It’s a hotly debated question: Is it better to be an expert or a generalist in the AI era?
For Aparna Nair, IBM’s chief talent, leadership, and culture officer, there’s no debate. She said she doesn’t think generalists alone “will cut it” in the workforce in the AI era.
“The world of generalists is long gone,” Nair said.
Nair said that generalists may be able to rely on AI to learn a lot, but at IBM, for example, the company’s clients aren’t expecting general advice from its workforce. IBM is hired for its deep technological expertise, she said.
Nair said the same is true for entry-level hires. IBM, which has committed to doubling down on junior talent, expects new workers to arrive with the skills — and domain expertise — to take on more complex work earlier in their careers, Nair said.
She said workers should identify an area that aligns with their employer’s needs, become an expert in it, and “go deep.”
That expectation remains even as engineering roles are starting to blur with AI. In the past, a software engineer might have spent 20% of their time coding and another 20% testing, Nair said. Now, AI can handle much of the coding work, and engineers need the critical-thinking and judgment skills to evaluate what the technology produces, Nair said.
They also have more opportunities to work across the business, such as speaking with clients and marketing teams and bringing what they learn back into the products they’re building.
While today’s engineers may wear many hats and work closely with different teams, they still need deep expertise in their field — and an understanding of how their work shapes the experience of clients and colleagues.
Without that added knowledge, Nair said, it would be difficult for them “to do anything meaningful.”
While domain expertise may be valuable, workers should also remain flexible. Nair said that there’s “nothing more powerful” than when workers combine deep expertise with the right behaviors, and also use AI to their advantage.
“That’s a lethal combination,” Nair said.
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