Consider this requirement:

Find policies that will expire within the next 30 days.

This does not require AI. SQL can retrieve the exact records:

SELECT *
FROM Policies
WHERE ExpiryDate >= CAST(GETDATE() AS DATE)
  AND ExpiryDate < DATEADD(DAY, 31, CAST(GETDATE() AS DATE));

Known expiry date + fixed 30-day rule → SQL result

AI becomes relevant only when the business asks:

Among the policies expiring within 30 days, which are most likely not to renew?

SQL still retrieves eligible policies. The ML model then assigns risk:

SQL:
Which policies expire within 30 days?

AI:
Which of those policies are at higher risk of non-renewal?

To make that prediction, the model needs meaningful inputs such as:

• Previous renewal count
• Claim history
• Complaint history
• Payment delays
• Customer relationship length
• Recent engagement
• Premium changes

Creating these useful inputs is called feature engineering.