AI made software easy to build. Running it is the hard part
The thin line between building and running
This is the point where discussions between business leaders and technology leaders begin to fall apart. The business sees an application that works. Technology sees an application that now needs to survive outside the protected environment in which it was created. Those are fundamentally different things.
A prototype rarely worries about things like identity management, resilience, audit trails, backup policies, API versioning, support models or regulatory obligations because none of those questions matter while an idea is still being tested. However, they become important once the organization decides that the application has graduated from an experiment to an enterprise capability. In many ways, this is the point where software engineering gives way to software operations—a discipline that organizations like Google have spent years formalising through practices such as Site Reliability Engineering (SRE).
This is the point where many organizations are beginning to underestimate the challenge. AI has democratised software development. It has not democratised enterprise operations. Running software is an entirely different discipline. It is less visible, less celebrated and considerably less exciting than building it, but it is also the reason enterprise technology exists. Every application that enters production quietly accumulates obligations. Someone has to secure it. Someone has to integrate it. Someone has to monitor it, patch it, support it and explain it to an auditor. Eventually, someone has to retire it. None of those responsibilities disappear simply because the first version happened to be created in forty-eight hours using an AI.
