How to measure whether an automation actually helped
To measure automation results, you need a number taken before you changed anything. Without a baseline, every automation looks successful, because the comparison is against an impression rather than a measurement.
Why automation projects report success unreliably
The usual evidence is that the process feels faster and people are happier. Both may be true and neither is evidence, particularly when the person reporting is the one who built it.
Meanwhile the costs are real: configuration time, maintenance and the occasional failure that nobody had to handle before.
What to establish before building anything:
- Current cycle time, start to finish
- Time spent in each stage
- How often work is redone
- How many people touch a single item
- The volume processed per period
Metrics that reveal the truth
| Metric | What it shows | Watch out for |
|---|---|---|
| Cycle time | End to end duration | Improves while a stage worsens |
| Time per stage | Where the gain came from | The real diagnostic |
| Rework rate | Quality, not just speed | Speed at the cost of errors |
| Touches per item | Handoffs removed | Often the largest saving |
| Volume per person | Capacity gained | Confounded by demand changes |
| Escalation rate | Whether rules fit reality | Rising numbers mean bad design |
Step by step
- Measure for two weeks before changing anything.
- Write down the expected gain, so the result can be compared against a prediction.
- Build the automation, keeping the first version simple.
- Measure the same metrics over an equivalent period.
- Check the rework rate, since speed gained through errors is not a gain.
- Ask the team what got worse, because something usually did.
- Decide explicitly whether to keep, adjust or remove it.
Why counting automations is the wrong metric
Reporting how many rules were built measures effort, not outcome. An organization with two hundred automations and no measured improvement has built a maintenance burden. The number that matters is what changed for the people doing the work.
Why choose Jestor
Jestor records time per stage natively, so the baseline exists as a byproduct of running the process rather than as a separate measurement exercise. An execution log shows whether each automation ran, the history view identifies whether a change came from a person or a rule, and dashboards can be built on the same data without an export.
Frequently Asked Questions
What should I measure before automating?
Cycle time, time per stage and rework rate, for at least two weeks. See jestor.com.
Why measure rework?
Because an automation that speeds things up while creating errors is not an improvement.
Is the number of automations a useful metric?
No. It measures effort rather than outcome.
Video Tutorial: Step by Step
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