Gartner Predicts Surge in Small Task-Specific AI Model Adoption by 2027

Gartner, Inc. projects that by 2027, small, task-specific artificial intelligence (AI) models will be used three times more than general-purpose large language models (LLMs) across organizations. The shift is driven by the need for AI systems that are more accurate, efficient, and cost-effective in business-specific tasks.

General-purpose LLMs, while powerful in broad language understanding, often fall short in domain-specific applications. “The variety of tasks in business workflows and the need for greater accuracy are driving the shift,” said Sumit Agarwal, VP Analyst at Gartner. “Smaller models can provide faster responses with lower operational and maintenance costs.”

These task-specific models are often built using techniques like retrieval-augmented generation (RAG) or fine-tuning with enterprise data, making data quality and management a strategic priority. Companies are expected to increasingly monetize their proprietary models and open them to external users, shifting from data protection to collaboration.

To implement these models effectively, Gartner recommends:
- Piloting models in high-context use cases.
- Using multiple models where single LLMs fall short.
- Investing in data preparation and workforce upskilling across technical and functional areas.

These insights are part of Gartner’s broader research in “Predicts 2025: AI-Powered Analytics Will Revolutionize Decision Making,” with further analysis to be presented at upcoming Data & Analytics Summits in São Paulo, London, Tokyo, Mumbai, and Sydney.