LLM in business: use cases that can pay off

§ 01Good use cases

Support triage, document extraction, internal knowledge search, draft generation and quality checks are usually stronger than generic “AI assistant” ideas.

The best scenarios have clear inputs, clear outputs and a human review step for sensitive decisions.

§ 02RAG and data

Retrieval-augmented generation works only when documents are clean, chunked, updated and permissioned. The model is not a replacement for information architecture.

Start with a small trusted corpus before connecting every file storage in the company.

§ 03Cost and latency

LLM costs depend on token volume, model choice, caching and retry behavior. A prototype that works for ten users can become expensive at scale.

Measure latency and cost per successful task, not per API call.

§ 04Governance

Log prompts, outputs, source documents and user feedback where policy allows. Add redaction for personal or commercial data.

For regulated workflows, keep final decisions with a person and document the AI role clearly.