Topic
8 facts from 1 episode, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
Undated
Sherif Mansour says most of Atlassian's users today work in non-technical departments such as HR, finance, marketing and legal.
“So today, the majority of our users are actually from non-technical departments, HR, finance, marketing, legal, and all that.”
Undated
Sherif Mansour says Atlassian has close to 3.5 million users of its AI capabilities.
“we've got close to, I think we're 3.5 million users of our AI capabilities”
Undated
Sherif Mansour describes an AI maturity pyramid in which layer one is asking questions and layer two is having AI generate artifacts.
“bottom layer, most people just ask a question, get an AI from an answer, layer one. Layer two is like, oh, hang on a second, AI can do some work for me”
Undated
Atlassian's Rovo chat tool has built-in personal memory that learns a user's preferences, such as Sherif Mansour's habit of putting emoji in headings.
“we've got inbuilt personal memory. So over time we start to remember, you know, for me, I like to write with a lot of emoji.”
Undated
Sherif Mansour says Atlassian uses retrieval-augmented generation rather than its graph for simple lookups such as a company's annual leave process.
“what's the process for taking annual leave in my company? RAG's probably a much better solution there. We don't traverse the graph for that.”
Undated
Sherif Mansour says adding AI agents to Jira workflows is probably the biggest or fastest growing use of agents among Atlassian customers.
“adding an AI agent in a Jira workflow, which is probably the biggest or fastest growing use case of agents in deployment”
Undated
A customer described to Sherif Mansour that using AI for simple string checks in a workflow was like swatting mosquitoes with a rocket launcher.
“I've got a rocket launcher and I'm swatting mosquitoes with it.”
Undated
Sherif Mansour says general-purpose models solve 80 to 90 percent of typical knowledge-worker tasks and that customer demand for choosing a specific model is declining.
“the general purpose models are 80% to 90% easily solving the problem and we're seeing that desire for model selection just decline over time”
Episodes