Lukas Paul Streiff | AI, Office & EAM in Practice

 AI discussions often focus on capabilities, models, and performance. In practice, most problems arise much earlier — at the question of ownership. 1. Ownership Is the Missing Layer Most AI initiatives fail long before model quality or system performance becomes an issue. They fail at a much more basic level: no one is clearly responsible for what the system does once it leaves the prototype stage. Ownership is the missing layer between “it works” and “it works in production.” Without it, even technically sound solutions slowly drift into ambiguity, risk, and neglect. 2. When Everyone Can Build, No One Owns AI dramatically lowers the barrier to building things. Suddenly, creating a script, a workflow, or a small application feels trivial. The effort shifts from planning and coordination to execution. The unintended side effect is subtle but dangerous: when everyone can build, responsibility becomes diffused. What used to require explicit ownership now often lives in personal fo...

 Intro ChatGPT is the new “Doctor Google” — just for tools and code. 1. The Familiar Misunderstanding Since ChatGPT became available to everyone, I keep seeing a familiar misunderstanding: “If I can ask it, I can just build it myself.” The assumption is simple: if an AI can generate code, scripts, or logic on demand, the step from idea to production suddenly feels trivial. But access to information has never been the same as experience, responsibility, or accountability. 2. We Have Seen This Before: Doctor Google This is not a new phenomenon. Years ago, the same pattern appeared with “Doctor Google.” Suddenly, medical information was available to everyone. Symptoms, diagnoses, treatment options — all just a search away. What did not suddenly appear was medical training, responsibility, or the consequences of getting things wrong. Information became accessible. Experience did not. 3. The Pattern Reappears in Companies Today, the same pattern shows up inside organizations. “We just n...