The integration of AI into software testing pipelines has moved past the experimental phase in most engineering organizations that were going to try it. Engineering teams are not evaluating whether AI belongs in their testing workflows anymore, but are discovering, sometimes painfully, that the architectural decisions they made when integrating it are the ones that will determine whether it improves their release confidence or quietly erodes it.