Do Legacy Organizations/Government Have More AI Talent Than AI Problems?

Has anyone else seen this, especially in government, large legacy companies, or places where software isn't really the business?

It feels like every AI discussion starts at 100 mph. Instead of asking "what is the simplest way to solve this problem?" the conversation immediately jumps to RAG, agent frameworks, vector databases, and whatever the latest LLM trend is. The data is still a mess. Some of it is in Excel, some is stuck in systems that don't talk to each other, business rules are undocumented, and people still argue about which dataset is the source of truth.

Before talking about autonomous agents and complex AI systems, shouldn't we first be able to answer basic questions? Where does the data come from? Who owns it? Is it accurate? Can we reproduce the numbers?

I don't think this is because engineers or data scientists aren't capable. Many of these people are talented and could solve very difficult problems. The issue is that many organizations simply don't have problems that require this level of AI sophistication yet.

If you are hired as the AI person or brought in to lead AI initiatives, there is an expectation that you need to show AI value. Walking into a meeting and saying "we need better data governance, cleaner pipelines, and better documentation" may be the right answer, but it doesn't always justify the position, budget, or the expectations built around the role. Maybe this is just my observation, but it feels like a lot of talent is being wasted . Has anyone else seen this pattern in their organizations?