The rule of thumb
The best candidates are processes that occur frequently, follow clear rules, and currently demand a lot of manual work with unstructured content – texts, emails, documents, or forms. That's exactly where AI plays to its strength: it understands content that classic automation can't process.
Six selection criteria
- Frequency: the process runs daily or weekly – not three times a year.
- Time cost: each run visibly eats up minutes or hours.
- Regularity: the steps follow a recognizable pattern, even if the content varies.
- Unstructured content: emails, documents, or free text have to be read, understood, and categorized.
- Clear data situation: the necessary information exists digitally or can be connected.
- Error tolerance with oversight: results can be checked before anything critical happens.
Typical candidates in practice
Email classification and routing, checking incoming documents, preparing quotes, research and summarization tasks, transferring data between systems, and recurring reports are among the most rewarding starting processes. They're frequent, easy to scope, and the benefit is immediately measurable.
When AI is the wrong answer
If a process is fully rule-based and only works with structured data, classic automation is often enough – cheaper and deterministic. If the process itself is the problem because it contains unnecessary loops, fixing the process comes first. And for rare, high-risk special cases, human handling usually remains the best choice.
That's why an honest analysis belongs at the start of every automation project: sometimes the conclusion is that AI isn't the most economical solution in this particular spot.
How to prioritize
A simple matrix of benefit (time saved × frequency) and feasibility (data situation, system access, risk) has proven itself. The best starting process sits in the “high benefit, good feasibility” quadrant – not the most spectacular candidate, but the most reliable one. A successful first use case builds internal confidence for the next ones.