Where to Start an AI Automation Project

Most companies considering AI automation for the first time ask themselves the same question: where to start? The temptation is strong to aim straight at the most complex process, the one that seems most impressive to automate. That is often a mistake.

Look for repetition, not complexity

The best first automation projects are not the most technically interesting ones, they are the tasks that come up most often and follow a stable pattern. A task performed twenty times a week, even a simple one, generates more automated value than a complex task performed once a month. The math is arithmetic: frequency multiplied by time spent, minus the cost of setting it up.

Take stock before choosing

Before deciding what to automate, it is worth listing, over a week or two, all the manual and repetitive tasks of a team: processing emails, entering data between two tools, generating documents, chasing clients. For each task, three questions are enough: how many times a week, how long each time, and is there already a clear rule for handling it.

Tasks that combine high frequency with a clear rule are the best candidates for a first project. Rare tasks, or ones that require very fine judgment, can wait for a later phase.

Start small, but do it well

A successful pilot project on a single, well-chosen task convinces more than a large, ambitious but poorly calibrated project. It is better to fully automate a process end to end, even a modest one, than to partially automate a larger process. A well-executed pilot also serves as a reference for what follows: it concretely shows what works in the company’s specific context, with its own tools and its own constraints.

Involve the people who do the work today

The staff who carry out a task manually know its exceptions and edge cases better than anyone. Ignoring them when designing the automation is the most common cause of failure: the system works in a demo, then stumbles on a real case nobody mentioned. Involving them from the start avoids this kind of bad surprise and makes adoption easier once the automation is in place.

Once this first project is in production and its results measured, it becomes much easier to prioritise the next ones on concrete grounds rather than impressions.