{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"AI Thoughtmakers","title":"What It Really Takes to Make AI Work at Enterprise Scale","html":"","width":"100%","height":180,"duration":1376,"description":"Giving your team access to AI tools doesn't make your company AI native. Not even close. Making AI work at enterprise scale requires the right engineering systems, codebase context, workflows, and measurement.\n\nIn this episode of AI Thoughtmakers, we sit down with Chaitanya Deorukhkar, Principal Engineer at Atlassian — the company behind Jira, Confluence, and Bitbucket — for one of the most honest and grounded conversations about what it actually takes to make AI work inside a large company.\n\nAtlassian's codebase has over 500,000 files and millions of lines of code. When an AI agent steps into that environment without the right setup, it doesn't just slow down — it gets completely lost. Chaitanya breaks down exactly what that looks like, why throwing more information at AI doesn't always help, and how Atlassian built an internal tool to actually measure whether their AI setup is working or not.\n\nThis is not a conversation about hype. It's about the real, unglamorous engineering work that makes AI useful at scale.\n\nKey topics covered:\n• The difference between teams that use AI tools and teams that are truly built around AI\n• Why the same AI that works great for a small startup completely breaks down in a company with 500,000+ files\n• The kitchen analogy — why the environment around AI matters just as much as the AI itself\n• How AI agents get lost in large codebases and waste time looking in the wrong places\n• Why adding more information to AI doesn't always make it better — and can actually make things worse\n• How Atlassian built an internal scoring tool to measure how well their AI agents are actually performing\n• Why every team that owns a piece of the codebase should also be responsible for how well AI understands it\n• How Jira, Confluence, and Bitbucket now work together so AI agents can see the full picture of what's happening in an organization\n• Why writing code is no longer the hard part — and what the new bottlenecks actually are\n\nIf you're an engineer,...","thumbnail_url":"https://img.transistorcdn.com/9ucptEYd5kVPtNA_zR1pGGq70YIZjxQEgWQXPqsHHDg/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yNGQz/ZmI1YWRiYWMxMTM3/YmQyNDA4YTQ2ZTY4/N2RjNi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}