这是一个备受争议的问题：在人工智能时代，成为专家还是通才更好？
It's a hotly debated question: Is it better to be an expert or a generalist in the AI era?
对于IBM首席人才、领导力和文化官阿帕尔娜·奈尔来说，这没有争议。
For Aparna Nair, IBM's chief talent, leadership, and culture officer, there's no debate.
她说，她认为仅靠通才在人工智能时代的劳动力市场中“达不到要求”。
She said she doesn't think generalists alone "will cut it" in the workforce in the AI era.
“通才的时代早已过去，”奈尔说。
"The world of generalists is long gone," Nair said.
奈尔表示，通才可能能够依赖人工智能学习很多东西，但以IBM为例，公司的客户并不期望其员工提供泛泛而谈的建议。
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之所以被雇佣，是因为其深厚的技术专长，她说。
IBM is hired for its deep technological expertise, she said.
奈尔说，对于初级员工的招聘也是如此。
Nair said the same is true for entry-level hires.
IBM承诺加大对初级人才的投入，期望新员工到岗时就具备能够在职业生涯早期承担更复杂工作的技能——以及领域专长，奈尔说。
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.
她说，员工应确定一个与雇主需求相契合的领域，成为该领域的专家，并“深入钻研”。
She said workers should identify an area that aligns with their employer's needs, become an expert in it, and "go deep."
即使工程师角色开始与人工智能融合，这一期望仍然存在。
That expectation remains even as engineering roles are starting to blur with AI.
过去，软件工程师可能有20%的时间用于编码，另外20%用于测试，奈尔说。
In the past, a software engineer might have spent 20% of their time coding and another 20% testing, Nair said.
现在，人工智能可以处理大部分编码工作，工程师需要具备批判性思维和判断能力，以评估技术所产出的内容，奈尔说。
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.
他们还有更多机会跨业务合作，例如与客户和市场团队交流，并将所学内容带回到他们正在开发的产品中。
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.
虽然如今的工程师可能需要兼顾多种角色，并与不同团队紧密合作，但他们仍然需要在自己的领域具备深厚专长——以及理解他们的工作如何塑造客户和同事的体验。
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.
没有这种额外的知识，奈尔说，他们“很难做出有意义的事情”。
Without that added knowledge, Nair said, it would be difficult for them "to do anything meaningful."
虽然领域专长可能很有价值，但员工也应保持灵活性。
While domain expertise may be valuable, workers should also remain flexible.
奈尔表示，当员工将深厚专长与正确行为相结合，并善于利用人工智能时，“没有比这更强大的了”。
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.
“那是致命的组合，”Nair 说。
"That's a lethal combination," Nair said.