Most AI-powered business applications succeed or fail based on data quality and scope, not the specific model chosen. Clean, well-structured source data makes retrieval and automation far more reliable.
It helps to define upfront what the AI feature is responsible for, and what it explicitly should not do — clear scope reduces both hallucination risk and user confusion.
Guardrails (input validation, output checks, human escalation paths) and analytics (tracking what users ask and where the system struggles) should be built in from the first version, not added later.
Starting with one well-defined AI feature — a specific assistant, a specific automation — tends to produce better results than trying to make an application "AI-powered" everywhere at once.