Users who treat AI as a collaborator—debating with it, challenging its outputs, and engaging in back-and-forth dialogue—see superior outcomes. This mindset shift produces not just efficiency gains, but also higher quality, more innovative results compared to simply delegating discrete tasks to the AI.
Leaders often assume goal alignment. A simple exercise is to ask each team member to articulate the project's goal in their own words. The resulting variety in answers immediately highlights where alignment is needed before work begins, preventing wasted effort on divergent paths.
Status update meetings are a major productivity drain. Replace them with asynchronous videos (e.g., Loom). This method is more efficient, allowing people to consume updates on their own time. It also conveys more signal—tone, emphasis, and personality—than a written update, fostering better connection on distributed teams.
Productive teams need to schedule three distinct types of time. Beyond solo deep work and structured meetings, they must carve out 'fluid collaboration' blocks. These are for unstructured, creative work like brainstorming or pair programming, which are distinct from formal, agenda-led meetings and crucial for innovation.
The common failure of "pre-read" meetings is that attendees don't do the reading. Atlassian, like Amazon, solves this by starting decision-making meetings with a dedicated, silent period where everyone reads the context document together. This guarantees shared context and makes the subsequent discussion far more effective.
Atlassian's AI onboarding agent, Nora, answers new hires' logistical questions, reducing their reluctance to bother managers. More strategically, this initial, low-stakes interaction serves as an effective on-ramp, conditioning employees from day one to view AI as a standard collaborative tool for their core work.