According to McKinsey, despite widespread investment in AI, only 1% of companies claim to be mature on the deployment spectrum.

In the initial rush to adopt AI, large language models (LLMs) became the default choice. While LLMs accelerated experimentation, they also introduced challenges such as high computational costs, hallucinations, limited alignment with proprietary data, compliance risks, and complexities in production.

Although experimenting with LLMs is relatively easy, scaling them into production is hard. This is where small language models (SLMs) change the equation. Purpose-built for domains such as banking, healthcare, insurance, cybersecurity, and IT operations, SLMs reduce the burden of testing and validation and are more trustworthy for business-critical applications.

However, the real shift goes beyond LLMs or SLMs. It lies in adopting a platform-centric approach that orchestrates across models and adapts to rapid change, allowing enterprises to focus on outcomes rather than infrastructure.

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