The organizational reality is often grimmer than the technical possibility. Despite decades of progress, many agencies operate on fragmented data ecosystems and reactive maintenance paradigms. Asset information scattered across inspection notes, sensor logs, and archival records rarely feeds into coherent longitudinal analysis~\cite{jaberiEvaluationDigitalCapabilities2025}. Digital tools, including much discussed digital twins, have not fully closed this gap. Data acquisition has been prioritized over decision integration; implementations stumble over inconsistent standards, limited validation, and unclear governance. The result is a proliferation of methods without a principled framework for comparing them or justifying their adoption

Here's the thing - what actually happens in organizations is way messier than what's technically possible. Even after decades of improvements, tons of agencies are still stuck with scattered data systems and just fixing things when they break. You've got asset info spread all over the place - some in inspection notes, some in sensor data, some buried in old records - and it almost never gets pulled together for any real long-term analysis. Even those digital twins that everyone keeps talking about haven't really solved this problem. The focus has been way too much on just collecting data instead of actually using it to make better decisions. Projects keep falling flat because standards don't match up, nobody's validating the data properly, and there's no clear plan for who's in charge of what. What you end up with is a bunch of different approaches floating around with no good way to compare them or figure out which ones are actually worth using.