The top use case I've been hearing is in legal discovery. Law firms used to play games with diligence by disclosing TBs of email and making it cost prohibitive to find relevant emails. This task would normally require a $60-100/hr paralegal or lawyer.
GPT-4 can do that task for fractions of a penny per email now. It doesn't have to be perfect if its competing with nothing. I expect we'll see similar shops for any other high cost paper/trail business.
Is there a solution to the issue of data stewardship yet? I'd imagine it typically would not be permissible to send a bunch of proprietary legal documents off to OpenAI.
What I'd really love to implement is a way for GPT-4 to answer questions based on a corpus of "all our Confluence pages plus random other sources of documentation." Like with the legal document issue, it's a bit of a nonstarter right now given the proprietary nature of corporate documentation.
AFAIK the Azure APIs provide suitable data usage requirements. One of the most fascinating aspects of the AI world is that we've made extraordinarily expensive brute force search a valuable tool.
GPT-4 can do that task for fractions of a penny per email now. It doesn't have to be perfect if its competing with nothing. I expect we'll see similar shops for any other high cost paper/trail business.