It's not hard to build an impressive GenAI demo. Point a large language model at a knowledge base, wire up a chat interface, and within a few weeks you have something that looks like it could change how a team works. The hard part is everything that happens after the demo, the part that decides whether it ever reaches production.
Three things separate a pilot that ships from one that quietly disappears. First, a defined scope of what the system is allowed to do, and just as important, what it is not allowed to do. A support assistant that can summarize a ticket is very different from one that can also close it. Vague scope is the fastest way to lose stakeholder trust the first time the system is wrong.
Second, a workflow, not a chatbot. The organizations that get real value from GenAI embed it inside an existing process, such as drafting an invoice interpretation before a human approves it, summarizing an approval chain before a manager signs off, rather than launching a stand-alone assistant and hoping people remember to use it.
Third, governance that scales with usage. Access controls, logging, and a clear escalation path for when the system gets something wrong. This sounds like overhead until the first incident, at which point it's the only thing that keeps the project alive.
None of this is about the underlying model. It's about treating GenAI as a production system from the first design conversation, not as an experiment that gets 'hardened later.' Later rarely comes.