Why do so many internal enterprise AI projects stall after the demo stage?

I’ve been noticing a pattern with a lot of mid-size companies trying to build internal AI systems on top of their own data. The pilot/demo usually works. The LLM can answer a few curated questions, maybe even generate SQL. But once they try to make it reliable enough for actual business use, things slow down hard. The unexpected bottlenecks seem to be things like: * schema mapping across fragmented systems * defining what metrics actually mean across teams * query validation and auditability * handling changing schemas/connectors over time * getting consistent answers across departments * maintaining trust in outputs once real decisions depend on them A few engineering leaders I spoke to said the “AI model” part was actually the easiest layer. The infrastructure and grounding layer underneath became the real project. Curious how others here are thinking about it: * Are companies underestimating the operational complexity of production AI on enterprise data? * Does build-vs-buy start looking different once maintenance and governance are included? * Has anyone here actually seen an internal text-to-SQL / AI analytics layer scale cleanly in production? Would genuinely love to hear real experiences, especially from BFSI, SaaS, healthcare, or data-heavy teams.