Atlassian’s Head of AI, Sherif Mansour, revealed how the company embedded AI features into over 20 applications, nearly abandoned chat as an AI interface, and pivoted their hiring strategy to focus more on junior talent to accelerate innovation across their vast product portfolio.
- AI chat interface deployed across 20+ apps for user-driven feature innovation
- Bolt-on AI approach enabled fast iteration instead of full rewrites
- Hiring shifted heavily toward junior PMs to meet AI product demands
What happened
Sherif Mansour, who leads AI product management at Atlassian and oversees 450 product managers, discussed the company’s journey integrating AI across more than 20 applications—including six built in the generative AI era and many older legacy products. Despite the conventional advice to avoid bolting AI onto existing software, Atlassian integrated AI features directly into its established apps and introduced a chat interface called Rovo chat to enable broad experimentation with user interactions.
Initially controversial within the company due to chat’s poor user experience for certain tasks, Rovo chat was shipped across the portfolio because building its backend was necessary for powering AI features anyway. The chat interface acted as a universal command line for AI, allowing users to explore diverse use cases and guiding Atlassian in identifying where to develop dedicated AI-powered features. This approach led to millions of daily users engaging with AI inside Atlassian products.
Why it matters
Atlassian’s experience challenges common AI product development rules, proving that bolting AI onto existing applications can work at scale when combined with careful user behavior analysis. The company learned that chat interfaces serve a crucial exploratory role for AI interactions, capturing real user needs and informing the creation of more specialized, integrated features over time.
Furthermore, Atlassian’s ability to embed AI across complex workflows—such as those managed in Jira, which extends well beyond software development into diverse business processes—demonstrates the broad potential for AI automation in enterprise software. Their adjustments in hiring, favoring junior product managers, signal a strategic bet on rapid scaling and fresh perspectives needed to keep pace with evolving AI capabilities.
What to watch next
Monitor how Atlassian further evolves the balance between chat as a flexible AI interface and more focused, task-specific AI features across its portfolio. The company’s continued experimentation with bridging AI tools to both users and automation agents within workflows will be critical to watch, as this could set a precedent for AI deployment in legacy SaaS ecosystems.
Also worth observing is how Atlassian’s shift toward hiring junior talent impacts innovation velocity and product quality in AI initiatives. Their model may influence other SaaS companies aiming to cultivate AI competence at scale while managing legacy codebases and diverse user bases.