Why young professionals are not good at AI

When a new technology arrives, owners often turn to the youngest people in the room. They learn the tools quickly. Surely they should lead the way? With artificial intelligence (AI), that assumption can be expensive.

A young professional can produce an impressive strategy report without understanding the strategy. A CEO can mistake that report for evidence of expertise and act on it.

The danger is blindly trusting AI to replace knowledge, experience and judgment.

The employee whose thinking went missing

I saw this in my own company. We hired a bright young professional for a trial in a high-level back-office role. Let us call him Peter.

He was smart, eager to work long hours and had a good work ethic. I gave him important strategic questions.

One assignment required deep research into several industries, followed by a recommendation on how to move a project forward.

He needed to weigh pros and cons and argue for and against different strategic directions.

What came back was essentially a set of AI answers.

I told him repeatedly: I wanted Peter plus AI. I could get AI answers myself. I wanted his intelligence, his perspective and his judgment added to what the technology could do.

But when I questioned his recommendations, he could not explain why one direction was stronger than another or defend his position.

He had delivered conclusions he did not properly understand. We eventually had to let him go. His willingness to work was there. His thinking was missing from the work.

A beautiful report can hide an empty understanding

This creates a problem for CEOs and family business owners.

A few years ago, a detailed report suggested that someone had spent time researching and understanding the subject.

Today, AI can create that appearance quickly. The slides can look exceptional. The author may understand very little of the substance.

Judge work by its polish and you can reward the wrong person and approve the wrong recommendation.

If the author cannot explain the assumptions, limitations or practical consequences, who will take responsibility when reality differs from the report?

You need someone who can answer customers’ objections and notice when a crucial assumption has failed. That requires understanding the work.

The quick-answer trap

Young professionals often embrace new technology quickly. Their fluency can help an entire business adopt useful tools. But ease of adoption can also encourage overuse. For someone with a short attention span, an instant answer is especially tempting. Why spend hours wrestling with a difficult question when a machine produces a confident response in seconds?

Because getting an answer and understanding a problem are two different things. For an important expansion decision, the first answer should begin the investigation. Examine assumptions, question the evidence and consider what could go wrong.

There is also a risk of gradually outsourcing the habit of thinking.

When employees repeatedly skip the work of comparing alternatives and reaching their own conclusions, they miss the practice through which judgment develops.

A 2025 survey by Microsoft Research and Carnegie Mellon University associated greater confidence in generative AI with less reported critical thinking. This does not establish a loss of intelligence, but it raises a concern about how people use AI.

This can happen at any age. Younger professionals risk handing over their thinking before building the experience to challenge the answer.

Experience helps you spot what is wrong

AI can discuss industries with extraordinary confidence. That confidence can make a newcomer feel they have consulted an expert.

An experienced operator may notice what is missing: the customer who never behaves as the model assumes, the supplier constraint the analysis ignores, or the gap between a promising market and a profitable business.

That is why domain knowledge matters. The more deeply you understand an industry, the better equipped you are to ask useful questions and recognise an answer that does not fit reality. Pair your team’s willingness to experiment with the knowledge of people who understand the business.

Treat AI like a team member who needs supervision

I encourage leaders to explain AI through a simple comparison: imagine a team member who is knowledgeable in many areas but makes mistakes.

This colleague can sound brilliant and still reach the wrong conclusion. On some tasks, it may seem as though they are having a bad day. They can invent facts, reverse an earlier recommendation or argue a weak position with remarkable confidence.

AI does not literally have moods, but an intelligent-sounding answer still needs checking. Ask AI to review its work, identify weaknesses and make the strongest case against its recommendation. Cross-check important conclusions with another AI system. Then verify crucial facts against original sources and consult people with relevant expertise.

Two AI tools agreeing is not proof. Both can be wrong. Speak to customers, operators or specialists who know the reality you are trying to assess.

Keep asking why

Suppose you ask an executive whether to launch a pilot for a new business venture. You want a clear yes or no, supported by reasons.

When the recommendation arrives, probe it.

Why this market? Why this offer? Which evidence supports the demand? What is the strongest argument against proceeding? What would make you change your mind?

Keep digging. If the answer is a market forecast, ask how that translates into paying customers for your particular business.

Also examine how AI was used. What did the executive challenge? Which claims were checked? Where did they disagree with the tool?

Someone who understands the work can acknowledge uncertainty and explain the trade-offs. Someone who has copied an answer may struggle beyond the document.

A hesitant answer proves little. Look for reasoning, not merely confidence.

Teach your people this standard and apply it consistently. Encourage AI use. Require employees to understand and take responsibility for what they bring you. Make clear that you will continue to examine both the recommendation and the thinking behind it.

AI can help your people do better work. Make sure their brains are still part of the team.

Your five to thrive

1. Look beyond the presentation. A polished report tells you little about whether its author understands the work.

2. Keep asking why. Probe the evidence, assumptions, alternatives and consequences behind important recommendations.

3. Examine the AI process. Ask what employees challenged, checked and changed before accepting the output.

4. Build checks and balances. Use AI reviews, other AI tools, original sources and real experts to test important conclusions.

5. Demand the person plus AI. Develop your team’s knowledge and judgment so the technology adds to their thinking.