Automating a Poor Process Faster Still Leaves You with a Poor Process

Automating a process does not automatically turn it into a good process.

It may simply make it faster.

And that is precisely the problem.

If an operation contains unnecessary steps, duplicated data, approvals that add little value, contradictory rules, or exceptions created by poor design, automation may end up executing those inefficiencies thousands of times at extraordinary speed.

That is why there is an important difference between automating tasks and improving processes.

The risk of copying the current process

A common approach is to document what a person currently does and then ask a robot to reproduce those exact steps.

The user opens an application.

Downloads a file.

Copies information.

Opens Excel.

Modifies columns.

Sends the file.

The robot is then expected to do exactly the same thing.

It sounds logical, but it is not always the right approach.

Before development starts, another question should be asked: why does each of these steps exist?

Perhaps the file could be generated directly in the required format.

Perhaps Excel exists only as an intermediate layer because two systems were never integrated.

Perhaps a manual validation could become a business rule.

Perhaps three approvals could become one.

Perhaps the information already exists in another system.

Automating without asking these questions can institutionalize inefficiency.

Process Discovery: understand before changing

The first task is to understand the real process, not only the process shown in an official procedure.

That means understanding:

• Who participates.
• Which applications are used.
• What information is received.
• What information is generated.
• Which business rules apply.
• Which exceptions occur.
• Which activities consume the most time.
• Which activities generate errors.
• Which dependencies exist.
• Where bottlenecks appear.

It is also important to distinguish between the normal flow and the exceptions.

A process can look simple when its ideal path is described and become extremely complex once all the situations that occur during real operations are considered.

Eliminate before automating

One of the most important Process Improvement principles is to ask whether an activity should exist at all.

Imagine a process with 22 steps.

The first reaction might be to automate all 22.

A deeper assessment could determine that five steps can be eliminated, three can be consolidated, two validations can happen automatically at the source, one approval is unnecessary, and four activities can be handled through integration.

The redesigned process may contain significantly fewer activities.

Now the automation becomes simpler.

And simpler automation typically means:

• Less development.
• Fewer failure points.
• Lower maintenance.
• Higher speed.
• Greater stability.

Simplifying rules can also generate return

Another common issue is business-rule accumulation.

Over time, organizations create rules. Some were introduced because of circumstances that no longer exist. Others overlap. Others apply only to a very small percentage of transactions.

Before coding them, it is worth asking: is this rule still necessary?

Every additional rule can become additional logic, additional testing, and additional maintenance.

Process simplification has a direct impact on the complexity of the technology that follows.

Standardize before scaling

The same process may be executed differently depending on the country, business unit, team, branch, user, or system.

Automating immediately may mean creating several versions of the same solution.

In some cases, it is better to first determine which parts of the process can be standardized.

This is particularly important for regional organizations operating across several markets.

A common design allows genuine differences to be managed through controlled configurations or variants instead of creating a completely different automation for each operation.

Exceptions reveal process problems

Exceptions should not be analyzed only as obstacles to automation.

They also provide information.

If a significant proportion of transactions require manual intervention, the organization should understand why.

Possible causes include poor data quality, incomplete information, ambiguous rules, integration failures, inconsistencies between systems, or activities performed outside the defined process.

Solving the root cause may be more valuable than building additional logic to manage the exception indefinitely.

What should come out of Process Improvement?

Before development begins, there should be a clear definition of the target process.

That process should establish:

• Which activities disappear.
• Which are simplified.
• Which are standardized.
• Which are automated.
• Which require human intervention.
• Which exceptions will remain.
• Which systems will participate.
• Which controls are required.

That becomes the To-Be process.

And that is the process that should be automated.

Value can begin before automation

Removing an unnecessary approval generates value.

Reducing duplicate data entry generates value.

Standardizing an activity generates value.

Eliminating an intermediate spreadsheet generates value.

Simplifying a rule generates value.

Automation can then multiply those benefits.

Automation should amplify a good process

The question should not be: how do we automate what we do today?

It should be: if we could redesign this process today, how should it work?

Only then should the organization determine which combination of RPA, integrations, workflows, low-code, and Artificial Intelligence can execute it effectively.

Automating faster does not necessarily mean progressing faster.

When the process is poorly designed, taking the time to improve it first may be the decision that accelerates the final result the most.