How to choose the first AI use case in your organization

The first AI use case in your organization should meet three conditions: be measurable with a clear metric, repeat frequently, and rest on accessible data. Start where the value is obvious and the risk is low, not with the flashiest option. The goal of the first project is not to impress, but to reach production and prove that AI creates real value for the organization as a whole.

Start with the measurable, not the flashy

The most attractive use case in a presentation is rarely the best one to start with. A first project should have a success criterion you can define before you begin —time saved, volume handled, errors avoided. If you cannot set that metric, the project enters a loop of "let's improve it a little more" that never ends. The question that best filters a candidate is simple: in three months, will I know whether this worked?

Prioritize frequency and volume

AI pays off where the work repeats. A process that happens hundreds of times a month leaves real room to save time or reduce errors; one that happens twice a quarter does not justify the effort of taking it to production. Look for frequent tasks, with a clear pattern, where the team spends hours on something repeatable. There an AI system frees up capacity that flows back to the organization, not just to the team driving it.

Check that the data is accessible

A brilliant use case over inaccessible data is a project that never starts. Before choosing, verify where the system would read from, with what permissions and in what state that information is. If the data is scattered across tools that do not talk to each other and nobody has ever connected it, that work —not the model— will set the timeline. A first case with data on hand reduces the risk and speeds up the path to AI in production.

Bound the scope and the risk

The first project should be one where a mistake has limited consequences and human review always has room —the human-in-the-loop pattern. Deciding here over a critical process without a safety net is gambling the whole organization's trust on the first bet. A bounded case that crosses into production builds the momentum to tackle the more ambitious processes later, with the credibility already earned.

Think about the organization, not just your team

Innovation or R&D is usually the one that drives the first case, but the value should be felt outside that team. Choose a process whose outcome improves the work of another area —operations, support, finance— so the first project proves that AI serves the whole, not just whoever experiments with it. That is the best way to turn a pilot into a mandate to continue.

How we approach it at Codara

At Codara we start by researching where AI creates the most leverage in your organization and choose with you a first case that is measurable, frequent and low-risk. If you want to identify that first use case, let's talk about your organization and we will define it together.