Standard data validation asks whether a value is allowed: a date is a date, an amount is positive, a code exists in the list.
Business rule validation asks whether the record, or the decision made from it, is right under the rules the business runs on: a discount applies only above a tenure threshold, a claim is payable within a grace period, an order above a limit needs a second approval.
Rules worth validating for an AI task:
- eligibility conditions that decide who gets an outcome
- pricing and discount terms, including the ones applied by habit
- approval limits and the cases that need a second check
- exceptions the business handles differently, with the reason
How is business rule validation different from data validation?
- Data validation checks values against defined rules such as type, format, range, required fields and relationships; business rules can be among them once someone has written them down.
- Business rule validation checks records and outcomes against the logic of the business, including rules that are not yet written into any check, which needs the people who own the process to state them.
- Data can pass every validation rule and still break a business rule, because the rule may not be recorded anywhere the data can show it.
Where does business rule validation break in AI work?
A pricing assistant passes every data check, then quotes standard prices to customers who hold a loyalty term recorded only in an account manager’s notes. The rule exists and is applied every week, but nothing validated the assistant’s output against it, because the rule was never written where a check could read it.
The fix starts with writing the rules down with their owners and turning them into fields and test cases the task is checked against. Why does AI miss business rules and exceptions? walks through where such rules tend to live and how to catch them before a task ships.