The sequence most teams follow when looking at automation: identify a painful process, find a tool that promises to fix it, buy the tool, discover the tool does not fit the actual workflow, repeat.
This is expensive. Not because the tools are bad — most are competent — but because automation that does not fit the underlying process creates new overhead. You end up managing the tool as well as the process it was supposed to replace.
A more useful starting point is pattern recognition. Before evaluating any tool, identify what kind of work is causing the friction. Is it recurring coordination? Is it data movement between systems? Is it approval routing where the logic is actually consistent but nobody has encoded it? Is it exception handling that keeps falling to a person because nobody ever defined what a good exception looks like?
Each of these has a different automation profile. Recurring coordination often benefits most from structured notification and status-tracking systems. Data movement benefits from integration-layer work. Approval routing benefits from explicit decision logic design. Exception handling benefits from clearer categorisation before any automation is applied.
The teams that automate well share one habit: they can describe the process in detail before they touch a tool. They know the triggers, the decision points, the exception paths, and the handoff conditions. That description becomes the design for the automation — the tool is just the implementation vehicle.
If you cannot describe the workflow without referring to the current tool you use to manage it, you are not ready to automate. The first step is always the same: map the process, then design the automation, then select the implementation.