From isolated AI pilots to a working process

Many organizations now have multiple AI trials running. Employees use copilots, teams test chatbots, and departments experiment with their own documents. Yet the step to daily use often fails to materialize. Not because there are too few ideas, but because no one has established in advance when a trial is successful.

A workable pilot starts with a single process. Do not choose a broad ambition such as making employees more productive. Choose a recurring task with a known current execution. For example, summarizing helpdesk tickets, searching internal policy documents, or translating standard document types.

Establish four agreements in advance

First, appoint a process owner. This person determines which outcome is useful and decides after the test whether the application will proceed. Next, establish which data is required and which data remains outside the pilot. This prevents an experiment from gradually gaining access to more information than intended.

Then, choose two or three success criteria. For example, measure the time required per task, the percentage of answers that can be verified with a source, and the number of corrections by an employee. Also include a stop criterion. If quality or safety remains below an agreed threshold, the application does not go into production.

Finally, plan an evaluation immediately. Without a fixed decision point, a pilot can easily continue running for months. During the evaluation, you assess the outcome, the required monitoring, the costs of scaling up, and the impact on employees.

Technology follows the use case

Only when the process, data, and measurement points are clear does the choice of infrastructure become concrete. Confidential information may require local processing. Non-sensitive or temporary tasks may fit perfectly in the cloud. In many organizations, a hybrid model therefore emerges from the analysis, rather than from a pre-selected preference.