By 2027, Organizations Will Turn to Small, Task-Specific AI Models

Gartner predicts that by 2027, organizations will implement small, task-specific AI models, with usage volume at least three times more than that of general-purpose large language models (LLMs). While general-purpose LLMs provide robust language capabilities, their response accuracy declines for tasks requiring specific business domain context.

“The 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,” said Sumit Agarwal, VP Analyst at Gartner. “These smaller, task-specific models provide quicker responses and use less computational power, reducing operational and maintenance costs.”

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 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.

“As 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,” said Agarwal. “This marks a shift from a protective approach to a more open and collaborative use of data and knowledge.”

By commercializing their proprietary models, enterprises can create new revenue streams while simultaneously fostering a more interconnected ecosystem. Those looking to implement small task-specific AI models must consider piloting contextualized models, adopting composite approaches, and strengthening data and skills.