Inside Atlassian


Article in AI at Work

Want more confidence in AI? Give it more context.

New Atlassian research finds the bigger predictor of whether AI helps at work isn’t how much people use it — it’s how much of their work it understands.

Article in AI at Work

New research reveals how AI is making jobs bigger

New research from Atlassian’s Teamwork Lab finds that the more people use AI, the more their capabilities expand—suggesting AI is broadening what workers can do, not simply replacing them.

Article in AI at Work

AI made your people faster. But it’s your office that’s slowing them down.

AI made individuals faster, but your office is still designed for the old bottleneck. Atlassian research reveals a new framework for the AI-enabled office.

Article in AI at Work

The tech we love to hate is the tech we’d hate to lose

New research from Atlassian’s Teamwork Lab finds that even as knowledge workers are uneasy about AI’s broader impact, they increasingly embrace it at work.

Article in AI at Work

You don’t need a team of AI experts. You just need one.

New research from Atlassian’s Teamwork Lab found that adding a single AI superuser made teams more likely to achieve a top score during ShipIt.

Article in AI at Work

Stop counting who uses AI. Start finding who’s transforming with it.

At Atlassian, we’ve gone through three evolutions of how we measure employee AI adoption. Each time we changed the metrics, we learned something telling about what the old numbers were hiding.

Article in AI at Work

Why individual AI speed isn’t delivering the ROI CIOs expected

89% of executives say AI has increased speed. Only 6% can point to org-wide ROI. IDC’s Wayne Kurtzman and Atlassian’s Liz Fosslien unpack what’s missing – and where CIOs should start. Everyone on your team is faster. So why isn’t your organization moving further? That’s the provocation at the center of a recent fireside chat […]

Article in AI at Work

Is AI flattening your team’s creativity? Here’s how to tell.

AI can make “good enough” thinking the default. Use this framework to spot the risks of AI over-reliance and decide when humans — not models — should lead.