{"id":23499,"date":"2025-05-07T17:00:07","date_gmt":"2025-05-07T17:00:07","guid":{"rendered":"https:\/\/www.insurance-canada.ca\/?p=83005"},"modified":"2025-05-07T17:00:07","modified_gmt":"2025-05-07T17:00:07","slug":"gartner-predicts-organizations-will-use-small-task-specific-ai-models-three-times-more-than-general-purpose-large-language-models-by-2027","status":"publish","type":"post","link":"https:\/\/blog.lifeinsurance-orleans.ca\/index.php\/2025\/05\/07\/gartner-predicts-organizations-will-use-small-task-specific-ai-models-three-times-more-than-general-purpose-large-language-models-by-2027\/","title":{"rendered":"Gartner Predicts Organizations Will Use Small, Task-Specific AI Models Three Times More Than General-Purpose Large Language Models by 2027"},"content":{"rendered":"

Need for Contextualized, Reliable and Cost-Effective Solutions Is Driving the Shift Toward Small Task-Specific AI Models<\/h3>\n

San Diego, CA (May 1, 2025) \u2013 Gartner, Inc. predicts that by 2027, organizations will implement small, task-specific AI models<\/a>, with usage volume at least three times more than those of general-purpose large language models (LLMs).<\/p>\n

While general-purpose LLMs provide robust language capabilities, their response accuracy declines for tasks requiring specific business domain context.<\/p>\n

\u201cThe variety of tasks in business workflows and the need for greater accuracy are driving the shift towards specialized models fine-tuned on specific functions or domain data,\u201d said Sumit Agarwal<\/a>, VP Analyst at Gartner. \u201cThese smaller, task-specific models provide quicker responses and use less computational power, reducing operational and maintenance costs.\u201d<\/p>\n

Enterprises can customize LLMs for specific tasks by employing retrieval-augmented generation (RAG) or fine-tuning techniques to create specialized models. In this process, enterprise data<\/a> becomes a key differentiator, necessitating data preparation, quality checks, versioning and overall management to ensure relevant data is structured to meet the fine-tuning requirements.<\/p>\n

\u201cAs enterprises increasingly recognize the value of their private data and insights derived from their specialized processes, they are likely to begin monetizing their models and offering access to these resources to a broader audience, including their customers and even competitors,\u201d said Agarwal. \u201cThis marks a shift from a protective approach to a more open and collaborative use of data and knowledge.\u201d<\/p>\n

By commercializing their proprietary models, enterprises can create new revenue streams while simultaneously fostering a more interconnected ecosystem.<\/p>\n

Implementing Small Task-Specific AI models<\/strong><\/p>\n

Enterprises looking to implement small task-specific AI models must consider the following recommendations:<\/p>\n