Most generative tools are built on large language models, and the capability differs in kind from the prior decade of procurement AI: earlier systems could classify a contract clause, while a generative model can rewrite it, compress 60 pages into one, or draft the first version of an RFQ from last year's event plus a new line-item list.

The honest limit is hallucination: the model produces plausible text unsupported by any source. Asked what a supplier's payment terms are, an ungrounded model answers confidently either way. The fix is architectural, not motivational: retrieve the actual documents, generate from them, and cite the source so a person can verify. This is why generation pairs naturally with extraction: structured data in, grounded text out.

Where it changes daily work is volume: summarizing every quote package rather than just the big ones, drafting every RFQ from precedent. Where it does not: the model holds no opinion on whether to single-source, and treating fluent text as analysis is how teams get burned. The shift is far enough along to be worth understanding where the tipping point actually sits. In LightSource, generated quote summaries stay linked to the underlying documents, so a buyer can verify any line in one click.