Why AI agents are making specialists obsolete

Deep specialization is losing its premium. As AI agents take over increasingly technical tasks, companies are shifting hiring toward professionals who can work across multiple disciplines rather than masters of a single domain. Workday's leadership calls these adaptable workers "polymaths," and several enterprise tech leaders now say this profile will define who stays valuable in an AI-native workplace.

The argument is straightforward: if an AI agent can perform specialized analysis, code review, or data processing autonomously, the human whose only value was that specialty faces displacement. The worker who can orchestrate agents across functions, make judgment calls spanning multiple domains, and adapt as tooling changes weekly becomes far harder to replace.

What is a polymath in the AI agent era?

The term has ancient Greek roots, describing someone who excels across multiple fields. Leonardo da Vinci is the canonical example. In modern enterprise AI, the definition is narrower but no less demanding.

Gerrit Kazmaier, president of product and technology at Workday, argues that agentic AI "amplifies workers' strengths, allowing them to function as polymaths." The agents handle rote execution; humans provide the cross-domain judgment.

Kathy Pham, Workday's VP of AI, put it bluntly: professionals who can work with agents across multiple areas simultaneously will be crucial. "These are the gaps that we need to fill, and maybe we can then redirect our energy to higher-value areas, because we've now automated the less valuable parts of our working lives that took our time," she told ZDNet.

Two characteristics define the successful polymath: adaptability to AI-enabled change, and the confidence to make decisions that agents cannot. Neither is about technical depth in isolation.

Why specialization is losing value

Chris Kairinos, senior director of global modern workplace technology at A+E Global Media, offered a striking analogy. Learning a skill used to be like mastering the piano. You practiced, you improved, you became an expert.

“It's as if 30 different keys are being added to a piano. The shape of the piano is changing; there are now three layers of keys on that piano, and there are an extra three pedals. If the piano is changing all the time, how are you ever going to become a master? In those conditions, you can't. You've just got to be adaptable enough to be able to use it.”

— Chris Kairinos, A+E Global Media

The pace of change in AI tooling is the problem. A specialist who spent years mastering a particular workflow might find that workflow automated or fundamentally altered in months. The investment in narrow depth yields diminishing returns when the target keeps moving.

Kairinos went further: "Everybody needs just to embrace technology. I don't think you need to master technology, because I don't think there is a way to master it now." The implication is uncomfortable for anyone who built a career on being the expert in their silo.

The nuance: polymaths are not generalists

David Minahan, director of digital, data, and technology at UK charity Young Lives vs. Cancer, drew an important distinction. The shift away from specialists does not mean companies want generalists who know a little about everything.

"I think generalists are too broad," Minahan told ZDNet. "Specialists aren't required in most businesses anymore because AI leverages knowledge differently." His organization has deliberately built a strategy around cultivating this middle ground.

The polymath profile requires real competence in several areas, not shallow familiarity. You need to understand enough about marketing, product, engineering, and operations to orchestrate AI agents working across those functions. But you no longer need to be the person who writes the code or runs the campaign yourself.

This creates a strange new competency: knowing enough to evaluate agent output, spot errors, and make cross-domain tradeoffs, without necessarily being able to do the underlying work manually. It is a supervisory skill that did not exist five years ago.

What this means for hiring and L&D

Enterprise leaders are already adjusting. Minahan noted that his organization is building learning and development strategies that acknowledge the demand for polymath-like capabilities. The old model of sending employees to deep-dive certifications in narrow skills is giving way to broader cross-training.

For CTOs and hiring managers, this raises practical questions. Job descriptions built around specialist credentials may attract exactly the wrong candidates. Interview processes that test depth in one domain may miss the adaptability that actually predicts success.

Tools that help teams work across functions become more valuable in this model. Project management platforms like ClickUp, Asana, or Monday.com already position themselves as cross-functional coordination layers. Automation tools like Zapier and Make let non-specialists build workflows that would have required dedicated engineers. The polymath workplace runs on tools that lower the barrier to working outside your lane.

Compensation models may need rethinking too. If depth no longer commands premium salaries, what does? The answer appears to be judgment, adaptability, and the ability to ship outcomes rather than perform tasks. That is harder to measure and harder to negotiate.

The uncomfortable question

There is a risk in the polymath narrative that enterprise vendors will not say out loud. If AI agents become capable enough, even the cross-functional orchestrator role shrinks. The polymath is valuable today because agents still need human supervision and judgment. That ceiling may not hold.

For now, though, the direction is clear. The workplace is rewarding breadth over depth, adaptability over mastery, and judgment over execution. Whether that is a permanent shift or a transitional phase depends on how fast agentic AI matures. The workers betting on the former have reason to be nervous.

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