Working With AI Feels More Like Leadership Than Coding

Working with AI is less predictable than traditional software. That makes context, clarity, and feedback more valuable.

For most of my career, code gave me certainty. A program did what its instructions told it to do. If the same input produced a different result, we called it a bug.

People were never like that. As a leader, I can explain a task and get exactly what I asked for. I can also get something better because a colleague understood the intent behind the request. Sometimes the result shows that I was not as clear as I thought.

Working with AI feels closer to the second experience.

AI runs on software, but working with it is not fully predictable. The same request can produce a different answer. It can make a useful connection, miss an obvious point, or surprise me with an approach I had not considered.

This is frustrating when I treat AI like a compiler. It becomes more useful when I treat the interaction as a form of collaboration.

That does not make AI a person. It has no lived experience, accountability, or human judgment. The comparison is about how we work. Good leaders do more than issue instructions. They share context, explain the desired outcome, set boundaries, and respond to what comes back.

The same habits improve my work with AI. A good prompt helps, but a shared working context helps more. Examples, corrections, and reusable instructions reduce misunderstandings. Over time, the system becomes better aligned with how I think and what I need from it.

The investment is not in pretending that AI is human. It is in becoming better at expressing intent.

We spent years learning how to tell computers exactly what to do. Now we also need to explain why the work matters, what a good result looks like, and where judgment is needed.

For me, that is the shift. AI is making software work less like issuing commands to a machine and more like leading through a conversation. The technology is new. The leadership skills are not.