Corporates do not rely on a single AI model but rather opt for hybrid multi-paradigm architectures, called Poly AI. This evolution lays the foundation for agentic AI.
A hybrid AI model combines multiple AI approaches — traditional ML, LLMs, SLMs, distilled models and domain specific fine-tuned models. Such a variety enables the corporate to tackle complex business problems more effectively than any single model. It consists of assembling a specialised team.
Such an approach is necessary since business deals with diverse types of data and needs at the same time. A hybrid system leverages ML for structured financial data analysis, LLMs for complex reasoning tasks, SLMs for cost-efficient edge-processing, distilled models for faster versions and fine-tuned models for industry-specific terminology and workflows (healthcare diagnostics or legal document analysis). This combination delivers superior accuracy and reduces costs.
Infosys deploys small models for banking, IT Ops and cybersecurity. They use local models for on-premise data-processing. ML models are used for traditional data.
There is a transition to agentic AI systems. It is an inter-connected autonomous system. Hybrid AI lays the foundation for agentic AI. Agentic AI is being deployed across various industries in business, IT and operations. It leads to productivity and better customer experience.
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