AI at Work: Practical, Safe Tools for Monument Builders

Okay, so Dad, I'm going to go off script for a little bit, so please stay with me.

I hope you can.

So what I think I hear you're saying is that there's a certain amount of teaching that I, as an AI user, will need to do so AI will work for me.

Is that what you're saying?

A hundred percent.

So kind of imagine the first time that you set up ChatGPT or Cloud or Copilot, Gemini, whatever your tool is, that is an employee or an assistant for you coming to work the very first day.

But they also have no access to any information yet.

They don't have company history, your target audience, examples of what good looks like to you.

So if you didn't give a human access to that stuff, how could you ever think that they could create good output for you or things that sound like you? Look at AI the same way.

I think, understandably, folks will see things either in the news or on a show and just kind of expect they can say, computer, make me this perfect thing, enter.

And it just come out in a one-shot, perfect, ready-to-go way.

And that's kind of a quick way to set yourself up for disappointment, because just like an employee, it's not going to be perfect the first day, the first week, or even the first month.

It can take a while to get that kind of onboarded.

But once it is, then it can consistently, again, maybe it's never 100% every single time, but neither are humans.

If you can get something to be at 80 or 90%, and then you have to fill in the blanks with your own human brain, you still have to work.

you can still save a ton of time on that kind of initial grunt work or output or whatever that kind of content might

be.

Okay.

But I mean, there's already mountains and mountains of information on this internet, this wide world web, right? Why isn't that information enough? Why do we need to...

do our own teaching? That's a great question.

So think of it, think of these kind of base models or the base kind of setup of these things like a generalist.

It has access to, like you said, lots of information and lots of different processes, lots of different of what good looks like.

But your organization might have a very different view set or policy on like what good looks like, what your, who your target audience is, Your tone of voice, colors, fonts that you should use.

You can train these systems on all of that information.

So when output comes out, it looks like somebody in your organization actually created it.