For AI models to work well, the problem you're asking them to solve should be similar to problems already represented in their training data, context, and tools. Businesses spend an extraordinary amount of time creating those conditions. Data is cleaned, reports are finalized, processes are documented, and decision flows are documented as standard operating procedures. Increasingly, this institutional knowledge is placed into retrieval systems.
Fundamentally, however, there are two reasons why this is not enough.
First, the base model was not trained on your company's data. The vast majority of information on the internet is a kind of idealized, gossip-like agglomeration of public information. It does not include the highly optimized, trade-secret-like methods companies actually use to run their businesses. At best, it knows the documented case-study outcomes, not the behind-the-scenes processes that created them.
Second, any company is run by people who have much more knowledge than appears in its databases. This information is not written down. It is not available in the model's training data, nor can it simply be recreated through an injection using RAG.
In machine-learning language, the processes and knowledge your business runs on are out of distribution.
And that is a good thing. If the processes and knowledge your business runs on could be recreated from the model's training data, your business would probably have very little that distinguishes it from its competitors, whether they use AI or not.
It is therefore critical that the use of AI preserves two capabilities.
The first is to make sure that you allow humans to make decisions without having to fight with AI or prove to it that the decision is correct. You should not require a chatbot or an agent to approve a decision made by a knowledgeable human. For now, the correct decision may well be the one the AI disagrees with, precisely because the person making it knows something the model does not.
The second is to make sure that people retain the ability to sustain attention long enough to think through and solve difficult problems. And that means their work shouldn't simply be converted into writing everything down so that an agent can retrieve it, replacing the employee's judgment with simply reviewing the agent's output.
Outside work, sustained attention is already under constant attack. That attention is for sale to television, social media, and countless other forms of entertainment competing for it. Sustained attention is difficult and requires practice. For most people, work may now be the last reliable place where they are expected to focus for an extended period and apply sustained effort to solving a problem. You cannot rely on employees to get this skill elsewhere.
So unless you want your company to lose its edge, make sure to allow for sustained thought and effort, allow employees to reason and make choices, and don't force them to be proverbial boilermen shoveling knowledge into databases in the hope that the engine of AI will drive the company.