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    Nate
    Nate@nate_512
    🏢Atlassian🏢Vercel Inc.🏢Lovable

    State of the AI SDLC summit

    94% OF ENGINEERING ORGS USE AI. ONLY 6% HAVE THE SYSTEMS IN PLACE TO SCALE IT So how do we close that gap? and what are the systems that the 6% have in place? This is exactly what companies like Atlassian, Vercel, lovable, and more address during State of the AI SDLC, a digital summit for engineering & product leaders navigating the realities of building with AI. If you watch any of the on demand sessions, you can’t miss 'Context Over Code': mcannonbrookes (CEO + Co-Founder, Atlassian) & rauchg (CEO, Vercel) sit down and unpack how to navigate the shift from adopting tools to building actual systems. Then, I recommend watching matthewcanham & Ming Wu’s session on how they scaled the AI SDLC at Atlassian across an organization of over 6k+ engineers. My key takeaways: To scale across an entire engineering org you need governed loops that connect agent activity to the work already happening. These sessions provide a literal blueprint for moving from pilot to production. → context dictates output quality → native workflows require systems of record → scaling requires governed loops The summit sessions are available on-demand right now. Session link in the 🧵 ↓ #Atlassian #VercelInc

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    Nate
    Nate@nate_512
    🏢Atlassian🏢Vercel Inc.🏢Lovable

    State of the AI SDLC summit

    94% OF ENGINEERING ORGS USE AI. ONLY 6% HAVE THE SYSTEMS IN PLACE TO SCALE IT So how do we close that gap? and what are the systems that the 6% have in place? This is exactly what companies like Atlassian, Vercel, lovable, and more address during State of the AI SDLC, a digital summit for engineering & product leaders navigating the realities of building with AI. If you watch any of the on demand sessions, you can’t miss 'Context Over Code': mcannonbrookes (CEO + Co-Founder, Atlassian) & rauchg (CEO, Vercel) sit down and unpack how to navigate the shift from adopting tools to building actual systems. Then, I recommend watching matthewcanham & Ming Wu’s session on how they scaled the AI SDLC at Atlassian across an organization of over 6k+ engineers. My key takeaways: To scale across an entire engineering org you need governed loops that connect agent activity to the work already happening. These sessions provide a literal blueprint for moving from pilot to production. → context dictates output quality → native workflows require systems of record → scaling requires governed loops The summit sessions are available on-demand right now. Session link in the 🧵 ↓ #Atlassian #VercelInc

    51m

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