As AI absorbs routine tasks, the people who pull ahead are those who connect ideas across disciplines—the modern polymaths.
Julie Colwell
Principal Strategist
Workday
The question of what AI is doing to human thinking has moved past dinner-table speculation to become an institutional priority. The World Economic Forum's Future of Jobs Report says nearly 40% of the core skills a job requires today will have changed by 2030. Gartner frames the same shift in starker terms, projecting the life cycle of a technical skill will collapse from as much as 12 years to just two to five.— Gartner’s research argues the AI-era skills that matter aren't about completing tasks better but about making people sharper thinkers and stronger communicators.
For all the predictions about which skills will survive, one question goes unasked: What kind of thinker thrives when AI handles the tasks? The answer is a modern polymath, a person driven by curiosity who makes connections others can’t—or aren’t brave enough—to see.
One question goes unasked: what kind of thinker thrives when AI handles the tasks?
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It's a more consequential question than it sounds, because the answer shapes everything downstream: who you hire, how you develop people, and what you reward, versus what you say you reward.
For most of the last century, the answer was the specialist. The person who knew one domain cold, executed within it reliably, and handed off at the edges. Companies were engineered around that idea. It worked. Knowledge was expensive to accumulate, expertise took years, and depth was genuinely hard to come by.
That logic is shifting.
If the specialist was the mind the last century was built around, the polymath is the one this moment rewards. This isn't someone who knows a little about a lot. The root is the Greek manthanein: to learn. A polymath is someone who has pursued and developed knowledge across multiple disciplines in new or novel ways.
Most people know Edwin Land as the inventor of the Polaroid camera. However, before that, Land was a physicist who left Harvard to study polarized light—a phenomenon the scientific community had largely decided was settled. He disagreed. He spent years working across optics and chemistry, eventually figuring out how to manufacture polarizing film at commercial scale. But, physics was only part of the equation.
Land was also obsessive about the experience of photography—specifically the gap between the moment you take a picture and the moment you can hold it. He thought about that gap as an emotional problem. The Polaroid camera was the answer. Building it required him to be a physicist, a chemist, a product designer, and something close to a consumer psychologist—often at the same time. No single discipline got him there.
Leonardo Da Vinci is the obvious reference, almost too obvious, except that how he worked tends to get glossed over. He dissected cadavers so his painted figures would move with anatomical truth, and studied the flow of rivers and the mechanics of birds' wings in ways that fed his engineering. None of it was compartmentalized. The anatomy sharpened the painting, and the observation sharpened the engineering. It was one continuous act of attention.
What Land and Da Vinci produced—the Polaroid, the Vitruvian Man, flying machine sketches that held up—came from their ability to hold multiple disciplines simultaneously and find things at the intersections that specialists weren't positioned to see. They changed what was considered possible, in multiple fields, often at the same time. That's what serious knowledge across disciplines looks like in practice.
A polymath isn't someone who knows a little about a lot — it's someone who has developed serious knowledge across multiple disciplines.
At Workday, Joel Hellermark built our AI platform on a simple premise: knowledge should move freely across an organization rather than pool in silos. That premise rhymes with the historical pattern that brought us the Polaroid and the Mona Lisa. The people who shaped the Renaissance, the Bell Labs era, and the early internet weren't specialists who could only go deep in one direction. Rather, they were the ones who could hold multiple fields in mind and find options at the intersections that nobody else could see. What set them apart wasn't just access to information—it was the connections their curiosity prompted them to discover.
AI is making this creative process accessible. It’s not just unlocking information at scale; it’s synthesizing data across disciplines and making it available at speed, to anyone willing to engage seriously with it. As a result, the cost of crossing disciplinary boundaries has considerably dropped in real world business operations:
A people leader can now work through a systems-design problem with a level of expertise that would have required years of study before.
A finance executive can engage with product strategy.
A researcher can move into organizational behavior without starting from scratch.
What AI still requires is curiosity, judgment, and the willingness to integrate what you find. Those remain stubbornly human.
There's data validating this now. Workday's own global study of 2,500 workers across 22 countries found that as AI absorbs routine work, the human capabilities that gain value are precisely the ones it struggles to reproduce: judgment, creativity, and the ability to read a situation and connect what others keep separate. Ethical decision-making ranked as the single most valuable human skill—both today and in a future of full AI adoption—and 83% of respondents said AI would expand human creativity.
Read one way, that's a reassuring story about soft skills surviving. Read more carefully, it's a story about integration. The people who pull ahead aren't the ones holding a single deep skill the model hasn't reached yet. They're the ones who can combine judgment, creativity, and range across domains into something a model can't assemble on its own. That is, more or less, a working definition of a polymath.
The upstream problem for organizations navigating AI isn't tool adoption. It's talent architecture.
Most companies are still built to find and reward depth within a lane, and quietly filter out the integrative thinking this moment rewards.
That bias shows up in three places most organizations don't think to look:
Hiring, where credentials and functional track records remain the dominant signal even as cross-domain synthesis becomes more valuable.
Development, where learning programs are organized by job family rather than by the intersections between them.
Performance, where contribution within a defined scope is visible and rewarded, while the person who imported a perspective from one part of the business to solve a problem in another often goes unrecognized or told to stay in their lane.
This isn't a brief against specialization. For a long time, the specialist model was the right answer. But since conditions have changed, is it time for another model to matter more?
The periods of greatest human innovation shared something. They were environments where curious people could move freely between ideas and where synthesis across domains was rewarded. AI is recreating those conditions at a clip none of those periods could have managed. The constraint that limited polymathy to a small number of people in exceptional circumstances is being removed. Not completely, not overnight. But the direction is clear.
Polymaths are already on the rise. The question for leaders is whether their organizations will be somewhere these people want to work—and whether they'll be recognized when they show up. Creating an environment conducive to polymaths requires a degree of introspection and a willingness to do things differently. Consider:
Where in your hiring process do you unintentionally screen out integrative thinkers?
Can you create programs to explicitly encourage movement across disciplines?
Can you redesign development paths to create cross‑functional rotations where polymaths can thrive?
Polymaths have always existed. We're entering the period when they'll matter most.
If given an environment to thrive in, modern polymaths will become easier and easier to recognize. The person with fire in their eyes when handed an impossible problem. The one who keeps drawing connections between things that aren't supposed to be connected. The one who is, technically, a little hard to manage. That person has always existed. We're entering the period when they'll matter most.
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