Businesses need reliable software: Taming AI for enterprises could spell business for India’s IT sector

Enterprise technology has long rested on a basic assumption: determinism. When a system gets identical inputs, it must yield identical outputs. Business and tech leaders rely on this expectation.

Banks can reconcile millions of financial movements and telecom operators can bill subscribers accurately because the software they use behaves in a perfectly predictable manner. This is true across enterprises.Determinism is not a trivial engineering attribute; it gives regulators assurance, auditors clarity and businesses stability. It is an unspoken contract between organizations and their digital systems.

Historically, that contract has been upheld. Large language models (LLMs), however, have begun to stretch this long-standing assumption.The LLMs of AI did not descend from the deterministic lineage of classic enterprise systems. They emerged from probability theory, pattern recognition and statistical learning.

An LLM knows that in English, ‘good’ is more likely to follow ‘very’ than, say, ‘hippopotamus.’ But ask the same LLM the same question twice and its responses may vary even if it didn’t hallucinate. It may generate ‘cold’ after ‘very’ rather than ‘good.’ Use the same model on two identical machines and minor differences can appear. Even if the system’s ‘temperature’ is set to zero (which instructs it to choose the single most likely next word), minor variations may appear since an LLM generates text one token (a few letters, not always a full word) at a time.

A tiny difference in the underlying probability distribution can lead it to pick a different token, which influences the next token, and so on. But this violates the deterministic principle: that a machine must always act in the same way. Modern LLMs depend on