Why an automated process needs to be understood before it is built
When faced with a slow process, the temptation is to jump straight to the technical solution. Write the script, connect the systems, deliver quickly. This approach rarely works first time and often costs more than expected because an essential step has been skipped: understanding the real process before turning it into an automation.
What does "understanding the process" actually mean?
It is not the same as reading a written procedure. Documented procedures often describe the ideal case, the one where everything goes smoothly. Real work includes exceptions, workarounds learned through experience and decisions that are not documented anywhere.
Understanding a process therefore means observing how it actually works, or questioning in detail the people who perform it every day. What information is checked before starting? What happens when data is missing? What happens when two rules appear to conflict? Who makes the decision when a case falls outside the usual scenario?
Even a case that only occurs a few times a year can matter if it involves a significant amount, a particular risk or a situation the team cannot simply ignore.
Why skipping this step gets expensive
An automation built on an incomplete understanding of the process may work perfectly for the standard case and then fail as soon as it encounters a situation nobody had documented. At that point, the solution may need to be corrected, the affected case may have to be handled manually, or both.
A correction made after development can also challenge decisions that have already been made. If an exception changes an initial assumption, simply adding another condition to the code may not be enough. Part of the process may need to be redesigned and retested.
The team's confidence in the solution can also decline. An automation that regularly fails on cases users understand but the system does not will naturally lead people to check the results manually. The expected time saving then gradually disappears.
How to do this step properly
Start with real cases, not just theoretical examples. Files, emails, records or transactions that were actually processed in the past reveal variations that do not appear in a standard procedure.
Then classify the cases you find: the frequent and simple case, common variations, and rare but real exceptions. This classification helps determine what can be automated in the first version, what still needs human validation and what should remain manual for now.
Finally, have the people who perform the process today validate this understanding before a single line of code is written. They often know implicit rules, practical workarounds and particular situations that an outside observer would not notice.
This step also helps define a realistic scope for the first version. The goal does not necessarily have to be automating one hundred percent of the process from the start. Correctly handling a sufficiently representative part often provides a much faster way to demonstrate the benefit and identify what still needs to be solved.
The takeaway
Time spent understanding a process before automating it is not extra time before the real work begins. It is an essential part of the work: defining the rules, identifying exceptions and choosing a realistic scope. Solid automation starts with an honest look at how the work is actually done, exceptions included.
Solid automation starts with an honest understanding of how the work is actually done. If you are preparing an automation but are not sure you have captured all the variations and exceptions, I can help clarify the process before development starts.