When confronted with the error message indicating the inability to convert an object to a NumPy datetime, it often suggests that the data being passed into the conversion function is not in an acceptable format. Common pitfalls include attempting to convert incompatible types such as strings that don’t conform to standard date formats, or numerical values that don’t represent valid timestamps. To troubleshoot, ensure that the objects being processed are either strings in a recognized format (‘YYYY-MM-DD’, for example) or are already in a datetime-like structure. Utilizing Pandas can be beneficial, as its `pd.to_datetime()` function can handle a broader range of formats and automatically convert them into NumPy datetime objects, which ultimately simplifies the process.

Additionally, if you’re handling arrays or lists of dates, it’s crucial to validate the entirety of your data. Sometimes, the presence of `None`, NaT (Not a Time), or malformed date strings can derail the conversion process. Consider implementing robust data cleaning steps before conversion; filtering out invalid entries or replacing them with appropriate fallback values often mitigates these conversion issues. Finally, employing try-except blocks can help gracefully manage exceptions and identify problematic entries, allowing your program to continue running even when encountering non-convertible objects.