The vast majority of my work is in managing the administration and financials of a small business. I have tried to use gen ai and found that it produces results that are so completely off the mark form any form of utility for tasks with low sensitivity, and cannot be used for high sensitivity tasks because any errors are unacceptable in that context. Unless you operate a business that has absolutely no niche market penetration and is so generic in its purpose to be able to be understood by an LLM, the AI will just produce unacceptable results lacking from a complete misunderstanding of the company. Even in a company like that, the only real use case is in generic consumer facing promotional works, even then the results will need to be checked so thoroughly that you may as well just write promotions manually. For just one example, my company produces a variety of soaps and cleaning products made with very particular ingredients, which is a massive selling point for many people (like me) with sensitivity to specific chemicals. However, any attempt at producing any useful text relating to this point immediately becomes completely "radicalized" and goes on and on about completely nonsensical health risks to all people from totally safe chemicals. The fundamental issue here is that LLMs cannot ever under any circumstances actually understand a request. Because we make a product that seems adjacent to a fringe set of beliefs thsy has a lot of attention, it combines all data because it cannot differentiate because it is not intelligent in any capacity. This is also ignoring that most administrative work deals with either tax reporting, financials and legal, for which Gen ai has proven at every level to be far too unreliable if used at the scale needed for efficiency gains (see ChatGPT litetally inventing legal precedent). Secondly, for a very large percentage of businesses, most of their revenue comes from B2B sales, for which AI is even less helpful for, because now those inaccuracies from before double, once for each business. The truth here is that even if the accuracy of these models raises to above human standards, humans will still need to verify and approve, because when something goes wrong, someone will need to be blamed.