Small but mighty: Why specialised language models are driving the future of enterprise AI

By Ashok Panda, Vice President and Global Head – AI & Automation Services, Infosys

In the evolving landscape of artificial intelligence, large language models (LLMs) often grab headlines for their impressive capabilities and scale. But in the enterprise world, where data privacy, performance efficiency, sustainability, and domain specificity matter, a quieter revolution is underway. Small language models (SLMs) are emerging as powerful, practical alternatives, purpose-built for the next wave of enterprise AI.

SLMs excel at specific tasks with high precision. They are optimised for efficiency and consume fewer computational and data resources.

Purpose-Built for Precision

What sets SLMs apart is their ability to focus. While LLMs offer general intelligence across broad topics, SLMs are tailored for specific domains and workflows. This makes them ideal for enterprises looking to embed intelligence into business processes with high accuracy and explainability.

Research and field implementations consistently show that smaller models, when fine-tuned correctly, can match or even surpass the performance of much larger models in well-defined enterprise use cases.

Enterprise-Grade Efficiency

SLMs offer significant operational advantages. They run efficiently on commodity hardware, including edge devices without the need for specialised GPUs or massive cloud infrastructure. This enables enterprises to deploy AI capabilities directly on-premises, in remote locations, or within private clouds, reducing both latency and data exposure risks.

SLMs also lend themselves well to a digital model architecture where models evolve in a parent-child hierarchy. Similar to the concept seen in platforms like DeepSeek, a base (parent) model can spawn multiple specialised (child) SLMs, each fine-tuned for a specific purpose or domain.

This approach ensures consistency across the enterprise while allowing localised optimisation. It also supports incremental training allowing child models to adapt and learn independently while retaining core capabilities from the parent. This modular lineage supports explainability, traceability, and more efficient lifecycle management.

Sustainable by Design

Training and running LLMs requires significant energy, often resulting in carbon emissions comparable to entire vehicle fleets. SLMs, by contrast, consume a fraction of the resources.

This alignment with sustainability goals is increasingly important as businesses look to reduce the carbon footprint of their digital initiatives without compromising on innovation.

What is a hybrid AI model and why is it better suited for solving enterprise-scale challenges?

A hybrid AI model strategically combines multiple AI approaches traditional machine learning (ML), large language models (LLMs), small language models (SLMs), distilled models, and domain specific fine-tuned models to tackle complex business problems more effectively than any single solution.

Model-as-a-Service: Scalable AI Delivery

To further accelerate adoption, enterprises are embracing Model-as-a-Service (MaaS) paradigm. MaaS employs a comprehensive hybrid AI model strategy, integrating proprietary small language models with established large language models and traditional ML techniques for reliability, cost, and data compliance to help enterprises scale AI.

The transition to agentic AI systems capable of independent planning, execution, and adaptation requires a foundation of reliable, interconnected AI components. MaaS provides this foundation by creating robust systems that can handle the multi-step reasoning and diverse data processing autonomous agents demand.

Conclusion

In the enterprise context where we are seeing increasing adoption of autonomous agents, a hybrid AI model that strategically combines multiple AI approaches traditional machine learning (ML), large language models (LLMs), small language models (SLMs), distilled models, and domain specific fine-tuned models to tackle complex business problems more effectively than any single solution.

This approach is like assembling a specialised team, rather than relying on one individual for everything.