I use both ChatGPT and Claude.

Not really as a versus thing.

I do not trust one answer just because it sounds good. Sometimes I put the same issue into both and compare where they agree, where they split, and where one catches something the other missed.

That is useful.

But it is not the same thing as judgment.

I use AI more like a second set of eyes or a thinking tool. It can help me test a thought, organize information, draft process notes, and notice gaps. It can also sound very confident while missing the point.

That is why the real value is knowing what to ask and what not to trust.

A polished answer is not the same as a right answer

This matters in financial operations.

AI can give a clean explanation. It can outline a process. It can summarize information. It can put messy thoughts into a more usable order.

But it does not know the business unless the person using it understands the business well enough to give it the right context.

It does not know whether the numbers are reliable. It does not know whether the process feeding those numbers is broken. It does not know whether a report looks right because the report is right, or because bad inputs were arranged neatly.

That is where owners can get into trouble.

The answer may read well. That does not mean I should act on it.

LLMs are a virtual library

The best way I think about large language models is simple.

They are like having every how-to book, encyclopedia, and how-to guide at your fingertips. A virtual library.

That can be powerful.

But a library does not make the decision for you. It does not understand the consequences inside your business. It does not know which parts of your process are formal, which parts are tribal knowledge, and which parts only work because one person remembers to do them every month.

The tool can help.

The power still comes from the person using it.

AI does not fix a broken process by itself

I understand why some owners are cautious.

There is too much hype around AI. Too many people talk like it can fix every problem if you just add it to the business.

That is not how real operations work.

If the process is broken, AI can help you move a broken process faster.

If the data is bad, AI can organize bad data into a cleaner-looking answer.

If nobody understands where the work is getting stuck, AI does not magically create accountability.

Sometimes the right answer is not AI.

Sometimes the right answer is a cleaner workflow, better controls, a more reliable close process, or a system that connects the work before anyone tries to automate it.

That is the part I care about.

I use AI when it supports the work

In my work, AI is useful when it helps me think through a process, compare options, tighten documentation, or look for assumptions I may have missed.

I still have to know what I am looking at.

I still have to understand the numbers, the workflow, the people involved, and the risk if the answer is wrong.

AI can support that work. It cannot take responsibility for it.

I am not interested in using AI just to say I used AI. I am interested in whether it helps build a financial operating system that actually works.

If AI helps with that, I will use it.

If it does not, I will not force it.

A report tells you what happened. A system changes what happens next. AI can support the system, but it should not be mistaken for the system.

If your back office is being held together by manual work, disconnected tools, or reports nobody fully trusts, start with the process. Find what is actually broken before adding another tool.

If this sounds familiar, the issue usually isn’t the work — it’s how the system is built. And that doesn’t fix itself.

Start with the Pre-Call Fit Check so we can determine whether a conversation makes sense.