The conventional wisdom is that artificial intelligence will level the playing field among employees, giving the average employees the tools to shine as brightly as the superstars.

普遍的观点认为，人工智能将在员工之间拉平竞争环境，让普通员工也能拥有像顶尖人才一样大放异彩的工具。

My research suggests this conventional wisdom is wrong. I believe it is the superstars themselves who will gain the most from AI, widening the chasm between top performers and everyone else.

我的研究表明，这种传统观点是错误的。我认为，正是那些“明星员工”（superstars）自己将从人工智能（AI）中获益最大，这会进一步拉大顶尖人才与普通员工之间的差距。

While this may be good news for superstars, it’s problematic for companies, because AI-amplified performance gaps will intensify the workplace tensions and resentment that stars can sometimes create, undermining team cohesion—and ultimately hurting the collaborative work that drives business success. Organizations that fail to address this may find their best talent harder to keep and their remaining employees harder to motivate.

虽然这可能对明星员工是好消息，但对公司来说却是个问题，因为AI放大的绩效差距会加剧明星员工有时会带来的职场紧张和不满，这会破坏团队凝聚力，并最终损害推动业务成功的协作工作。未能解决这个问题的组织可能会发现，其顶尖人才更难留住，而其余员工则更难激励。

How expertise amplifies AI advantages

专业知识如何放大AI优势

Think about your own organization. When a new tool arrives that promises to make everyone more productive—advanced Excel features, sophisticated customer-relationship-management systems or powerful analytics platforms—who actually masters it first? It’s usually the superstars who dive deep, discover hidden capabilities and find creative applications no one else thought of, while the average employees tend to stick to basic functions.

想想你自己的公司。当一项新的工具出现，承诺能让每个人都更高效时——比如高级的Excel功能、复杂的客户关系管理系统，或是强大的数据分析平台——谁会最先掌握它呢？通常是那些“明星”员工，他们会深入钻研，发现隐藏的功能，并找到其他人从未想到的创新应用，而普通员工则倾向于只使用基本功能。

AI follows the same pattern as every other workplace tool: The superstars are the first to embrace it. What’s more, research shows that stars also leverage their “domain expertise”—that is, their in-depth knowledge of a subject or business—to extract fundamentally more value (and catch more mistakes) from AI systems than average performers.

人工智能（AI）遵循了所有其他职场工具的相同模式：明星员工总是最先接受并使用它。更重要的是，研究表明，明星员工还能利用他们的“领域专业知识”（即他们对某个主题或业务的深入了解），从人工智能系统中提取出比普通员工多得多的价值（并发现更多的错误）。

Imagine a star consultant working on bringing a new product or service to market. Instead of asking AI to “analyze this market” and receiving generic insights, the star uses years of experience to ask more nuances and targeted questions about competitive dynamics, regulations and barriers. Stars’ deep expertise will lead them to better refine the commands or questions they give AI, rather than accepting the first output. This results in more useful and accurate results.

设想一下，一位明星顾问正在致力于将新产品或服务推向市场。他们不会仅仅让AI“分析这个市场”并获得泛泛的见解，而是利用多年的经验，提出更细致、更有针对性的问题，比如关于竞争动态、法规和进入壁垒。明星顾问深厚的专业知识会促使他们更好地优化给AI的指令或问题，而不是直接接受AI的首次输出。这会带来更有用、更准确的结果。

In addition, research finds that employees with more expertise than their peers are significantly better at accepting AI recommendations when they are correct and, more important, rejecting them when they are wrong.

此外，研究还发现，专业知识比同事更丰富的员工，在人工智能（AI）给出正确建议时，能够显著更好地采纳；更重要的是，当AI建议有误时，他们也能更好地识别并拒绝。

Stars have another advantage: They work more systematically in general, meaning they are more organized and thoughtful in how they approach tasks compared with the average worker. Research finds that these are exactly the kinds of people who get dramatically better results from AI tools than those who just dive in randomly. AI tools respond best to clear, structured inputs—exactly what stars naturally provide through their organized work habits.

顶尖人才还有另一个优势：他们通常工作更有系统性，这意味着与普通员工相比，他们在处理任务时更有条理、考虑更周全。研究发现，正是这类人能从人工智能工具中获得显著更好的结果，而非那些随意尝试的人。人工智能工具最能有效处理清晰、结构化的输入——这正是顶尖人才通过他们有条理的工作习惯自然而然地提供的。

Extra credit

额外优势

The way managers treat superstars only exacerbates their advantages.

经理们对待“超级明星”的方式只会加剧他们的优势。

The reputation and status of top performers grant them autonomy and discretion in their work, according to my research. That means stars are more likely to dive in and start experimenting with AI immediately. While average employees wait for official guidance or follow company-approved templates for fear of making mistakes, stars will test boundaries, discover creative applications and build personalized workflows long before their organizations catch up. If an AI experiment goes sideways, they are more likely to get a pass—or at least the benefit of the doubt.

根据我的研究，顶尖人才的声誉和地位赋予了他们在工作中的自主权和决策空间。这意味着明星员工更有可能立即投入并开始试验人工智能。当普通员工因为害怕犯错而等待官方指导或遵循公司批准的模板时，明星员工则会突破界限，发现创新应用，并构建个性化的工作流程，这远在他们的组织能够跟上之前。即使人工智能实验出了岔子，他们也更有可能被放过——或者至少得到善意的理解和信任。

Then there is the question of getting credit.

接着就是关于如何获得认可的问题了。

Decades of research show high-status individuals gain outsize credit for doing work similar to that of low-status employees. That suggests that when AI assistance is invisible—which it often is—observers are likely to fill in the gaps based on what they already believe about the employee. Stars will get the benefit of the doubt: Their AI-enhanced work becomes proof of their superior judgment and strategic thinking. Average performers face the opposite assumption: If the work is exceptional, AI must have done it.

数十年的研究表明，地位较高的人在完成与地位较低的员工相似的工作时，往往会获得不成比例的巨大赞誉。这意味着，当人工智能的辅助是隐形的——这通常是事实——观察者很可能会根据他们对该员工已有的看法来填补空白。明星员工将获得信任：他们借助人工智能完成的工作会被视为他们卓越判断力和战略思维的证明。而普通员工则面临着相反的假设：如果工作成果非凡，那一定是人工智能的功劳。

This creates a vicious double bind for average employees. They’re already less equipped to leverage AI strategically, but even when they do manage to produce outstanding AI-assisted work, they’re unlikely to get the career-advancing recognition that comes with it. Sometimes just the suspicion of AI involvement is enough to diminish how others view their contributions.

这给普通员工带来了严峻的双重困境。他们本来就在战略性地利用人工智能方面能力不足，但即便他们真的设法借助人工智能完成了出色的工作，也很难获得随之而来的、有助于职业发展的认可。有时，仅仅是怀疑工作中有AI的参与，就足以让别人对他们的贡献评价大打折扣。

How to level the field

如何拉平差距

So, what can companies do to prevent AI from turning stars into an untouchable class? I suggest three things:

那么，公司能做些什么来防止人工智能将“明星员工”变成一个“不可触及的阶层”呢？我建议以下三点：

• Encourage everyone to experiment with AI. While stars are quietly building personal AI workflows, most employees are waiting for official guidance that may never come. Smart leaders should create “AI sandbox” time where all employees can test tools without fear of making mistakes, and establish cross-training programs that pair average performers with early adopters.

鼓励所有人尝试AI。当明星员工正在悄悄构建个人AI工作流时，大多数员工却在等待可能永远不会到来的官方指导。明智的领导者应该创建“AI沙盒”时间，在其中所有员工都可以无惧犯错地测试工具，并建立交叉培训项目，让普通员工与早期采用者结对学习。

More important, they should invest in AI-literacy training that goes beyond basic tool usage to include prompt engineering, output evaluation and strategic-task delegation. The goal isn’t to eliminate stars’ expertise advantage, but to teach the learnable skills that can level the playing field.

更重要的是，公司应该投资于AI素养培训。这种培训不应只停留在基本的工具使用层面，而应包括提示工程（prompt engineering，即如何有效地向AI提问）、输出评估以及战略性任务分配。其目标并非要消除顶尖人才的专业优势，而是要传授那些可以帮助普通员工提升能力、从而使竞争环境更公平的技能。

• Spread the knowledge. Since AI responds best to clear, detailed inputs, leaders need to train average performers to adopt work habits that will enable them to get the most from AI. This means providing templates for organizing information and creating shared collections of effective AI prompts, strategies and use cases. Rather than letting stars hoard their discoveries, make sharing knowledge a standard practice. When one employee discovers an effective AI workflow, capture it and spread it across teams so it becomes something everyone can use rather than one person’s secret advantage.

传播知识。由于人工智能对清晰、详细的输入反应最佳，领导者需要培训普通员工养成有助于他们充分利用人工智能的工作习惯。这意味着要提供组织信息的模板，并创建有效的AI提示、策略和用例的共享集合。与其让明星员工独占他们的发现，不如将知识共享设为一项标准实践。当一名员工发现一种高效的AI工作流程时，要将其记录下来并在团队中推广，使其成为每个人都能使用的资源，而不是某个人独有的秘密优势。

• Redesign employee-evaluation systems to account for AI-augmented work. The bias that gives stars disproportionate credit for AI-assisted work will only worsen if left unchecked. To fix that, companies should establish clearer guidelines about AI disclosure. They should develop evaluation criteria that fairly assess AI-assisted work regardless of the performer’s existing status. And they should train managers to recognize when bias might be skewing their assessment of an employee’s performance. Consider implementing “AI transparency” practices where teams share how they use AI tools, making the assistance visible rather than hidden.

• 重新设计员工评估体系，以适应人工智能辅助的工作方式。如果不加以制止，那种让明星员工在AI辅助工作中获得不成比例功劳的偏见只会恶化。为此，公司应该制定更清晰的关于AI辅助工作披露的指导方针。他们应该制定评估标准，公平地评估AI辅助的工作，无论员工现有身份如何。此外，他们还应培训管理者，使其能识别偏见何时可能扭曲对员工绩效的评估。可以考虑推行“AI透明化”做法，让团队分享他们如何使用AI工具，使其辅助作用清晰可见而非隐藏。

Without these systemic changes, AI risks creating a two-tier workforce where a small group captures most opportunities and everyone else falls further behind.

如果缺乏这些系统性的变革，人工智能就有可能形成一个两级分化的劳动力结构，其中一小部分人将占据绝大多数机会，而其他人则会进一步落后。