Need for Contextualized, Reliable and Cost-Effective Solutions Is Driving the Shift Toward Small Task-Specific AI Models
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.”
“Small, task-specific models provide quicker responses and use less computational power, reducing operational and maintenance costs.”– Sumit Agarwal, Vice President Analyst
By commercializing their proprietary models, enterprises can create new revenue streams while simultaneously fostering a more interconnected ecosystem.
Implementing Small Task-Specific AI models
Enterprises looking to implement small task-specific AI models must consider the following recommendations:
- Pilot Contextualized Models: Implement small, contextualized models in areas where business context is crucial or where LLMs have not met response quality or speed expectations.
- Adopt Composite Approaches: Identify use cases where single model orchestration falls short, and instead, employ a composite approach involving multiple models and workflow steps.
- Strengthen Data and Skills: Prioritize data preparation efforts to collect, curate and organize the data necessary for fine-tuning language models. Simultaneously, invest in upskilling personnel across technical and functional groups such as AI and data architects, data scientists, AI and data engineers, risk and compliance teams, procurement teams and business subject matter experts, to effectively drive these initiatives.
