Embracing AI: How I Use It to Learn, Not to Shortcut

AI is a tool. Like any tool, it can be used well or poorly. A lot of people use it to replace their critical thinking. I use it to sharpen mine.

That distinction is the entire point of this article.

My Rule

Before I bring AI into any problem, I have to have already tried to solve it myself. Not glanced at it. Actually tried. Iterated. Hit a wall. Figured out why it is not working. Only after I have genuinely exhausted my own thinking do I bring in the assist.

And when I do, I do not just paste in the problem and ask for an answer. I treat AI like a mentor I have earned the right to ask. I explain what I am trying to accomplish, what I have already tried, and why it is not working. Then I ask for suggestions and for an explanation of what the solution does and why it works. Because if I cannot explain it afterward, I have not learned anything.

The goal is never to get the answer. The goal is to become someone who understands the answer.

What That Looks Like in Practice

During the development of my West Region Operations Quiz Analysis, I needed a formula that would count participation per cafe, but only if the response came from an Ambassador and only if the score field was not empty.

I already knew COUNTIF. I had iterated on my own formula multiple times. It kept breaking down when I tried to add the second condition. So I brought in AI as my mentor. I explained what I was trying to do, what I had already tried, and exactly where it was failing.

The suggestion was COUNTIFS a cleaner, more flexible version of what I was already using that could handle multiple conditions simultaneously.

=COUNTIFS(Responses!A:A, A2, Responses!C:C, "Ambassador", Responses!E:E, "<>")

But I did not just copy it. I asked for an explanation of each argument, why the syntax worked the way it did, and how I could adapt it in the future. That conversation turned a formula fix into a skill I now own.

That is the difference between using AI as a shortcut and using it as a learning tool.

Other Ways I Use AI in My Workflow

As a clarification partner. When a number does not add up the way I expect or a visualization is telling me something I cannot immediately explain, I talk it through with AI. Describing what I see and what I expected to see helps me identify where my logic broke down. The act of explaining the problem out loud, even to an AI, often surfaces the answer on its own.

As a learning accelerator for SQL. As I continue building my SQL skills, I use AI the same way I use it for Excel. I bring what I have tried, explain where it fails, ask for the fix, and then ask for the explanation. Every session is a lesson.

As an aesthetic collaborator. Data visualization is not just about accuracy. It is about clarity and communication. I use AI to think through chart design, color choices, and layout in ways that make data easier to read and more visually compelling. The analysis is mine. The presentation gets better with input.

What AI Cannot Do

It cannot replace the thinking that happens before you bring it in.

The frustration of iteration, the moment where something almost works and you have to figure out why it does not, that is where real learning happens. Shortcuts through that process are shortcuts away from growth.

The analysts and researchers who will use AI most effectively are not the ones who use it the most. They are the ones who know exactly when to use it, what to ask, and how to learn from the answer.

I am building toward that. And every time I bring AI in as a mentor rather than a replacement, I get a little closer.