'Go hybrid if you want accuracy and reliability'

As enterprises look to extract real, scalable value from their AI investments, the limitations of one-size-fits-all models are becoming clear.

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Companies are evolving from single-model AI deployments to hybrid, multi-paradigm architectures—also known as Poly AI—and this evolution is laying the foundation for agentic AI. Infosys has launched over 200 enterprise AI agents that are designed to transform enterprise processes.

In this interview, Balakrishna DR, executive vice-president, global services head, AI & Industry Verticals, Infosys, speaks to Sudhir Chowdhary on the market transition and how Poly AI architectures are enabling the rise of agentic AI. Excerpts:

Why is a hybrid AI model 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. This approach is like assembling a specialized team, rather than relying on one individual for everything.