These days, there is no shortage of information about how to use AI.
Everywhere we look, people are talking about prompts, tools, agents, workflows, automation, and productivity hacks. New platforms appear almost every week. New techniques are shared constantly. There are guides, videos, courses, newsletters, and endless examples showing us how to use AI more effectively.
But the more I think about it, the more I feel that something important is often missing.
The real question is not simply how to use AI.
The real question is: what should I do with it?
Learning how to use AI is important, of course. We need to understand the tools. We need to know what they can and cannot do. We need to learn how to ask better questions, how to structure our thoughts, and how to turn vague ideas into useful outputs.
But knowing how to use a tool is not the same as knowing what to build with it.
A hammer does not tell us what kind of house to build. A camera does not tell us what is worth photographing. A notebook does not tell us what thoughts are worth recording. In the same way, AI does not automatically tell us what is worth creating, improving, questioning, or discovering.
That part still belongs to us.
This is where personal experience becomes important.
A person with no real problem may see AI as a toy, a shortcut, or a search engine with better language. But a person who has lived through problems, worked through complexity, made mistakes, solved issues, and observed repeated patterns can use AI in a much deeper way.
Experience gives us better questions.
Someone who has worked with databases for many years may not simply ask AI, “How do I write this SQL query?” They may ask, “Why does this kind of data problem keep happening?” or “Can this repeated judgment be turned into a checklist?” or “How can I make this process easier for the next person?”
Someone who has worked inside an organization may not simply ask AI to write an email. They may ask, “Where is the real confusion in this process?” or “What decision framework would help people avoid wasting time?” or “How can we make this invisible knowledge visible?”
This is the real power of AI.
AI is not only a tool for producing text or code. It is also a tool for discovering patterns, clarifying judgment, and turning experience into something reusable.
Many people spend a great deal of time and energy making decisions. They decide what is urgent, what is important, what can wait, what should be automated, what should be ignored, what should be escalated, and what should be studied more deeply.
These decisions often feel small, but they consume enormous mental energy. In many cases, the work itself is not the hardest part. The harder part is deciding what the work actually is.
This is why decision frameworks may be one of the most valuable things we can create with AI.
A good decision framework helps us turn vague judgment into clear criteria. It does not remove human responsibility. It does not make decisions for us blindly. Instead, it gives structure to our thinking.
For example, before automating a task, we might ask: How often does this task repeat? How much time does it take? How costly are mistakes? Are the inputs and outputs clear? Are there too many exceptions? Who will maintain the automation later?
These questions may seem simple, but they save time. They reduce confusion. They help people think better.
In this sense, AI does not replace experience. It helps us extract value from experience.
A person’s years of work, frustration, observation, and problem-solving can become checklists, diagrams, workflows, training materials, small tools, or essays. What was once only intuition inside one person’s head can become something visible and shareable.
That is a powerful change.
This also changes how I think about creativity.
Creativity in the age of AI is not always about inventing something completely new. Often, it is about connecting things that were already there: past experience, repeated problems, personal curiosity, and new tools.
AI can amplify these connections, but it cannot replace the act of noticing.
We still have to notice what bothers us. We still have to notice what keeps repeating. We still have to notice where people waste time. We still have to notice what we understand deeply but have never explained clearly.
That act of noticing may be the beginning of real AI use.
So perhaps the best way to use AI is not to start with the tool.
It is to start with discovery.
What problem have I seen again and again?
What judgment do I make repeatedly?
What experience do I have that others may not have?
What small value can I create from what I already know?
These questions matter more than any prompt template.
AI usage methods are everywhere.
The real challenge is discovering what I should do with it.
And that discovery comes from the life I have lived.
In the end, AI may not simply be a machine that gives us answers.
It may be a mirror that helps us see the value hidden inside our own experience.