Opinions expressed by Entrepreneur contributors are their own.
Key Takeaways
- Automate mechanical tasks, but protect judgment-building work that develops future leaders.
- AI should accelerate learning by shifting juniors toward analysis, evaluation and decision-making.
Have you ever thought about what happens to your company when you stop teaching people how to think?
I keep coming back to that question as more teams hand entry-level work to generative AI. Yes, the output still shows up. The spreadsheet is still built. The dashboard still updates on time. And yes, on paper, productivity looks better than ever.
However, the quiet cost sits somewhere else. The junior employees who used to earn their judgment through that work are not getting the same reps. They are not wrestling with messy inputs anymore. They are not making the kinds of small mistakes that create instinct. They are not getting coached through the blind spots that turn “smart” into “reliable.”
When I look at the A-players on my own teams, they did not become great by avoiding mistakes and foundational work. They became great because they did it anyway, got feedback, did it again and learned from real people’s experiences. If you remove that path entirely, you create a dangerous kind of organizational short-sightedness. The knowledge may live inside systems and prompts, but fewer people are learning how to produce it, challenge it and pass it on.
This is not an argument against AI. It is an argument for using it with intent.
The work that teaches judgment is not the same as the work that wastes time
A lot of entry-level tasks take time. They are repetitive. They often sit at the bottom of a process. Leaders see that stack and instantly think, “Automate it.”
That is where the mistake starts.
Some entry-level work is mechanical. It needs to get done, but it does not build much judgment. For instance, formatting decks, pulling standard reports, cleaning up recurring spreadsheets or drafting a first-pass template that follows the same pattern every time. If AI can handle those tasks well, you should let it. Protecting busywork does not build talent. It burns it out.
Other entry-level work is where judgment forms. It is the moment someone learns to separate signal from noise. It is the moment they realize that a familiar approach does not fit a certain situation. It is the moment they learn why the business cares about one metric and ignores another. This work builds future leaders and is exactly the work you cannot handle via AI without replacing it with something equally developmental.
If you treat both categories the same, you get the worst outcome. You remove the training ground, then you wonder why your bench has become weaker two years later.
What AI changed for us
After decades of building and scaling teams, I have learned that new technology is rarely the real challenge. The challenge is redesigning work so that the technology absorbs the mechanics while people grow into higher-value contributions. That is where scale comes from, and that is where resilience lives.
Here is a simple example.
A junior analyst used to spend hours pulling data and formatting spreadsheets, then they would get a short window to interpret what the numbers meant. That is backwards. AI can often handle the pulling and formatting quickly, which means the analyst can spend their time on the part that actually teaches them something. They can test assumptions. They can spot what looks off. They can explain what the data suggests and what it does not.
The same shift applies across functions.
If AI drafts an internal memo, the junior employee should not be graded on how fast they can hit send. They should be taught how to evaluate whether the memo answers the right question and whether the recommendation holds up when the context changes.
If AI summarizes research, the junior employee should be expected to find what is missing and to surface what conflicts. A clean summary is not the same as a reliable conclusion.
This is not about doing less work. It is about doing different work and doing the work that builds capability.
How to get this right without slowing down
Look at your team’s entry-level workload with fresh eyes. Separate the purely procedural tasks from those that require trade-offs. If a task can be completed by following a checklist, automate it. If it requires judgment, delegate it to people.
What comes next is where most organizations stall. You cannot remove the mechanical work and hope development happens on its own. You have to redesign the judgment-building work so that juniors still get reps, coaching and responsibility. That means putting review standards in place. It means requiring juniors to explain why an AI output is correct and what would make it incorrect. It means giving them ownership over the thinking, not just the deliverable.
Finally, track more than productivity. If your only scoreboard rewards output and efficiency, you will optimize for the wrong future. Pay attention to whether your junior team is getting better at analysis and decision-making over time. If they are not, you are not building real capability.
Key Takeaways
- Automate mechanical tasks, but protect judgment-building work that develops future leaders.
- AI should accelerate learning by shifting juniors toward analysis, evaluation and decision-making.
Have you ever thought about what happens to your company when you stop teaching people how to think?
I keep coming back to that question as more teams hand entry-level work to generative AI. Yes, the output still shows up. The spreadsheet is still built. The dashboard still updates on time. And yes, on paper, productivity looks better than ever.
