**Global**: Gartner’s latest research forecasts that by 2027 enterprises will adopt specialised AI models three times more than general LLMs to improve accuracy, cost-efficiency, and responsiveness. Businesses must prepare data and invest in skills to capitalise on this tailored AI shift.
Recent research from Gartner Inc. highlights a significant shift in the use of artificial intelligence (AI) models within enterprises, indicating that by 2027, organisations will adopt small, task-specific AI models at a rate at least three times greater than general-purpose large language models (LLMs). This transition is primarily driven by the demand for AI solutions that offer greater contextual accuracy, cost-efficiency, and responsiveness tailored to specific business domains.
General-purpose LLMs are recognised for their robust language processing capabilities but tend to exhibit a decline in response accuracy when applied to tasks requiring specialised business knowledge. In contrast, smaller, task-specific AI models, designed to address particular functions or domain-specific data, provide enhanced precision. They also benefit from requiring less computing power and delivering faster responses, resulting in lower operational and maintenance costs for organisations.
The Gartner report further explains that businesses can leverage methods such as retrieval-augmented generation (RAG) and fine-tuning to develop customised LLMs aimed at specific tasks. A critical element in this process is enterprise data, which must undergo thorough preparation, quality assessments, version control, and administrative organisation to meet the fine-tuning requirements effectively.
Sumit Agarwal, Vice President and analyst at Gartner, emphasised the evolving attitude towards data utilisation within enterprises. Speaking to NDTV Profit, he stated, “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. This marks a shift from a protective approach to a more open and collaborative use of data and knowledge.”
The Gartner report advises enterprises contemplating the adoption of small, task-specific AI models to take several key steps:
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Pilot Contextualised Models: Enterprises should introduce small, contextualised models in areas where specific business context is critical, especially where general LLMs have not met expectations regarding response quality or speed.
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Adopt Composite Approaches: For use cases where a single AI model’s performance is insufficient, a composite approach involving multiple models and workflow stages should be considered to enhance overall effectiveness.
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Strengthen Data and Skills: Prioritising the preparation and organisation of relevant data is essential for successful model fine-tuning. Furthermore, investing in the upskilling of personnel—including AI and data architects, scientists, engineers, risk and compliance teams, procurement, and business subject matter experts—is crucial to drive AI initiatives effectively.
This evolving landscape suggests a future where enterprises increasingly rely on specialised AI to meet the diverse needs of corporate workflows while optimising operational efficiencies and leveraging their data assets more strategically.
Source: Noah Wire Services
