  Large language models (LLMs) know little about enterprise database tables in
the private data ecosystem, which substantially differ from web text in
structure and content. As LLMs' performance is tied to their training data, a
crucial question is how useful they can be in improving enterprise database
management and analysis tasks. To address this, we contribute experimental
results on LLMs' performance for text-to-SQL and semantic column-type detection
tasks on enterprise datasets. The performance of LLMs on enterprise data is
significantly lower than on benchmark datasets commonly used. Informed by our
findings and feedback from industry practitioners, we identify three
fundamental challenges -- latency, cost, and quality -- and propose potential
solutions to use LLMs in enterprise data workflows effectively.
