Munich's industrial and financial concentration produces four fairly distinct kinds of request, and they behave differently enough that it is worth naming them separately.
Automotive and industrial groups arrive with maintenance, quality or supplier problems and a data estate that spans four decades of acquisitions. The engineering that pays for itself is usually unglamorous: getting sensor histories, work orders and inspection records into one place where a question can be asked across all three. Once that exists, prediction is comparatively easy. Before it exists, no model helps. Both halves are described under legacy modernisation and predictive maintenance.
Insurers and reinsurers arrive with documents — claims files, treaty wordings, loss reports, medical evidence — and a process where a person reads for an hour and then makes a decision that has to be defensible years later. The valuable system does not make the decision. It assembles the file, cites where each fact came from, and leaves the judgement with the underwriter, which is also the only version a regulator is comfortable with.
Aerospace and semiconductor suppliers arrive with material they cannot let out of their own network, sometimes for contractual reasons and sometimes for export-control ones. That resolves to a deployment inside their own perimeter rather than a hosted endpoint, which we cover under on-premise AI.
Everyone eventually asks the same question about scope: whether the pilot that worked on one line will work on eleven. It is the right question, and the honest answer usually depends on whether the eleven lines record data the same way. They rarely do.
