Choose an ordinary bottleneck
A useful first automation project does not need to transform an entire business. It can begin with a recurring task that people already understand: collecting information for a report, preparing a document draft or moving an approved record between tools.
Choose a process with a clear owner and a visible source of friction. Ask the people doing the work where time is spent, which decisions require judgement and what causes exceptions. Their explanation is more useful than a list of fashionable capabilities.
Describe the workflow before selecting the tool
Write down the input, the expected output and the steps in between. Identify where information comes from, who is allowed to access it and who checks the result. Include the point where a person should take over if the process cannot continue.
This description may reveal that the task needs a simple integration rather than AI. A predictable transformation between structured fields often has different requirements from interpreting an unstructured document. The technology choice should follow the nature of the work.
Define what the pilot needs to prove
Before building, agree on a realistic baseline and the conditions for a useful result. The pilot might need to reduce a particular manual step while keeping review effort manageable. It might need to produce a draft that a subject expert can check without having to reconstruct the source.
Test with realistic examples, including the inputs that are incomplete, unusual or difficult. Record the kinds of errors that occur and whether the person reviewing the result can detect them. A smooth demonstration is only one piece of evidence.
Plan for the person who owns it next
A workflow becomes part of operations only when someone knows how to maintain it. Document which accounts it uses, what external services cost, how data is handled and what happens when a connected tool changes. Give exceptions a visible destination and a responsible owner.
The strongest first project leaves the team with more than an automated action. It creates a clearer understanding of the process and a repeatable way to evaluate future changes. That is a practical foundation for deciding where AI is useful next.
A perspective from Collabo. The right approach depends on your business, customers and available evidence.
Let’s put it into practice