If AI makes everyone competent, how will we distinguish real talent

If AI makes everyone competent… how will we distinguish real talent?
By: Walid Marwan

A while ago, I was reviewing work that was crafted exceptionally well. The content was organized, the ideas clear, the language strong, and the presentation professional.

Then a simple question came to mind:
Am I now evaluating the quality of a person's work… or the quality of the tool they used?

The question seems simple, but it made me think about something larger.
Because today AI can help almost any employee write better, analyze faster, organize their thoughts, and present them more powerfully. That is very positive.

But if output quality starts converging among people, how will we distinguish the person with genuine ability from the one with a powerful tool?

In the past we saw ability in the result

For a long time, it was natural to judge an employee by the quality of their work.

• Is the report well written?
• IsIs the analysis strong?
• Is the offer convincing?
• Did the task finish on time?

There was a logical assumption that the quality of the output largely reflects the level of the person who produced it.
But artificial intelligence has started to change this equation.

Today, a person with extensive experience and someone at the start of their career might produce two drafts that are very similar if both use the same tools well.
And that does not mean that both are at the same level.

Because the real difference may not be apparent in the final form.
The difference lies in the way of thinking that occurred before the result.

The result is the same… but the path is different

Imagine that two people gave essentially the same recommendation.
The first analyzed the problem, understood the context, compared multiple alternatives, and used artificial intelligence to test their ideas and uncover aspects they may have missed.
The second asked the AI: “What is the best solution to this problem?” They took the answer, tweaked the wording slightly, and presented it.

The final result may look very similar.
But is the capability the same? Of course not.

The first used artificial intelligence to broaden their thinking.
Use the second as a substitute for thinking.

And this, in my opinion, is one of the biggest challenges we will face in evaluating performance and talent in the coming period.

Because we won't be able to rely only on the output quality.
We will need to understand more:

• How did the person reach this result?
• Why choose this solution?
• What alternatives have you considered?
• And what made them reject one proposal and accept another?

The most important question now is: why?

In one instance, I was reviewing an output that had been prepared with the help of artificial intelligence, and the result was truly excellent.

Instead of focusing only on the content, I started asking the person about their way of thinking.
Why did you choose this recommendation?
What part are you not convinced about?
If circumstances change, would you choose the same solution?
And what risks do you see?

These questions revealed to me their level far more than the file itself.

And that made me think that perhaps managers need to add a very simple question to their work review process: Explain your thinking.
Not to test or to prove whether the person used AI or not.
But because the thinking process itself has begun to become an important part of the performance we need to see.

Do we need to change the way talent is evaluated?
And this leads me to a larger question regarding the field of learning, development, and talent management.
Are the tools we use to evaluate people still sufficient?

If an assessment task relies only on writing a report, preparing a presentation, or analyzing information, we need to consider: are we truly measuring the person's ability, or their ability to use an artificial-intelligence tool?

And of course using AI itself has become an important skill, and it shouldn't be ignored or prohibited. But future assessment may need to focus more on deeper skills:

• Quality of questions.
• The ability to verify information.
• Understanding the context.
• Linking multiple ideas together.
• Professional judgment
• Decision making.
• And most importantly, a person's ability to interpret their region and defend their choice.

Because a good answer has become easier to reach.
As for whether this answer is appropriate in the first place, that’s a different story.

The outstanding employee's appearance will change

In the past, we sometimes described the distinguished person as someone who knows more than others or produces higher-quality work.
In the future, the picture may change a bit.

A distinguished employee is not necessarily the one who writes the best report from scratch.
It may be the person who knows how to pinpoint the right problem before they start.
And AI knows how to ask the right question.
And they doubt the answer because they need to doubt.
and adds the context that the tool does not know.
and they choose from among the alternatives
Then they bear responsibility for the choice.

For me, this is a very important point.
Because artificial intelligence may reduce the gap in how work is structured, but at the same time it will reveal the gap in the quality of thinking.

In the end

I think artificial intelligence won’t give everyone the same capabilities.
But it may make their work appear more aligned than ever before.

And that means the challenge facing managers, human-resources specialists, and learning and development professionals will not be limited to teaching people how to use artificial intelligence.
But also there is a reconsideration of a very important question:
What exactly are we evaluating?
Do we evaluate the beauty of the result?
And not the quality of the thinking it produced?

In the coming years, the question may not be:
Who gave the best answer?
But:
Who knows how to ask the right question, understand the answer, judge it, and turn it into an appropriate decision?

Because AI may raise the level of output for everyone, but the quality of thinking will remain what distinguishes them.

About the author Walid Marwan is a talent development strategist, learning and development manager, trainer and facilitator, certified professional coach, with over 18 years of professional experience across various sectors. He currently serves as Learning and Development Director at Tamr Group in Saudi Arabia, leading the design and implementation of talent development strategies and initiatives, leadership development programmes, corporate learning, competency-based development, and fostering a coaching culture within organisations. He also sits on the board and heads events and community for the Saudi Chapter of the International Coaching Federation (ICF), is a founding partner of The Career Hive community, and co-author of the book “Al-Kholasa”. His professional approach is grounded in a clear mission: learning, empowerment, and developing individuals from potential to performance.