Most AI proofs of concept “work” in their frame. A few tidy documents, a filtered dataset, a demo in front of a convinced committee. Then the project stops. It is not always a budget issue. Often no one named, from day one, what must remain true for the same system to survive outside the meeting room.
Five blockers that reinforce each other
Five blockers show up with tiring regularity. The first is budgetary but poorly framed. The experiment is funded, operations rarely are: monitoring, error recovery, business support. The second is data, quality, access rights, freshness. The third is ownership: who owns the decision when the model is wrong. The fourth is IT integration, authentication, writes, real workflows. The fifth is maintenance, with drift in prompts, sources, and thresholds. Missing any one of them is enough to turn a successful demo into debt.
These blockers reinforce each other. Hard-to-reach data leads to a frozen export. The frozen export makes maintenance opaque. Missing business ownership leaves errors without arbitration. A budget sized only for the demo phase does not fund the time needed to leave that loop. Teams then assume they need a better model, when they mostly need a run frame. The same diagnosis appears in our notes on AI agents and human control.
What separates projects that ship
What separates projects that ship is not a more powerful model. It is ordinary discipline: write the scope, the PoC exit criteria, the production metrics, and the business owner before opening the IDE. When those are missing, extending the PoC mostly widens the failure surface. When they are present, even a limited first version can enter production without falling apart at the first incident. The AI Solution Factory approach formalizes that discipline: connect, industrialize, govern.
Treating the PoC as an instrumented step changes the conversation. The question is no longer only whether the demo impresses. It is what must still be true in six months for the system to remain operable. That question is less flattering. It also avoids funding a next phase that still has no clear path. These criteria also meet our notes on AI and agents and the solutions catalog, as soon as the topic becomes a real integration workstream.
