Interns used to be known for their busy work, from grabbing coffee to sitting in on meetings. Now, we’re producing real work, and we’re doing it faster than ever because AI can support research, synthesis, and even build out decks in a fraction of the time it used to take.

I have seen that shift happen in real time, and it’s made me way more capable than I ever expected as an intern. But it’s also made me step back and ask if I’m actually learning how to think, or just getting really good at executing.

When I first started my internship with CourtAvenue, AI was more of a support tool we used periodically for grouping survey responses, pulling out verbatims, or summarizing information. There were a few AI tools built into the existing workflows, but they weren’t the majority of how work got done.

Now, AI is a regular part of how I approach my work. It helps with early stage thinking, structuring decks, and getting to a strong starting point more efficiently. It also plays a role in clarifying feedback and connecting ideas, especially when working through more complex thoughts. That said, it doesn’t replace asking questions or learning from the people around me. Instead, it helps me come into those conversations with more context and clearer thinking. As a result, it’s made it easier to contribute in a meaningful way and feel more confident in the work I’m putting forward.

But at the same time, I understand the concern. When things move this fast, it’s easy to rely on the output without fully understanding the input or the implications. If AI can help you get to a strong answer quickly, there’s always the risk that you skip over the deeper thinking that actually gets you there. The concern isn’t just about speed, it’s about losing the process behind it.

That’s where the question comes in: are we actually learning how to think through problems, or just getting really good at using AI to execute them?

In practice, AI shows up across how I approach both building and evaluating work. Tools like ChatGPT, Claude, and Gemini help with structuring ideas and getting to a clear starting point faster. They make it easier to map out thinking and refine it before anything is finalized.

At the same time, platforms like Vizit, an AI platform that predicts and analyzes consumer attention on creative, provide a more data-driven view of how creative will perform, from attention mapping to opportunity areas. That output is helpful, but it still requires interpretation. AI can support both the process and the analysis, but it doesn’t replace the need to explain the work or stand behind the decisions being made.

AI can easily take over more of the thinking if you let it. When it’s this accessible and this fast, it’s easy to default to the output instead of pushing your own understanding further.

But in my experience, it comes down to the environment you’re in, what’s expected of you, and your own motivation to actually learn. If you’re pushing to explain your thinking, defend your decisions, and actually understand the work, AI becomes a tool that accelerates how you learn, not something that replaces it. If that expectation or motivation isn’t there, it’s a lot easier for it to turn into something you just rely on.

Tips for Young Strategists Entering the Workforce in the Era of AI

For interns and young strategists entering the workforce right now, the ones who will grow the most are the ones who stay curious beyond the output. A few things to keep in mind:

• There’s a big difference between generating an answer and actually understanding it. Don’t let speed close that gap.
• Keep asking questions, even when AI gives you a strong starting point quickly.
• Push yourself to understand the “why” behind the work, not just the what.
• Think deeper than the first result AI gives you. The best insight is rarely the first one.

AI can absolutely accelerate learning, but only if you’re willing to be curious, challenge ideas, and put in the effort to truly understand what you’re creating.

That’s what I keep coming back to. AI is making it easier than ever to execute quickly, but whether we grow from it still depends on how willing we are to think deeper than the output itself.

So as AI continues to shape the way interns learn and work, the real question becomes: are we using it to think better, or just move faster?