Gartner: Why Task-Specific AI Models will Overtake LLMs

Following the widespread adoption of general-purpose large language models (LLMs), like those from OpenAI, Anthropic and Cohere, businesses are now recognising the limitations these models present when handling industry-specific tasks requiring deeper domain knowledge.

Simultaneously, concerns are growing about computational costs, accuracy requirements and the need for models that can operate effectively within regulated environments.

Gartner has looked into these challenges – and found that organisations will implement small, task-specific AI models at a rate three times higher than general-purpose LLMs by 2027.

The benefits of small, task-specific AI Models

While general-purpose LLMs offer broad capabilities across multiple domains, they frequently lack the precision required for specialised business functions in sectors such as healthcare, financial services and manufacturing.

Recent implementations at firms including JP Morgan, which developed its own financial text analysis model and GlaxoSmithKline, which created specialised models for drug discovery, demonstrate the tangible benefits of domain-specific approaches.

These targeted models typically require fewer resources to run while delivering more reliable outputs for specific business processes.

“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,” says Sumit Agarwal, Vice President Research Analyst at Gartner.

“These smaller, task-specific models provide quicker responses and use less computational power, reducing operational and maintenance costs.”

How enterprise data monetisation creates new commercial opportunities for AI model deployment

The growing recognition of proprietary data value is changing how organisations approach their AI assets, with many expected to commercialise their models.

“As companies 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,” Sumit says. 

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

This commercialisation strategy enables enterprises to develop additional revenue streams while fostering a more interconnected business ecosystem.

Gartner’s implementation pathway for organisations adopting specialised AI models

According to Gartner, organisations seeking to deploy small, task-specific AI models must first pilot contextualised models in areas where business context is essential or where general-purpose LLMs have failed to deliver satisfactory response quality or speed.

Businesses should also identify use cases where single model orchestration proves inadequate and implement composite approaches involving multiple models and workflow steps.

Furthermore, data preparation efforts must be prioritised to collect, curate and organise information necessary for fine-tuning language models.

Concurrent investment in workforce development across technical and functional teams is essential, including AI and data architects, data scientists, engineers, risk and compliance personnel, procurement specialists and business subject matter experts.

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