According to Gartner’s latest report, the adoption of small, task-specific artificial intelligence (AI) models is set to outpace general-purpose large language models (LLMs) by 2027, with usage expected to triple. This shift is driven by the growing demand for contextualized, reliable, and cost-effective solutions tailored to specific business functions.
While general-purpose LLMs are valued for their broad capabilities, their performance often declines in specialized domains. Task-specific models, on the other hand, offer quicker responses, greater accuracy, and reduced computational costs, making them ideal for enterprise workflows. Gartner highlights techniques such as retrieval-augmented generation (RAG) and fine-tuning as key methods for developing these specialized models.
The report also emphasizes the importance of data preparation and management, urging organizations to curate and structure their data effectively for fine-tuning processes. Gartner predicts that enterprises will increasingly monetize their proprietary models, fostering a collaborative ecosystem in the AI space.
This trend underscores the evolving landscape of AI, where precision and efficiency are becoming paramount in addressing complex business challenges. As organizations embrace task-specific models, the future of AI appears poised for transformative growth.
