Gartner sees that the use of small, task-specific artificial intelligence (AI) models will be at least three times greater than that of general-purpose large language models (LLMs) in organisations by 2027. This expected shift is being driven by the increasing demand for contextualised, reliable and cost-effective solutions in business operations.
General-purpose LLMs are currently valued for their broad language capabilities. However, their performance tends to decline when used for tasks that require an in-depth understanding of a particular business domain. According to Gartner, this limitation is prompting organisations to focus on specialised models tailored to specific business functions.
“The variety of tasks in business workflows and the need for greater accuracy are driving the shift towards specialised 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.”
Gartner explained that enterprises can develop these models using approaches such as retrieval-augmented generation (RAG) or fine-tuning techniques. These methods allow companies to adapt existing LLMs to meet their own specific needs, using enterprise data as the core differentiator. In doing so, businesses must focus on preparing and managing data properly—this includes quality checks, version control, and ensuring the data is structured to support the fine-tuning process effectively.
“As enterprises increasingly recognise the value of their private data and insights derived from their specialised processes, they are likely to begin monetising 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.”
This change in outlook presents a significant opportunity for organisations to create new revenue streams by commercialising their proprietary models. Gartner believes this will help foster a more interconnected and collaborative ecosystem in the AI space.
To effectively implement small, task-specific AI models, Gartner recommends that organisations take a strategic approach. One of the first steps is to pilot contextualised models in areas where the accuracy and speed of general-purpose LLMs have not been satisfactory.
Further, Gartner advises organisations to strengthen their data foundations and develop relevant skills across departments. This includes collecting, curating, and organising data for fine-tuning models, while also upskilling technical and functional teams.
