Yes, AI Is Really Impacting The Job Market. Here’s What To Do.Josh Bersin 在 2025 年末指出，美国就业市场正在出现结构性变化。整体失业率上升至 4.6%，其中应届大学毕业生的失业率接近 10%，成为最受冲击的群体。与此同时，不要求大学学历的岗位持续增长，一线员工的重要性正在被重新定义。更值得关注的是信任问题。Edelman 调研显示，70% 的员工不信任企业关于 AI 裁员的说法，只有 27% 信任 CEO。AI 不只是技术工具，而是一场社会与组织层面的转型。Josh Bersin 强调，AI 并非消灭岗位，而是放大能力。真正的挑战在于，企业是否愿意投资年轻人才，是否能用透明沟通化解 AI 焦虑。
详细来看
All year I’ve been studying the employment data and talking with press about the smallish impact of AI on the job market. Most of the slowdown in US jobs, from my data and conversations, has been driven by cost-cutting and general economic uncertainty, not explicit AI job replacement.
Well going into 2026 the situation is changing. The US unemployment rate is now 4.6%, up from 4.2% one year ago (a 9.5% increase) and 3.7% in November of 2023 (a 24.7% increase in two years). These are significant increases, especially considering that unemployment was 3.6% in November of 2022.
This tells me that the US economy is slowing after the post-pandemic “revenge buying” frenzy of 2021 and 2022. And of course US tariffs, inflation, and relatively high interest rates all contribute.
But now let’s look under the covers and break out unemployment into two sub-groups: new college graduates (24 years and younger), and more seasoned workers (age 25-35). Suddenly you see a divergence.
The green line, tenured college graduates, shows a steady unemployment rate below the average. This makes sense: these are experienced employees with skills, judgement, and seasoned decision-making maturity.
The orange line, new college graduates, is trending upward. In fact right now it’s almost 10%, which is the highest it has been since July 2021, the peak recovery from the pandemic. Looking backwards, the only time young college grad unemployment was this high was in 2011, a period of recovery from the 2008 recession. (St. Louis Fed agrees.)
And by the way, to round this out, jobs that do not require a degree are plentiful, roughly 82% of the workforce (up from 79% five years ago). So AI is not only slowing new college grad hiring, it’s also reducing the total number of jobs that require college.
There are three important things happening here:
First, whether it’s correct or not, employers are slowing down entry level hiring. Companies hire new college grads for many reasons (largely for talent pipeline), and many newly minted grads are far more AI-ready than we are. Despite this, it appears to economists that it’s harder than ever for these young folks to compete, so they need to “sell” their AI readiness and learning capacity.
Second, the frontline workforce is becoming much more important. The general automation of white collar work (it’s still early days) and the explosion of jobs in healthcare, social services, retail, repair, entertainment and distribution are making the “college grad” part of the workforce relatively smaller. That’s not to say the money isn’t good, but as a CEO or leader more and more of your energy has to go into supporting these frontline workers. (Read our Frontline-First research for more.)
Third, employees don’t trust CEO talk about jobs. A new study by Edelman shows a massive lack of people’s trust in business leaders (and AI scientists) around AI. This 5,000+ worker survey found that 70% of US workers do not trust statements about AI job reductions. When asked “who you do trust” only 27% of US workers trust the CEO. So we, as leaders, have a trust problem.
Here’s the trust data, and this is all about “Trust in AI’s Value” not “trust in the AI platform.”
AI Is a Socio-Technological Innovation
As I talked about in this week’s podcast, AI is “socio-technological.” It has many societal and sociological impacts.
If only half your employees believe what leaders are telling them, they’re going to hold back, grumble, and resist change. This is why economic insecurity is high: people are concerned about their jobs, careers, and future earnings.
(So AI anxiety could actually lower economic productivity!)
The solution to this is not to ignore the topic, but rather to discuss it openly.
None of us really know how much impact AI will have (I do know most platforms over-sell its value right now), and AI is a little scary.
We have to get comfortable with phones that talk back to us, creepy emails that know our name, avatar-based job interviews, AI-driven career advice, and AI-informed performance reviews. And in 2026 we’re going to see digital twins, robots, and more real-life animations of people at work. (Galileo Learn uses a “Josh Agent” to coach and challenge you as you learn.)
Here’s my advice. If you’re holding back on entry-level hiring you may be making a mistake. Younger staff, who have lived with this technology for more of their lives, are likely to be the ones to most quickly use AI, build with AI, and innovate with its new applications.
People who are tenured tend to see new tools as a way to “speed up what they know how to do.” New employees might just say “why not do it this way?” and bring you the reinvention you need.
Everyone Has The Opportunity To Be A Superworker Now
AI is not a job killer, it’s a big job-leveler.
You, as a younger worker, have access to information and research which was often hoarded by experts. If you’re willing to roll up your sleeves, you can move from “apprentice” to “newly minted expert” quickly. And if you’re looking for a job there’s no excuse for not becoming an expert on the company before you talk with a recruiter.
For senior, more tenured people the same applies. You can’t rely on your experience alone any more: you, too, should be digging in and learning about new technologies, tools, and advancements in your domain.
Employers: Be Careful How You Think
For hiring managers and executives, beware of the “tenure trap” above. Just because a senior person knows your business better, you may find that the young “AI-Guru” right out of college catches up fast. Remember, tenured people may see AI as a way to “do things the old way faster” rather than “rethink the way we work.”
For HR leaders and recruiters, remember one thing. Younger workers may learn faster and ultimately improve productivity at a faster rate (plus they cost less). If you seek out fast-learning AI pioneers they could be your Superworkers of the future.
And for CEOs and other execs, be honest and thoughtful about your plans. All our research points to AI as a “scaling technology,” not one to “eliminate jobs.” The more honest and supportive you are, the faster your employees will adapt and help your company stay ahead.
HR领导力
2025年12月22日
HR领导力
从战略劳动力规划到AI时代的全域劳动力规划Workforce planning has evolved from a headcount-driven exercise into a strategic business function. In the age of AI, organizations must move beyond Strategic Workforce Planning (SWP) toward Total Workforce Planning (TWP) — a model that integrates all types of labor, from full-time employees to contractors, gig workers, and AI-powered automation.Jimmy Zhang emphasizes that AI is reshaping not just how work is done, but who—or what—does the work. Companies that embrace total workforce planning gain agility, visibility, and the ability to optimize talent and technology together. TWP aligns workforce strategy with business outcomes, allowing HR to manage capacity, cost, and capability across the entire ecosystem of work.
作者：Jimmy Zhang（武田制药全球人才招聘负责人）
劳动力规划：从“战略”走向“全域”
在与众多企业与人力资源领导者的交流中，我注意到一个明显趋势：劳动力规划终于成为了战略层面的核心议题。然而，下一个阶段已经到来。
如今，仅仅依靠“战略劳动力规划”已不足够。组织现在需要的是——全域劳动力规划（Total Workforce Planning）。
全域劳动力规划不仅仅是预测人头数或招聘趋势，而是审视整个工作的生态系统。它整合了各种形式的劳动、能力来源与价值创造方式。在一个由人工智能与持续变化主导的环境中，它提出了一个简单但深刻的问题：“我们的工作是如何完成的？”
为什么战略劳动力规划已不再足够
战略劳动力规划帮助企业将人才与业务优先事项对齐，并提前识别能力需求。但在大多数情况下，它仍被传统的组织结构与岗位层级所限制。它依然将“员工”作为分析的主要单位，而如今的工作早已变得分散、灵活，并被技术所增强。
正如Jesuthasan和Boudreau在《Work Without Jobs》中所言：未来的劳动力战略，不是“优化（Optimization）”，而是“统筹（Orchestration）”——设计人、合作伙伴与技术如何共同创造价值。
在我对39项关于AI整合与劳动力转型的研究进行系统性回顾后发现，虽然AI加速了新技能与新结构的需求，但真正完全准备好的组织屈指可数。只有少数企业能被归类为成功的转型者。它们与失败者的区别，不在于技术本身，而在于领导力协同、伦理治理与整合性劳动力规划。
技术决定了“可能性”，而领导力决定了“现实”。
整合业务、财务与劳动力战略
最具前瞻性的组织，都是把劳动力规划直接嵌入到业务与财务战略之中的。
在我研究的最佳实践案例中，劳动力规划被视为全公司共担的职能，而非单纯的HR职责。业务、HR与财务共同拥有关于能力建设、投资与时间节奏的决策权。
这种整合让企业能够模拟多种未来场景，把劳动力能力与业务风险相联系，并将洞察转化为战略执行。
正如Gartner所描述的，这是**“企业整合式劳动力设计（Enterprise-Integrated Workforce Design）”**——一种让能力规划、资本配置与转型管理互为依存、非线性衔接的系统。
当规划真正整合后，讨论的重心就会从“我们需要多少人”转变为——“我们需要怎样的人、合作伙伴与技术组合，才能实现目标？”
从战略到全域：这是一段成熟旅程
从“战略”迈向“全域”并非一跃而就，而是一个成熟曲线（Maturity Journey）。
初期阶段：以人头预测和运营效率为中心；
进阶阶段：转向基于能力的建模和外部市场洞察；
成熟阶段：构建一个“活的劳动力系统”，持续整合内部人才数据、外部趋势与自动化洞察。
在实践中，最可持续的方法是从试点开始：选择一个正在进行转型的业务领域，将其目标与劳动力数据及市场情报相结合。当领导者看到完整图景——包括成本、能力与灵活性——劳动力规划自然会变成一场关于价值创造的战略讨论。
5Bs框架：全域劳动力规划的核心工具
为了指导这些决策，我常使用5Bs框架：Buy、Build、Borrow、Bot、Balance。
该框架帮助企业将战略转化为行动，使能力设计直接连接业务成果。
Buy（购买）：通过外部招聘获得稀缺或新兴技能。
Build（培养）：通过内部培训与再技能化，构建长期能力。
Borrow（借用）：通过合作伙伴与外部人才网络保持灵活性。
Bot（自动化）：运用自动化与智能系统扩大人类能力。
Balance（平衡）：在成本、能力与员工福祉之间保持动态均衡。
在我的研究中，最成功的企业都强调“平衡”：他们选择再培训而非替代，清晰传达目标，让转型成为一种包容的过程，而非取代。
因此，5Bs不仅是决策工具，更是一种将人类、数字与混合形式的工作整合为一体的战略思维模型。
人的维度
所有劳动力模型的背后，都是人。
研究一再表明，成功的转型往往来自员工的早期参与、透明沟通与持续学习投资。在这样的企业中，员工会将变革视为机遇，而非威胁。
平衡不仅是财务层面的，更是道德与心理层面的平衡——确保商业进步与人类尊严并行发展。当员工在企业未来中看到自己的位置，准备度与信任自然随之而来。
在AI时代的领导力
人工智能扩展了规划的可能性。预测分析能够揭示潜在技能、构建情景模型、展现工作的演化方向。
但AI无法取代领导力。
最有效的领导者展现出Nyberg等人所称的**“适应性智能（Adaptive Intelligence）”：以好奇心、共情力与清晰度应对变革。他们不把劳动力规划当作行政流程，而是当作领导工具**，用于统一人、数据与目标。
全域劳动力规划正是为此而生——它让洞察变成前瞻，前瞻变成方向。
为什么5Bs比以往更重要
5Bs不仅是人才决策模型，更代表了应对复杂性的组织思维模式。
成功的企业会：
Buy with foresight（有远见地招聘）；
Build intentionally（有目的地培养）；
Borrow strategically（有策略地借力）；
Bot responsibly（负责任地自动化）；
Balance continuously（持续保持平衡）。
这些选择不仅塑造劳动力规划，更塑造企业文化与组织诚信。
结语：从稳定到适应
未来的工作，不属于那些追求稳定的组织，而属于那些为适应性而规划的组织。
全域劳动力规划不仅是一种流程，更是一种领导哲学——它将商业雄心、人类潜能与技术可能性结合成一个整体系统。
在AI时代，竞争优势不来自“预测未来”，而来自建立平衡、能力与勇气去在变化中蓬勃发展。
参考文献：
Zhang, J. (2025). AI Integration in Workforce Planning: A Systematic Review of 39 Studies.
Jesuthasan, R., & Boudreau, J. (2023). Work Without Jobs.
Bughin, J. (2023). Does Artificial Intelligence Kill Employment Growth?
Nyberg, A. J., et al. (2025). A Brave New World of Human Resources Research: Navigating the GenAI Revolution.
Haipeter, T., et al. (2024). Human-Centered AI Through Employee Participation.
Gartner (2025). Reframing Strategic Workforce Planning for the Modern Enterprise.
HR领导力
HR领导力
HR领导力
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