Episode 005: Teaching the System to Work for My Needs

By the time I finished building the framework behind the JW Designs Business OS, I had already learned something important. Giving AI access to more information was not enough, and giving it structure was not enough either. The framework helped define where information belonged, which department owned what, which records were authoritative, how decisions should move through the business, and where AI should stop and ask for help instead of guessing.

That was a big step forward, but it exposed another problem.

The system now had structure.

It still did not always know how I wanted it to behave inside that structure.

That turned out to be a completely different challenge.

A System Can Be Correct and Still Be Annoying

One of the stranger things about working with AI is that an answer can be technically reasonable and still be completely wrong for the way you want to work.

That happened constantly in the early stages.

I would ask a department a simple question and get a perfectly logical recommendation that involved three other departments, two follow-up decisions, and a bunch of extra administrative steps.

Nothing in the answer was necessarily incorrect.

It was just too much.

That was not what I wanted.

So I started teaching it something that is obvious to a person but surprisingly difficult to define for AI: just because something could be done does not mean it should be done.

That sounds simple.

In practice, it affected almost everything.

Teaching It When to Stay Put

The department structure created another unexpected problem.

Once the system understood that JW Designs had different departments, it became very eager to use them.

A topic might touch Sales & Marketing, Operations, Engineering, and Business Development, and the AI would sometimes see all four and decide all four needed to be involved.

But that is not how I run the business.

If the work clearly belongs in one department, I want it to stay there. Engineering does not need a transfer simply because a marketing conversation mentions a product feature. Operations does not need a transfer because Engineering discusses manufacturability. Business Development does not need to become involved every time someone has an idea.

And a department definitely does not need to create a transfer back to itself.

That actually happened.

It was a perfect example of something that made sense to the AI at a surface level and absolutely no sense operationally.

The system understood the concept of routing.

It did not yet understand restraint.

So restraint became part of the framework.

Before recommending a transfer, the system needed to identify the active department, decide whether the work already belonged there, and only create a handoff if another department had a genuinely distinct action.

That one change removed a surprising amount of friction.

The goal was never to build the most elaborate organization possible.

The goal was to make the business easier to operate.

I Did Not Want Another Employee to Manage

This became the test I kept coming back to.

If I had to constantly manage the AI, correct its workflow, tell it where things belonged, and explain the same operating preferences over and over again, then I had not reduced my workload.

I had just created another employee I had to supervise.

I wanted the system to become useful enough that I could give it a problem and trust it to handle the obvious parts without dragging me into every decision.

That meant teaching it when to act and when to ask.

Those are very different things.

There are decisions I want to make myself: pricing changes, major product decisions, significant spending commitments, policy changes, or anything irreversible or high risk.

But there are also hundreds of smaller decisions I do not want escalated back to me.

If there is an obvious next step and the system has enough information to take it, I want it to take it. If a department already owns the work, I want it to continue. If there is no real decision for me to make, I do not want a fake one manufactured just because the AI likes asking questions.

That became another operating lesson.

AI often tries to be helpful by asking for confirmation.

Too much confirmation becomes friction.

Useful Does Not Mean Maximum Detail

I also had to teach the system something about how I process information.

More detail is not always more useful, especially at the executive level.

If I am looking at the CEO Dashboard, I do not need a transcript of everything every department did.

I need to know what changed.

What matters.

What is at risk.

What needs a decision.

What can wait.

The same information might need to be presented very differently depending on where I am working. Engineering can be detailed. Operations can be procedural. Sales & Marketing can be conversational. The CEO Dashboard needs to stay at executive altitude.

At first that sounds like a formatting preference, but it is more than that.

It changes how useful the information actually is.

The system needed to learn that the longest or most complete answer was not automatically the best answer.

Sometimes useful means less.

Ideas Are Not Decisions

Another recurring problem was the difference between discussing something and approving it.

This matters a lot in a business because conversations naturally contain possibilities.

I might say, “That could be interesting.”

Or, “We should look at that.”

Or, “I wonder if there is something there.”

None of those statements mean, “We are doing this.”

But AI is very good at turning conversational momentum into apparent certainty.

An idea mentioned casually in one session could come back later sounding like a project. A possibility could turn into a recommendation. A recommendation could start looking like a decision.

So the system needed another rule:

Separate ideas, assumptions, tests, recommendations, and approved decisions.

Do not blur them together.

That distinction became even more important as the Business OS started remembering more information.

A bad memory is not just forgetting something.

Sometimes it is remembering something too confidently.

Finding Information Is Not the Same as Knowing It Is Current

That became one of the biggest lessons.

Once I started building better retrieval into the Business OS, the system became much better at finding information.

But finding information introduced another problem.

Old information does not stop existing just because it becomes outdated.

A previous chat might say an action is still open. A current-state record might say it is complete. A permanent standard might still be valid even though the working conversation around it changed weeks ago.

The system had to learn that retrieval was only the first step.

It also had to understand authority and freshness.

What is the source of truth?

Which record is newer?

Is this a permanent standard or temporary operating state?

Was this decision approved or merely discussed?

That changed the way I thought about AI memory entirely.

I did not want the system to remember everything.

I wanted it to understand what kind of information it was remembering.

Correcting the Answer Was Not Enough

This was probably the biggest shift in how I worked with the system.

When something went wrong, I stopped asking only:

“How do I fix this answer?”

I started asking:

“Why did the system produce this answer in the first place?”

That is a completely different problem.

If a department created an unnecessary transfer, I could delete the transfer, or I could change the routing rule.

If the system kept escalating trivial decisions back to me, I could keep answering the questions, or I could redefine when executive guidance was actually required.

If old information kept appearing as current, I could correct it every time, or I could improve the source hierarchy and current-state records.

That became the real learning loop.

A problem would appear. I would observe what caused it. We would make a correction, turn that correction into a rule, and then test it again during real work.

Sometimes the fix worked.

Sometimes it exposed another problem.

Then we adjusted again.

The Business OS was not something I designed perfectly and switched on.

It was taught through use.

Personalization Is More Than Remembering Facts About Me

This is where I think the idea of AI personalization gets misunderstood.

Most people hear personalization and think about facts: your name, your preferences, your business, your writing style, your favorite tools.

Those things matter.

But for me, the more valuable form of personalization became behavioral.

How much detail do I want?

When should the system ask me a question?

When should it make a reasonable decision on its own?

What counts as a real escalation?

What kind of friction am I willing to tolerate?

When should something stay inside the current department?

When does a conversation become an actual decision?

What does “good enough” look like?

Those are not really facts about me.

They are operating expectations.

The more of those expectations I could define, the more the system began to fit the way I actually ran the business.

Not perfectly.

But better.

And better was enough to keep building.

The System Started Getting Out of the Way

That is probably the best way to describe the improvement.

The Business OS became more useful when I noticed it less.

I should not be thinking about routing rules while I am trying to solve an engineering problem. I should not be maintaining organizational structure while I am talking through a marketing idea. I should not have to remember where every piece of information belongs.

The system should handle as much of that overhead as possible.

That was the entire reason I started building it.

Not to create an elaborate AI organization.

Not to automate everything.

Not to remove myself from the business.

I wanted to spend less mental energy remembering, routing, organizing, correcting, and re-explaining things so I could spend more energy actually running the company.

Teaching the system how I wanted it to work moved me much closer to that.

But then another problem started becoming impossible to ignore.

I had spent a lot of time teaching the system how to behave. I had built rules around ownership, decisions, retrieval, communication, and current state. It was finally starting to understand how I wanted the business to operate.

There was just one problem.

And if the conversation that taught the system all of those things eventually ends, what happens to everything it learned?

That became the next problem I had to solve.