Every few weeks I talk to a company that already "tried AI." There's a proof-of-concept somewhere — a chatbot that answered questions in a meeting once, a script someone's nephew built, a ChatGPT workflow that lived in one enthusiastic employee's browser. It impressed people for a day and then quietly died.
The pattern is so consistent that I've stopped believing it's a technology problem. Here's what actually kills these projects:
1. The demo answered the wrong question
A demo proves that the model can do the task. Production requires that it does the task on the worst day — the ambiguous email, the scanned PDF at 45 degrees, the customer who writes in dialect. If nobody tested the ugly 20% of real inputs, the first bad output erodes trust, and trust never comes back.
2. Nobody owned the workflow, so nobody owned the automation
An automation that saves the support team 10 hours a week still dies if no specific person is responsible for it when it misroutes a ticket. Software needs an owner; a demo doesn't. That difference only becomes visible after go-live.
3. It replaced a tool instead of removing work
The graveyard is full of "AI portals" that asked employees to go somewhere new. The automations that survive are invisible: they live inside the inbox, the CRM, the phone system people already use. If adoption requires a training session, the design is wrong.
4. No one budgeted for month two
Models change, prompts drift, edge cases accumulate. A workflow that ran at 95% in March runs at 80% in June if nobody is watching. The maintenance isn't expensive — but it has to exist, and almost no POC plans for it.
None of this is fixed by a better model. It's fixed by scoping smaller (one workflow, end to end), testing on real data before believing anything, wiring into existing tools, and treating go-live as the start of the work rather than the end.
That's the entire reason modernagents sells a fixed-scope sprint and a boring monthly retainer instead of "AI transformation." The demo is the easy part.