A new forecast from Gartner reveals significant efficiency gains coming to generative AI development. By 2028, the research firm projects that 80% of commercial generative AI applications will be developed on existing data management platforms - a shift expected to slash delivery times by half.

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Currently, enterprises face challenges combining large language models (LLMs) with internal data while managing evolving technologies like vector search and prompt engineering. Without unified approaches, companies risk adopting fragmented solutions that extend timelines and increase costs.

At the recent Data and Analytics Summit in Mumbai, Gartner emphasized Retrieval-Augmented Generation (RAG) as a game-changing framework. This technology enhances model accuracy while providing flexible implementation options. "RAG delivers three critical benefits," explains Prasad Pore, senior Gartner analyst. "It offers adaptable deployment methods, improves explainability, and seamlessly integrates with LLMs."

The impact spans multiple business functions. Sales teams can automate processes more efficiently, HR departments streamline operations, and IT gains better data governance tools. Traditional data management often bogs down engineers with manual tasks - RAG promises to change that equation dramatically.

Static LLMs present another challenge addressed by RAG. These models operate only on their training data, lacking current information. RAG enables continuous updates with the latest organizational data, supercharging applications from customer support to business intelligence.

Gartner identifies three primary application categories benefiting from this evolution:

• Process automation: Corporate knowledge management and document processing
• User experience: Personalized shopping and automated customer support
• Insight generation: Conversational analytics and data discovery tools

For enterprises planning their AI strategy, Gartner recommends three crucial steps:

1. Assess existing platforms for RAG-as-a-service potential
2. Prioritize integration of vector search and metadata technologies
3. Leverage operational data to protect intellectual property while addressing privacy concerns

The coming wave of platform-based development could fundamentally reshape how businesses deploy AI solutions. With major efficiency gains on the horizon, organizations that adapt quickly may gain significant competitive advantages.

Key Points

1. Development time for generative AI applications is projected to decrease by 50% through platform-based approaches
2. Retrieval-Augmented Generation (RAG) addresses critical challenges around accuracy and current information access
3. Enterprises should evaluate their existing infrastructure for RAG integration potential
4. Three primary application categories will see the most immediate benefits: process automation, user experience enhancement, and insight generation