Hi, I'm Miriam from Denmark and I am here to learn about workflows, automation and prompt engineering as a documentation specialist. I have worked with ChatGPT, Claude, Deepseek and checking out Gemini. Look forward to learning how one can optimize AI to make work more effective, efficient and productive. Great to see you all. Look forward to this!

A lot of people say they want teams to adopt AI faster, but many of the social signals around AI make adoption harder. The tool gets framed like a test of who is innovative, who is behind, who “gets it,” and who does not. Once that happens, people stop approaching AI as a workflow tool and start experiencing it as a referendum on their ability. That shift creates delay. It adds pressure where curiosity should be. It turns simple experimentation into a performance moment. And it makes the learning curve feel more personal than practical. If we want AI adoption to move faster and create real time savings, we need to stop treating it like a talent test and start treating it like what it actually is, a way to reduce friction in the work. ------------- Performance pressure slows practical learning ------------- When a new tool enters the workplace, people do not respond only to the tool itself. They also respond to the culture around it. If the unspoken message is that capable people should already know how to use AI well, then anyone who feels uncertain is likely to hide that uncertainty instead of working through it. That is where time starts to get lost. Instead of asking basic questions, people stay quiet. Instead of testing a small use case, they wait until they feel more confident. Instead of learning in public through normal trial and error, they try to avoid looking inexperienced. This is a common pattern in high-performing environments. People are comfortable being competent, not visibly early. So when AI becomes tied to status, speed of adoption often slows down. The people who most want to avoid wasting time end up spending even more time observing, second-guessing, and delaying the first useful experiments. The irony is that AI does not usually become valuable through image management. It becomes valuable through repeated practical use. And practical use gets harder whenever people feel like they are being evaluated instead of learning. ------------- AI is not proving who is smart, it is revealing where work is inefficient -------------