How to choose your first process for AI

“We want to use AI” is an intention. “We want to cut the time spent sorting requests while keeping human review” is already a starting point for a project. The difference is that the second phrasing lets you choose a solution and measure the outcome.

For your first project, look for an activity frequent enough to matter, yet well-defined enough that you can observe and correct mistakes.

Choose a task, not a whole department

A first test can follow the classification of messages, the drafting of a reply or the extraction of fields from a document. These are examples of tasks that can be evaluated separately, without changing the entire process from day one.

First check whether you need AI at all. A simple, stable and explicit rule can be implemented with ordinary automation. AI is worth exploring where the information varies and a list of rules would become hard to maintain.

Define what a good result means

Gather representative examples, including the unclear and rare cases. Write down the answer you would expect and who can validate it. A test run only on the easy examples gives an incomplete picture.

Track more than speed. What matters is how often the result has to be corrected, how long the review takes and how much a mistake costs. If reviewing a proposal takes longer than doing the task from scratch, the approach needs to change.

Keep a path for human intervention

Decide when the system may only suggest and when it may act. At the start, a useful option is for it to prepare the result while a colleague confirms it before it reaches the client or changes a record.

For ambiguous situations there must be a clear way out: handing the case to the right person. “I can’t determine this” can be a better answer than a plausible but wrong completion.

Check the data before the test

Clarify what information the solution can receive, where it is processed and who has access to it. Don’t send real documents to a tool just because the demo is easy to set up. For the first trials, use examples suited to the test environment and to the organization’s rules.

Also check whether the data you need exists in a usable form. If the information is contradictory or hard to find, organizing and integrating your systems may be the first step.

Expand once you have a real comparison

Run a limited test and compare the result with the way you work today. Decide in advance what would justify expanding and what would mean stopping or redoing the test. That way you avoid continuing just because you have already invested time.

A small, well-measured project shows you where AI adds value and where something else is needed. We can look at a concrete process together, before choosing any tools.