The first decision in an AI project is choosing which work deserves to change. That choice shapes the scope, the team's involvement and the chance to learn something useful.
A visible, well-defined task usually tells you more than a broad initiative where it's hard to see what improved.
1. Start with a specific task
Talk to the people who do it. Ask them to show you a recent example: what they receive, where they look for information, what decisions they make and what they deliver.
"Improve operations" is a goal. "Prepare the log of invoices received by email so the team can review it" already describes a process.
2. Check what information it needs
Identify the sources, permissions and criteria the team uses. A solution needs enough context to do the task and to notice when it should ask for help.
- Where is the information?
- Who can access it?
- What changes between a normal case and an exception?
- How do we know the result is correct?
These questions help you tell what can be built now from what needs groundwork first.
3. Scope a useful first version
Pick a stretch of the process with a clear input and output. Define what the system does and what a person reviews.
You can start by preparing a draft, extracting data or classifying incoming items. The goal is to validate a specific improvement before expanding what the system does.
4. Measure the starting point
Record how much time the process takes, which errors appear and what volume the team handles. Add a quality measure that makes sense for that task.
Then use the same criteria to evaluate the first version. Time saved matters, but so does the time it takes to review and correct the result.
A shared decision
The first case should have a business owner, people willing to try it and an agreed success criterion.
At AI Mate we connect that assessment with design and development. That way, the opportunities we identify already include a view on how to put them into operation.