Anyone using language models in a business setting notices it quickly: the outcome depends less on the model than on how the task is phrased. Since the widely read Google whitepaper on prompt engineering, a stable toolkit has established itself: instructions without examples, supplied sample cases, fixed roles and step-by-step reasoning chains.
In day-to-day ERP work each technique has its place. Standard sales enquiries call for short, unambiguous instructions. Quotations in consistent language turn out more reliably when a sample document is supplied. For financial analyses it pays to store a ground rule that every answer closes with a trend and a recommendation. Where master data conflicts arise, it helps to have several clean-up paths played through rather than taking the first idea.
Our advice to mid-sized firms: name the techniques, document them and share them across the team. Only then do answers from language models become reproducible and therefore useful in operations.