The step that most determines the success of an automation project isn't the technology chosen, it's the choice of process. A good first case has high volume, clear rules, few exceptions and a measurable manual cost. Starting here generates returns early, reduces risk and builds confidence to extend automation to more complex processes. Choosing the first case poorly is the most common reason automation projects fail to reach the expected return.
The decision that determines success
Many companies start with the technology, asking which tool to adopt. The correct order is the reverse. First identify the process with the most waste and the best profile for automation, and only then choose the approach, between integration, robotic automation and artificial intelligence (see AI-driven vs traditional automation). This order avoids investing in solutions looking for a problem.
The five selection criteria
A process is a good candidate for a first case when it combines:
- Volume: it repeats frequently. The more often, the greater the gain.
- Clear rules: the steps are predictable and the decision follows defined criteria.
- Few exceptions: most cases are handled the same way. Too many exceptions reduce the benefit.
- Measurable manual cost: it's possible to quantify the time and error rate of the current process.
- Stability: the process and the systems involved change little, which makes the automation durable.
A simple scoring grid
A practical way to compare candidate processes is to score each one from 1 to 5 on the five criteria and add them up. The processes with the highest scores are the best starting points.
| Criterion | Process A | Process B |
|---|---|---|
| Volume | 5 | 3 |
| Clear rules | 4 | 2 |
| Few exceptions | 4 | 2 |
| Manual cost | 5 | 3 |
| Stability | 4 | 3 |
| Total | 22 | 13 |
In this example, process A is clearly the best candidate for a first project. The grid doesn't replace analysis, but it helps make the decision objective and easier to justify internally.
Common mistakes to avoid
- Starting with the hardest process: wanting to solve the most complex problem right away increases risk and delays the return.
- Trying to automate everything: the value lies in automating the normal case and routing the exception well, not in covering every variation.
- Ignoring maintenance: unstable processes require frequent reconfiguration and can cost more than they save.
- Not measuring the starting point: without data on the manual process, it's impossible to demonstrate the return afterwards.
How to measure the return
Before automating, record the time spent, the frequency and the error rate of the manual process. Then compare it with the automated process. The return tends to be positive when the process consumes significant skilled time and is stable (see how to calculate automation ROI). For low-volume processes, lighter alternatives may be preferable to a full project.
Frequently asked questions
What is the best first process to automate?
The one with the highest volume, the clearest rules, the fewest exceptions and the highest manual cost. Back-office tasks tend to combine these characteristics.
Should I automate a process that changes a lot?
Generally, not as a first case. Unstable processes require frequent maintenance. It's preferable to start with a stable one.
Do I need a tool before choosing the process?
No. First the process, then the approach. The tool should serve the problem, not the other way around (see what RPA is).