The learnings in this blog post are based on the session, “How Mercedes turns Teamwork Graph into an AI advantage”, presented at Atlassian’s Team ’26 conference. You can check out this session and others on demand. Foundation models have become astonishingly capable over a very short period. At the beginning of this decade, they could barely look up facts online. Today, they can produce work that would take a human engineer more than an hour, and the pace of improvement has been exponential, especially over the last 18 months. And yet, something strange is happening. When you ask individual users whether AI tools have helped them, more than 50% say “yes”, that they’ve actually saved time and effort. But when you zoom out and ask companies whether they’ve seen dramatic improvements to their business, 96% report that they have not. The technology is there, but unlocking the enterprise-level value has been stubbornly difficult. Now, Mercedes-Benz’s recent experience proves the harder enterprise challenge is not whether AI can generate output; it’s whether that output can connect to the messy reality of work across tools, teams, data, and decisions. The real bottleneck isn’t the model Work inside organizations depends on collaboration, so when enterprise AI treats it like an individual activity, it will always fall short of its promise. The average large enterprise runs over 367 SaaS applications, since often multiple teams use their own tools and follow their own rituals. When teams need to collaborate, people find themselves navigating unfamiliar interfaces, reconciling different planning cycles, and chasing down context scattered across a dozen systems. Goals developed in isolation can leave teams rowing in opposite directions, even when everyone has the best intentions. “You have to use at least 11-plus tools to accomplish a task, or even more — like, I don’t