Maybe build products that customers actually
want and not seek what problem you can solve with whatever hype bandwagon you got yourself onto?
Otherwise it is trying to convince the customer that they are idiots and didn't drink enough of the hype cool-aid. Not a good start for building a business and a customer relationship, IMO.
>The ultimate application for an enterprise inevitably involves access to all the internal data for the AI to do its magic
That's complete nonsense in more ways than one. It is the same hype BS as "web3", "blockchain" and other fads people tried to sell before. It completely ignores what the technology actually can and cannot do and what are the sensible applications of it. And also totally ignores what the customers actually need to get work done.
Feeding all internal data to a 3rdparty AI (presumably LLM) provider is a complete non-starter, no matter what kind of resulting pie in the sky handwavy magic you will try to sell to anyone.
Would you feed your employee data there? Your payroll? Your customer information? With the unknown risks that it could be regurgitated to your competitors, made public or errors introduced due to hallucinations?
Probably not, right? Why do you think other companies should be convinced to do this?
And that doesn't touch legal restrictions, with laws outright prohibiting the company from doing this in many cases - e.g. employment data are highly confidential and heavily protected/regulated in Europe. The same applies to confidential customer data that is typically heavily NDAed.
>Which is stupid because they already have all their data, like Confluence, Notion, Slack, Zendesk, etc., on external servers.
There is a big difference in having e.g. HR data and something like developer chat/OPs or customer support in the cloud. Ask your lawyer, they will explain it to you right away.
In addition, all these cloud contracts come with heavy heavy liability language where the cloud provider guarantees security and confidentiality - or they get their asses hauled to court immediately should anything go wrong. Are you able to do that as a startup? Likely not.
Sensible LLM applications are highly customized on premises systems for querying locally generated and locally available information. If you can't build or sell that - well, sucks to be you?
Or look for other, more meaningful, applications of AI - classification problems, defect detection, outlier detection, language translations, speech recognition, etc. That is what neural networks are actually good at and what can be sensibly deployed.
Obviously, all the above applies if you are trying to build a business that will actually deliver some value to the customers and not only to the shareholders/founders/VCs financing until the company is sold off. If you are trying to do the latter then investors love meaningless buzzword salads and the latest hype.