Three months reveals which habits are real
Short pilots mostly measure enthusiasm. Multi-month usage shows whether the assistant becomes part of review culture, documentation habits, and day-two operations. That is where the signal becomes more valuable.
In this team study, the strongest gains came from summarization, draft acceleration, and calmer handoff between research and execution tasks.
What slowed teams down
The main friction was not output quality alone. It was verification overhead, uncertainty about memory boundaries, and the temptation to overuse the tool on tasks that still needed faster human judgment.
The healthiest teams built narrower lanes: where the model could help repeatedly without expanding into every decision surface.
The durable change is operational tone
The surprising benefit was not raw speed but steadier collaboration. Teams reported that work became easier to hand off when AI outputs were treated as structured drafts instead of authoritative answers.
That is why Lab should keep publishing workflow studies: they show which usage patterns actually survive contact with real teams.
