80% of GenAI Business Apps on Existing Data Platforms by 2028

- Prasad Pore, Sr Director Analyst at Gartner

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%. 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. However, without a unified management approach, adopting these scattered technologies leads to longer delivery times and potential sunk costs for organizations.

Future-Ready Platforms Must Support GenAI

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

RAG: The Core Enabler of Intelligent AI Applications

Retrieval-augmented generation (RAG) is becoming a cornerstone for deploying GenAI applications, providing implementation flexibility, enhanced explainability and composability with LLMs. 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. 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 catalogues can help capture this semantic information, enriching knowledge bases and ensuring the right context and traceability for data used in RAG solutions.

Creating an Infrastructure for Sustainable GenAI Success

To effectively navigate the complexities of GenAI application deployment, enterprises should consider these key recommendations:

> Evolve Data Management Platforms: 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.

> Prioritize RAG Technologies: 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. These options are more resilient to technological disruptions and compatible with organizational data.

Metadata: The Silent Guardian of GenAI Integrity

As GenAI systems grow in complexity and reach, protecting them from ethical, legal, and operational risks becomes paramount. Here, metadata—both technical and operational—plays a protective role. Metadata generated during real-time operations can help identify misuse, ensure privacy compliance, and guard against unauthorized access to intellectual property. Properly managing this metadata adds a layer of governance that strengthens the trustworthiness of GenAI deployments.