Much of the debate about AI and talent has focused on which jobs will change, which tasks machines will take on and what skills people will need to keep pace. Less attention has been paid to a different question: how do you identify exceptional talent when AI improves everyone’s general abilities?

Singapore’s Health Minister Ong Ye Kung recently touched on this when he told a room full of graduates that artificial intelligence (AI) is replacing certain tasks, even as entire jobs remain intact¹. His advice to young people for this new era was: “Mastery of your craft will define you as a professional and become your biggest competitive edge.”

I agree. But I think AI is changing what that mastery looks like.

We can already see how much more individuals are able to do. An organizational experiment with 776 professionals at Procter & Gamble found that AI could work as a “cybernetic teammate”.² Individuals given AI tools produced ideas roughly as good as those of two people working without AI, and cut the time needed to reach solutions by 16 percent.

In my field of quantitative finance, we see this playing out directly in our International Quant Championship. Solo participation is growing exponentially as AI tools become a more important part of how people conduct research.

That shift has an important consequence for talent. As AI expands what individuals can produce on their own, the ability to produce sophisticated work becomes less of a differentiating factor. What quickly becomes distinctive is the judgment behind it: the ability to evaluate and choose among competing ideas and tolerate ambiguity in creative work in ways that AI tools, as yet, cannot.

For companies, that raises two practical questions. The first is how to identify and hire for great human judgment. The second is how to restructure work so that individuals empowered by AI can operate with more independence and still contribute to the wider team.

Hiring for judgment

For decades, hiring in knowledge-intensive industries has followed a familiar pattern of companies looking for certain degrees, particular technical credentials and the right amount of experience. But as the skills that make a person valuable are starting to change, companies will need to adjust how they identify and assess talent.

A particular university degree can open a door, but it tells a potential employer little about a person’s ability to handle uncertainty or weigh competing ideas against a consensus view. In the search for these skills, companies may want to cast a wide net. Strong candidates will include those whose experience, as Mr. Ong put it, “may not fit neatly into a conventional CV.” These could be people whose resilience and initiative show up in unconventional pursuits, if employers are willing to look.

In practice, this involves building hiring assessments that test how people think as opposed to what they know, and also how they approach unfamiliar problems. It calls for asking candidates to evaluate conflicting inputs and explain their reasoning. And it points towards sourcing candidates from a wide range of backgrounds, including people who have built things on their own, switched fields or taught themselves new disciplines out of curiosity.

Structuring work for independence

The second change is how work itself is structured. If AI amplifies what each person can do, then an organization’s advantage may come from how many independent lines of inquiry it can run in parallel.

To reach full creativity in output, companies will need to increasingly consider how they empower people to pursue their own methods, allowing ideas to develop separately before the wider group tests them.

That creates the need for organizational structures that allow employees to work both independently and interconnectedly. One example would be to implement review processes that give individuals room to explore before reporting back. It could involve allocating time or resources for experimentation, where people are encouraged to pursue a hypothesis before seeking group approval at each step. And it could entail evaluating people on the quality of the questions they ask and the ideas they generate, alongside how well they apply those ideas in practice.

What this means for educators

These changes carry implications for education as well. As companies start to prioritize judgment and the ability to develop ideas in fast-paced and sometimes uncertain environments, universities and training institutions will need to start preparing students for that reality.

This could mean placing greater emphasis on open-ended problem solving, including teaching the skills needed to evaluate and compare information from multiple different sources. It also suggests that developing the confidence to form and defend an independent view will have greater weight in higher education.

The level of technical knowledge matters and will continue to matter. But the ability to apply it wisely, even at a young age, is increasingly what employers will look for.

Singapore’s talent advantage

Singapore has long placed a premium on developing human capital, and that emphasis is now visible in how it is preparing its workforce for AI.

In the financial sector, Singapore’s three local banks have trained over 26,000 employees in foundational AI skills, close to 80 percent of their combined workforce. And the Institute of Banking and Finance is partnering with training providers to equip more than 100,000 finance professionals with AI skills over the next three years.³

That investment is important because it helps raise the baseline of what people across the workforce can do with AI. But as those capabilities become more widespread, the question of differentiation becomes more important too.

For Singapore, which has always relied more on the quality of its talent than on sheer scale, the opportunity is to make sure greater access to AI translates into more independent thinking, better judgment and more original ideas. That will depend not only on training people to use the tools, but on whether companies and educators create the conditions for those qualities to develop and be put to use.

Mr. Ong told graduates to focus on mastery. AI may change what exceptional talent is able to do, and how companies identify and develop it, but the qualities that underpin strong judgement remain deeply human: curiosity, critical thinking and the ability to find creative ways through difficult problems. The opportunity is to combine those qualities with the greater capability AI provides, so that technology expands what people can achieve with their distinctive thinking.