Step 1: Select the right process
Don’t start with the biggest process, but with the most rewarding one: frequent, rule-based, time-consuming, and with data that is already digital. A short list of candidates with the estimated time spent per week quickly shows where the leverage is greatest.
Step 2: Understand and streamline
Before anything is automated, the process needs to be laid out on the table: Who does what, in which system, with which exceptions? This almost always reveals dead weight, such as duplicated work or loops no one needs anymore. Simplify first, then automate.
Step 3: Choose the technology
Different processes call for different solutions: RPA for rule-based clicking work, interfaces for stable system connections, AI for unstructured data such as documents or emails, or a combination. The technology follows the process, not the other way around.
Step 4: Pilot with real cases
The automated process initially runs in parallel or with human approval. This way, real cases reveal where exceptions occur before they cause damage. Define in advance how success will be measured: time saved, error rate, cycle time.
Step 5: Scale and operate
Once the pilot runs reliably, it is expanded: more volume, adjacent processes, additional departments. A clear operating plan is essential: Who monitors the automation, who gets notified of exceptions, and how are changes to the systems involved reported?
The three most common mistakes
- Automating chaos: Automation doesn’t make an unclear process better, it just makes it go wrong faster.
- Starting too big: A mammoth project as your first effort stifles acceptance. The first visible success is what counts.
- Forgetting operations: Without monitoring and clear responsibilities, any automation dies at the first exception.