Getting disappointing results from AI? It could be because AI is often used like a vending machine — type a prompt, hope something usable comes back.
There’s a better approach.
This session demonstrates a repeatable, human-guided AI loop where curated discovery inputs turn Atlassian Intelligence into a true teammate that delivers actionable, high-quality work. You’ll learn a framework for improving Atlassian AI performance by providing the right context through the Teamwork Graph, a pattern that works across teams and workflows. We’ll make it real with a live product discovery-to-development example, showing how better inputs turn rough ideas into clear, buildable work.
The key insight: AI is only as good as the context it’s given. Curation drives quality. Verification drives confidence. When humans invest in the right inputs up front and confirm the output, AI starts producing work that’s genuinely useful, and speed to value follows.
Key Takeaways
- Learn a repeatable framework for improving Atlassian AI outcomes by providing the right context through the Teamwork Graph.
- See how curated context, not clever prompts, determines AI output quality.
- Build reusable context assets using pattern pages and agent configuration.
- Brief an AI agent the same way you’d brief a new team member.
- Apply the iterative loop: human curates context, AI drafts and improves, human verifies.
- Compress idea-to-ready timelines without sacrificing clarity or rigor
Speakers:
 | Parth Patel Sr. Director AI-Enabled Transformation & Enteprrise Transformation, Cprime |
 | Mark Holmes Senior Consultant, Solution Design and Engineering, Cprime |
 | Roopa Nagaraj Partner Solutions Architect, Atlassian |
