A new forecast from Gartner reveals that enterprises could slash generative AI application development time by 50% within four years. The research firm projects that 80% of commercial generative AI solutions will be built on existing data management platforms by 2028, dramatically reducing complexity and accelerating deployment.

Current generative AI development often involves piecing together large language models (LLMs) with internal enterprise data, vector search systems, and evolving technologies like metadata management and prompt engineering. This fragmented approach frequently leads to extended timelines and ballooning costs.

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At the recent Data and Analytics Summit in Mumbai, Gartner emphasized Retrieval-Augmented Generation (RAG) as the emerging standard for developing reliable generative AI applications. This framework enhances model accuracy while providing flexible implementation options and improved explainability when combined with LLMs.

"RAG transforms how businesses automate processes across sales, HR, IT, and data management," explained Prasad Pore, senior Gartner analyst. Traditional data pipelines demand excessive manual effort, but RAG-based solutions can streamline governance while boosting productivity.

The static nature of current LLMs presents another challenge—these models operate only on their training data without access to current information. RAG solves this by allowing companies to inject real-time business data into the system, dramatically improving performance for tasks like query responses, log analysis, and decision support.

Gartner identifies three primary categories for commercial generative AI applications:

• Process automation: Including corporate knowledge management and document processing
• User experience: Such as automated customer support and personalized shopping
• Insights generation: Covering conversational business intelligence and data discovery

For enterprises planning their AI strategy, Gartner recommends:

1. Assessing existing platforms for potential conversion to RAG-as-a-service solutions
2. Prioritizing integration of vector search, graph technology, and chunking capabilities from current systems
3. Leveraging metadata to protect intellectual property while addressing privacy concerns

The shift toward platform-based development promises to democratize generative AI implementation while reducing technical barriers. As organizations race to harness this transformative technology, those adopting structured approaches like RAG may gain significant competitive advantages.

Key Points

1. Development time for generative AI applications could drop by half through platform-based approaches
2. RAG technology enhances model accuracy while providing crucial flexibility for businesses
3. Enterprises should evaluate existing infrastructure for potential RAG integration opportunities