG (Retrieval-Augmented Generation) architecture
These platforms will become AI-ready backbones, enabling secure, accurate, and scalable deployment of GenAI tools.
RAG: The Foundation of Next-Gen GenAI Applications
Gartner highlighted the rising importance of Retrieval-Augmented Generation (RAG) as a foundational architectural pattern. RAG allows LLMs to access real-time or proprietary datasets, thus enhancing the relevance and accuracy of AI-generated outputs.
“Most LLMs are trained on publicly available data and lack business-specific context,” said Pore. “RAG bridges this gap by injecting enterprise-owned data into the LLM pipeline, delivering better accuracy and business alignment.”
He stressed the critical role of semantics and metadata, particularly data catalogs, in enriching knowledge graphs and ensuring traceability and explainability.
Key Recommendations for Enterprises Building GenAI Apps:
Gartner’s experts advise businesses to take the following strategic actions to future-proof their GenAI initiatives:
Transform Data Platforms into RAG-as-a-Service Hubs:
Repurpose existing data infrastructure to act as centralized RAG delivery engines, replacing fragmented knowledge stores with unified, traceable sources.
Adopt Native RAG Technologies:
Leverage vector search, graph technologies, and data chunking—either natively within platforms or via ecosystem partners—for building scalable, GenAI-ready solutions.
Incorporate both technical and runtime operational metadata to safeguard GenAI applications against Data privacy breaches, IP leakage, Malicious exploitation.