You'd be shocked and amazed how poorly designed and managed many gov. databases are. Many of these databases follow this pattern: 1) Some worker is fed up with

You'd be shocked and amazed how poorly designed and managed many gov. databases are. Many of these databases follow this pattern: 1) Some worker is fed up with having to sift through hundreds of excel spreadsheets in order to find the information need, and the nightmare of keeping such spreadsheets updated 2) Said worker deploys a small database - just for their own use. After some time the DB gets more users, who make their own views and what not. The database and tables within may or may not follow some rules. Heck, maybe the worker that created it was learning as they went on. 3) Said worker quits or gets a new job, and the database is essentially unmanaged. Some other worker might not now about that DB, and you go back to step 1) Now multiply a database like that with 100, and span it over 20 year. You get this unimaginable spaghetti monster of multiple DBs, some written in one dialect, some in others. Some are completely unnormalized, others a high degree of normalization. And to build a report, you may need to access tables from numerous such databases. Even seasoned analysts dread starting on the reports, because they'll spend a good day just to find the right dbs, tables, and all the errors. So of course when the directors hear about this new fangled AI magic that just spits out the results when you ask it it plain English, they immediately order a use/benefit analysis on it. Ok, so that may be a bit harsh - but that's the reality of many agencies. Dogshit DB management, and being 20 year behind the digitalization revolution. But yes, a problem would of course be: How do you know that the data the LLM returns is true? Is it conjuring up fake data? Does it process/calculate data as you want it to?