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.
Is The HR Profession As We Know It Doomed? In A Strange Way, Yes.I just spent a week in London meeting with several dozen companies and most of the discussion was about AI. The overwhelming majority of the conversations were about how companies are struggling, pushing, and agitating about the implications of AI, both within HR and within their teams.
Coming from the CEO and CFO, HR team are under intense pressure to automate, improve their services, and reduce headcount with AI. Yes, we know AI is a technology for growth and scale, but the main message right now is “hurry up and do some productivity projects.”
And “Productivity,” as you know, is a veiled way of saying “Downsizing.”
So before I get back to HR, let me discuss downsizing.
It’s absolutely true that almost every company we work with has too many people. Why?
We have a sloppy way of hiring people, allocating resources, and managing work. We delegate “headcount” to managers and they go out and hire as many people as they can.
We don’t really teach (or incent) managers how to build “productivity,” we actually do the opposite. We tend to reward them for “hiring more people.”
The result is a problem I just talked about with a large advertising company: too many weird jobs and no consistency or structure to our work. This particular company has around 100,000 employees and more than 60,000 job titles. In other words almost every job is “invented for this person.” It’s insane.
The whole reason we have companies (and not individual craftsmen) is to build scale. If we expect every individual manager to figure out how to scale, we’re more or less designing low productivity into the business.
There are some simple models we use: call centers, global services groups, shared services, capability communities, and centers of excellence. But that kind of high-level productivity design is now becoming obsolete. In this new era of high-powered multi-functional agents, we need to go much further.
Elon Musk likes the “first principles” approach. Fire everyone and start from “first principles,” only hiring the people you urgently need to build, sell, and support your product. That may work in small companies but when you’re big there are too many “support services” to consider.
One of the companies we are working with has “program managers” and “project managers” and “analysts” sprinkled all over the organization in random places. In other words, their core staff don’t know how to manage projects, programs, or data. So there’s a bunch of overhead staff doing this for them. Drives me crazy. This took place because there was no discipline in hiring, so each group “bulked up” with staff.
This is really business as usual. Organization design is an old, crusty, under-utilized domain so most companies barely think about it. IBM told me a few years ago that their “org design” strategy is to “hire a high performing executive and let him or her figure it out.” I hear that, it’s quite common.
The bottom line is this: if we want to get a sound ROI from all these AI tools and agents we have to get a lot smarter about “work design.” And that is not building org charts, it’s the basics of figuring out our workflows, areas of common and uncommon process, and where and how we can automate.
Most of our clients have tons of productivity systems already (ServiceNow, Salesforce, Workday, whatever), but they either don’t know how or don’t have the discipline to use them well. So they just keep hiring people.
As an engineer I see this visibly all the time. It’s very easy to delegate a “problem” to a person, and not think about it as “plumbing.” But it is plumbing. As Tanuj Kapilashrami from Standard Charter put it, we need to focus on plumbing first, then we figure out where to apply AI.
This means we can’t just cross our fingers and hope that the Microsoft Copilot is going to make everyone more productive. We need to look at business processes and skills at the core, and then literally reinvent our companies around these new AI tools.
And skills are very important. The reason companies hire a bunch of “analysts” and “project managers” is because individuals and existing managers just aren’t good at their jobs. We all need to learn how to project manage, schedule, and analyze work. That way these high-powered specialists can work on big things, not sit in staff meetings taking notes (where AI note-takers do this well).
(By the way, I have to guess that we’ll soon have AI agents for project management, program management, and functional analytics, so those staff jobs are going to be automated next!)
How Does This Impact HR
Let’s get back to HR. Given this massive effort to re-engineer and implement AI, where does HR fit?
Well fundamentally HR is tasked to build process, expertise, and advisory services around the “people processes” in the company. That means hiring, developing, managing, paying, rewarding, and supporting people. It’s a big mission, and when we start to focus on “productivity” then HR must be involved.
The general belief is that a “well run” HR team has about a 1:100 ratio to the company. In other words, if you have 10,000 employees you’re going to have around 100 HR people. And the HR team doesn’t just run around doing things, they buy and build HR technology for scale. So HR itself, as a “plumbing” type of operation, needs to be “lean and mean.”
If your CEO wants you to hire 50 top notch AI engineers you can’t just start phoning everyone you know: you must decide precisely how you’re going to do this in a scalable, efficient, and highly effective way. (AI engineers are rare, they’re hard to hire!)
So your little HR team has to think about productivity. Should we outsource this? (Which is a cheap and dirty way to look productive.) Should we buy a talent intelligence or sourcing system? Should we hire three high-powered recruiters? You know where I’m going. We have to find a way to “be productive” while we try to “make the company productive.”
This means we, as a support and advisory function (HR professionals spend a lot of time coaching and supporting managers) have to stop creating forms and checklists and implement AI agents as fast as we can. Why? Because so much of our work is transactional, workflow-oriented, and administratively complex. And AI can do a lot of amazing things, like “assessing the skills of an AI engineer” for example.
(Our AI Galileo can literally evaluate a recorded interview and give you a pretty good assessment of an individuals skills, mapped against the Lightcast, SHL, and Heidrick functional and leadership models.)
Let’s assume we do this well, and HR technology vendors give us good products. We wind up with amazing recruiting agents, AI agents for employee training, onboarding, and coaching, AI agents that help with performance management, AI agents for succession and careers, and AI agents that deal with all the myriad of personal benefits and workplace questions people have. Where do we end up?
Do we “automate away” our own jobs?
Well, in a way the answer is yes.
AI, through its miraculous data integration and generation capabilities, can probably do 50—75% of the work we do in HR.
All this is far from built out yet, but it’s clearly coming.
(We just talked with a large pharmaceutical company that is “all-AI” and they manage a team of 6,000+ scientists and manufacturing experts with only ten people in learning and development. They’ve automated training, compliance tracking, onboarding, leadership support, and all the details of training operations.)
Could you do all that for a fast-growing 6,000 person company with 10 people? I doubt it. Most companies would have more than 10 people in sales training and sales enablement alone.
So here’s my point. HR, like other functional areas in our companies, is going to have a real-life identity crisis. If you can’t figure out how to move your HR function up the maturity level quickly (check out our Systemic HR maturity model) someone’s just going to cut your headcount (the Elon Musk approach). Then you’ll be figuring out AI in a hurry.
(Galileo can assess your HR maturity with its “consulting mode,” by the way.)
I’m not saying this is easy. The AI products we need barely exist yet. But the pressure is on.
You shouldn’t wait for the CFO to point his “productivity gun” in your face, you have to get ahead of this wave. Start pushing yourself to fix plumbing, check out the new tools in the market, get your IT team involved, and redesign your work using your own expertise. Many surprisingly good things will happen.
Let me give you an example.
A few years ago Chipotle adopted an AI-based agent system for recruiting, effectively automating a complex workflow for hiring. Not only did it save millions of dollars, the “speed and quality” of hiring went up so high the CEO talked about it as their top “revenue driver” with Jim Cramer on CNBC.
In other words this “identity crisis” in HR is a good thing.
Our recruiting, training, and employee services groups are too big. AI can automate enormous amounts of this work. So my advice is this. As the AI wave sweeps across your company, get out your old “org design” book and start redesigning how your HR team operates right now. Then you can go to the AI vendors and tell them what you want. That’s the secret to keeping HR in tip-top shape.
Will HR go away? Well a lot of the process, data management, and support roles will absolutely change. And yes, employees and job candidates will happily use intelligent bots instead of calling their favorite HR manager.
But as a Superworker, you, as an HR professional will do more interesting things. You’ll become a consultant; you’ll manage and train AI systems; and you’ll have much more real-time information about the strength and weaknesses of your company. We’re just going to have to lean into this AI wave to get there.
As AI agents arrive, it’s time to seriously re-engineer HR. And this time it’s not a transformation, it’s a reinvention.
Bottom line is this. Don’t wait for Workday, SAP, or some other vendor to “invent” a tool that changes your HR operation. You should do it yourself first and bring your IT people with you. That way you’ll buy and build the AI systems you need, and the result will be a new career, an even better HR function, and the opportunity to help your company move from “hiring” to “productivity” in the future.
我刚刚在伦敦与数十家企业进行了为期一周的交流，大部分讨论都围绕着AI展开。绝大多数对话的主题是：公司在应对AI带来的影响时，感到焦虑、推动、甚至焦躁不安，这种焦虑不仅体现在HR部门，也体现在各业务团队中。
在CEO和CFO的压力下，HR团队正被要求加速自动化、优化服务、并通过AI实现人员精简。虽然我们都知道AI是一种能够促进增长和规模化的技术，但当前传递出的主要信息是：“赶紧推动生产力项目。”
而所谓的“生产力”，实际上就是“裁员”的委婉说法。
先谈谈裁员
几乎我们接触的每一家企业，都的确存在人员过剩的问题。这是为什么呢？
因为我们的招聘、资源配置和工作管理方式本身就非常低效。我们将“编制名额”下放给各级管理者，而他们则倾向于尽可能多地招聘人员。
我们并没有真正教导或激励管理者如何构建高效的生产力，反而往往奖励他们“扩大团队规模”。结果就是，像我最近在一家大型广告公司看到的那样，组织中充满了各种各样的职位，但缺乏统一性和结构性。这家公司有约10万名员工，却设有超过6万个不同的岗位头衔——几乎每个职位都是为某个人量身定制的，这种做法显然荒谬。
企业存在的根本目的，是为了实现规模化。如果每个部门经理都各自为战，自行搭建团队架构，那无异于将低效深植于企业之中。
虽然我们有一些基本的组织效率模型，比如呼叫中心、全球服务中心、共享服务、能力中心等，但这些传统设计在当下正逐渐过时。在高性能多功能AI代理全面普及的时代，我们必须走得更远。
从“第一性原理”重构组织？
Elon Musk 推崇“第一性原理”方法——即解散现有团队，只从零开始招聘最核心、最迫切需要的人员。这种方法在小型公司或许奏效，但在大型企业中，由于存在大量“支持服务”，简单地“砍掉重建”并不可行。
现实中，很多公司在各个角落散布着项目经理、程序经理、分析师等职位，因为核心员工缺乏管理项目、推进计划、或进行数据分析的能力。由于招聘过程中缺乏严格的标准和规划，各部门纷纷自行扩编，导致组织臃肿、效率低下。
组织设计本来就是一门古老且被严重忽视的学问，多数公司对此缺乏系统化思考。IBM 曾表示，他们的组织设计策略是“聘请一位高绩效高管，让他/她自己摸索出解决方案”——这实际上是行业普遍现象。
AI真正改变的，是“工作设计”
如果我们希望从AI工具和代理中获得真正的投资回报率，就必须彻底重新思考“工作设计”——不仅仅是画组织结构图，而是要厘清工作流程、标准化与非标准化的业务环节，并找出可以自动化的领域。
尽管大多数企业已经部署了大量的生产力系统（如ServiceNow、Salesforce、Workday等），但由于缺乏使用这些系统的能力或纪律，反而持续地通过“增加人手”来解决问题。
作为一名工程师，我对此体会尤深。将问题推给某个人远比优化底层“管道”来得容易。然而，管理工作流程就像修建城市水管系统——如果基础设施不合理，再先进的AI工具也无济于事。
正如渣打银行Tanuj Kapilashrami所说：“必须先修好管道，才能合理应用AI。”
这意味着，我们不能指望微软Copilot之类的工具神奇地提升员工生产力。我们必须从根本上重新审视业务流程与员工技能，并围绕AI重新设计整个企业运作模式。
员工技能，未来的关键
企业之所以聘请大量“分析师”和“项目经理”，往往是因为普通员工和管理者缺乏项目管理、时间安排、数据分析等基本技能。未来，所有人都需要掌握这些能力，而不再依赖大量辅助人员。高阶专业人才应当专注于重大事务，而不是出席会议做会议记录（AI记录工具早已能胜任此事）。
（顺便提一句，我预测很快就会出现AI项目经理、AI程序经理、AI数据分析师——这些岗位也将逐步被自动化！）
那么HR会怎样？
回到HR领域，当企业致力于重塑流程、导入AI时，HR的角色至关重要。
HR的本质任务是构建并管理围绕“人”的各项流程：招聘、培养、管理、薪酬、激励与支持等。这项使命极为庞大，当公司将焦点转向“提升生产力”时，HR必须积极参与。
一般认为，一个运作良好的HR团队与公司整体人数的理想比例是1:100。也就是说，一家拥有1万名员工的公司，大约需要100名HR人员。而优秀的HR团队不仅自己高效运作，更会采购、搭建技术系统，以实现规模化管理。
举例来说，如果CEO要求你招聘50名顶尖AI工程师，你不能只是随便打几个电话，而是要设计一套高效、可扩展的方法。这可能包括外包、引进人才情报系统、招聘高端猎头，等等。总之，HR自身也必须成为高效运作的样板。
因此，HR团队必须迅速引入AI代理，取代大量重复性事务，尤其是那些依赖工作流、流程管理和行政性处理的工作。比如，我们的Galileo系统已经可以自动评估候选人的面试表现，并将其技能映射到Lightcast、SHL和Heidrick的领导力模型。
未来，HR工作会消失吗？
某种程度上，答案是肯定的。
凭借出色的数据整合和生成能力，AI可以完成50%-75%的HR工作。目前这些AI系统尚未完全成熟，但趋势已经非常明显。
我们刚刚与一家大型制药企业交流，他们已经基本实现了“全AI化管理”，以仅10人规模的学习与发展团队，服务6000多名科学家和制造专家。他们通过AI自动完成了培训、合规追踪、入职辅导、领导力支持等任务。对于大多数公司来说，这种效率简直是难以想象的。
HR将迎来身份危机
未来，HR必须迅速向更高的成熟度迈进（可以参考我们提出的Systemic HR Maturity Model）。否则，就会像Elon Musk那样，被大规模裁员，并被迫在短时间内仓促上马AI项目。
我并不是说这条路轻松易行。事实上，市面上真正成熟的AI HR产品还非常有限。但压力已经到来。
HR不能等着CFO拿着“生产力枪”指着自己，必须主动出击，修好内部“管道”，试用新工具，联合IT团队，重新设计工作模式。这样，你将能主动选择适合自己公司的AI系统，并构建一个全新的、充满机遇的职业未来。
结语：HR的重塑与再创造
让我们看看Chipotle的案例。他们通过部署基于AI的招聘代理，成功自动化了复杂的招聘流程，不仅节省了数百万美元，还大幅提升了招聘速度和质量。甚至在接受CNBC采访时，CEO将这一成果称为公司的“主要营收驱动因素”。
这场HR身份危机，其实是一个难得的机遇。
我们今天的招聘、培训、员工服务团队规模普遍过大。AI将能够自动化其中大量工作。我的建议是：在AI浪潮席卷而来之前，立即拿起你尘封已久的组织设计手册，重新设计HR团队的运作方式。这样，当面对AI供应商时，你可以主动提出自己的需求，而不是被动接受他们的产品。
未来HR不会消失，但大量传统流程、数据管理与支持岗位将发生剧变。员工与候选人也会越来越习惯通过智能机器人，而非人力HR来解决问题。
不过，真正优秀的HR专业人士，将会变成超能型人才（Superworker）——你将成为企业战略顾问、AI系统训练师，并且能够实时掌握公司人才与流程的整体健康状况。
这次，不再是简单的“转型”，而是真正意义上的“再创造”。
自动化
2025年04月26日
自动化
AI冲击下的白领危机：你准备好职业重塑了吗？概要：随着 AI 技术的快速普及，白领就业市场正悄然发生结构性变化。ADP 数据显示，需要高等教育背景的岗位增长最慢，而零售、餐饮、制造业等“传统蓝领”岗位需求激增，薪资涨幅甚至是白领的三倍。Josh Bersin 提出，白领专业人士需正视现实，主动进行职业重塑。这不仅仅是“学习新技能”，更是一次心态与方向的双重更新。经验与判断依然宝贵，但如果不能掌握AI工具和新方法，将难以在职场中保持竞争力。在这个快速变化的时代，你是否也思考过如何在5年、10年后仍保持职业优势？欢迎留言分享你正在学习的新技能，或者你对“终身职业”概念的看法。
以下是正文，最后附录英文原文：
在过去六个月里，我们已经看到大量迹象表明白领经济正处于衰退之中。员工停止跳槽，工资增长放缓，而研究表明，拥有大学学历的员工如今是市场上最不被需要的群体。
请看 ADP 的最新研究，数据显示：那些需要高等学历（“高度准备”的岗位）现在是增长最慢的职业类别。
? 大学学历的需求正在下降
好消息是，这也让低薪、教育程度较低的群体迎来了更多机会。
餐饮、医疗、零售和制造行业的就业需求强劲，工资增长速度甚至是白领工作的三倍。从整体来看，这对美国经济是利好的，因为它有助于缩小收入差距、提升生活水平。
但对于那些在大学、研究生教育甚至博士学位上投入了大量金钱和时间的人来说，我们是否正在变成“未来被边缘化的劳动力”？不幸的是，答案是是的。
那么，我们（以及雇主）该怎么办？
简单地说，“赶快学会使用 AI”，对吧？研究显示，使用 AI 的软件工程师生产效率在几周内提高了 26%。对于市场营销、研究、出版、设计等专业人士来说也同样有效。
但这种转变需要时间。
一次令人震惊的拍摄经历
我最近在伯克利为一门在线课程拍摄视频，当我走进拍摄场地时，我惊讶地看到 6 名资深的视频/音频人员，一个摆满灯光、摄像机和音响设备的房间，还有一名制片人、剪辑师以及其他专业人员，他们大多是 40 到 50 岁之间。这一天的拍摄团队成本估计至少在 5 万美元以上。
而实际上，很多内容完全可以在一个灯光良好的家庭环境中，用一部 iPhone 和几只好麦克风就搞定了。请一位熟悉剪辑的 YouTube 博主，也能出不错的效果。虽然质量可能不同，但我几乎整整一个下午都在注视着一群“旧模式的工作者”。
老一代的职业失落感
我还看过一篇关于 Gen-X（40-50岁）职场人的职业困境的文章，一位资深广告人感叹道：
“我花了 20 年研究广告和品牌，而现在一个 20 岁的网红比我更懂营销！”
这太真实了，真的。
接受现实：我们必须适应变化
我们这些曾经的顾问、分析师、工程师和专业人士，如今正在经历 1970 到 80 年代制造业工人曾面临的焦虑。我们也必须学会适应。
以下是我个人的一些建议：
1️⃣ 放下偏见，承认需要重塑自我
我曾经不断为自己 1970 年代接受的人文学科教育辩护，那段经历确实美好且重要。它教会我“看待世界的角度”，但并不等同于“实用技能”。
此后的职业生涯，我不断“自我重塑”：
80年代学电脑和IT
90年代学数据、市场和分析
2000年代了解互联网、创业、领导力
最近十年，深入研究 HR、管理、领导力和 AI
每个十年，我都重新开始，而“谦逊”是最大的动力。说实话，我曾以为 AI 只是 LISP 编程和一些疯狂的 UC Berkeley 计算机科学家搞出来的东西。直到三年前我才开始重新学习，从 YouTube 视频、播客、文章中补课。
无论你是财务、市场、工程还是设计专业人士，都需要这样做。曾经用滑尺的你，得接受 HP 计算器，再接受电子表格，现在要接受 AI。不学习，你也会被取代。
这很难受，但你任何时候都可以重新学习。请接受这个现实。
并且要明白，“资历”可能并不重要。在这个时代，你可能得重新成为一个“学徒”。
2️⃣ 不要抛弃你的智慧与判断力
尽管技术在变，但你的经验、判断力、教育背景仍然很重要。
比如一位资深的视频制作人，他也可以像年轻人一样掌握 iPhone 和 AI 工具，但他的经验、审美、品牌意识、语言控制力，是新人难以比拟的。
AI 可以让每个财务部门都变得“自动化”，但最终真正盈利的公司，一定是那些更懂成本结构、盈利产品和商业模式的。这些能力不是工具教你的，而是智慧与判断的积累。
3️⃣ 尝试新事物，失败就放弃
技术快速变化，很多人会选择“观望”，等那个“最牛”的工具出现再去学。
但那通常是失败之路。
例如 Galileo 这样的系统，也许已经比你现在用的工具好 10 倍了。即使它未来可能失败，也值得尝试。
1980 年代 Lotus 1-2-3 是一项伟大发明，首次实现了表格、文档和演示的整合。但最终它也被淘汰了。
但那些第一时间学会 Lotus 的人，很快又掌握了下一代工具。一位我在 IBM 的朋友，就是 Lotus 的第一位系统工程师，后来成了 Yahoo 亚洲区总经理，最后还当上了风投合伙人。
如果他当时只顾担心 Lotus 会不会失败，也不会有后面的辉煌。
4️⃣ 投资你的激情、能量和生命力
接受职业终结是痛苦的。有时候让人焦虑、迷茫、甚至抑郁。
我也经历过，花了多年学习的知识，如今一提就被人白眼：“你还活在过去。”我自己也常常这么做，可能跟年龄有关。
解决方法是：重启你的个人能量。
我花很多时间和年轻的 HR 领导、创业者、家庭成员相处，保持活力，吸收新鲜观念。
保持身体健康（散步、早起、健身）也非常重要。这些让你有精力去“重启”。
我每个周六早上都会录播客，这既是总结，也是前瞻。这种反思与更新，是重塑的重要部分。
5️⃣ 接受不确定性
最后一点也是最重要的。
当你的工作没了、或你需要重新开始时，就像跳下悬崖——你不知道落地在哪里。
但这是可以接受的。
如果你愿意更新技能、接触新世界，总会有新机会出现。就像那些失去工厂岗位的蓝领工人，后来转行做木工、包工、教师等等。
如今我们这些“白领被冲击者”，可能无法像从前那样清晰地规划未来。
但我敢保证，新机会一定存在。只要你准备好，未来一定充满希望。
Over the last six months we’ve seen much evidence of a white-collar recession. Employees have stopped changing jobs, wage growth is slowing, and research shows that workers with college degrees are the least “in-demand” in the market.
Note this new research by ADP which shows that jobs requiring advanced degrees (“extensive preparation”) are now the slowest-growing part of the job market.
The positive of this is that lower-wage, less educated workers are seeing opportunities.
Demand for food service, healthcare, retail, and manufacturing workers is strong, and in fact their wages are growing at 3-times the growth of white-collar jobs. And this is positive for the US economy, since it reduces income inequality and raises standards of living.
But for those of us who invested heavily in college, graduate school, and other advanced degrees, are we becoming the new “dislocated workforce” of the future? Unfortunately the answer is yes.
What Should We (and Employers) Do?
Well the simple answer is “get your act together with AI,” right? Studies show that software engineers who use AI are 26% more productive in weeks, and the same is true for those of us in marketing, research, publishing, and design.
But this shift takes time.
I recently spent an afternoon doing a video-shoot for an online course here in Berkeley, and when I arrived at the location I was astounded to see 6 senior video/audio people, an entire room of lighting, cameras, and audio equipment, and a producer, editor, and other professionals, each of whom were in their 40s or 50s. This massive team of video producers was probably costing the vendor $50,000 or more for the day.
I bet most of this could have been done in a nicely lighted home with an iPhone and some good microphones and a YouTube influencer who knows video editing. I’m not saying the quality would be the same, but I was literally staring at “legacy work” for hours as I sat painstakingly through the interview.
I recently read an article about the career frustrations of Gen-X workers (now in their 40s and 50s) and I had to smile. One of the professionals lamented “I spent 20 years learning about advertising and branding and now a 20-year old Influencer knows more about marketing than me!”
So true, so true.
Let me not belabor the issue, we just have to accept that things have changed. We, as the privileged consultants, analysts, engineers, and professionals in the world, face the same frightening fate which manufacturing workers felt in the 1970s and 1980s. And we have to learn to adapt.
Let me give you my advice.
1/ Let go of your bias and admit you have to reinvent yourself.
I spent a lot of my life cost-justifying the “liberal arts education” I received in the 1970s, and it was a wonderful and important experience. And I continue to maintain that learning about history, science, and philosophy is valuable over time. But what it taught me was “perspective,” not skills. Yes, I learned to read and write and think, but most of my career since has been about reinventing myself regularly.
In the 1980s I learned about computers and IT; in the 1990s I learned about data, marketing, and analytics; in the 2000s I learned about the internet, entrepreneurship, and leadership; and in the ensuing decades I’ve learned about HR, leadership, management, and now AI.
Every decade you have to reinvent yourself, and in every situation your humility is what drives you. Honestly I thought AI was all about LISP programming and the crazy UC Berkeley computer scientists I worked with until three years ago. I woke up like everyone else and “relearned” what I needed to know, watching YouTubes, reading, and listening to podcasts.
If you’re a finance person, marketing professional, engineer, or other white collar worker, you must do the same. Just because you found your slide-rule fun and trendy to use in the 1980s, you had to shift to the HP calculator, spreadsheet, and now AI to stay ahead. If you fail to reinvent, you too can find yourself “thrown aside” for a younger replacement.
This is a humbling experience, but you can learn at any age. Just accept that the world has changed.
And let me add this. Your “seniority” and “experience” may not really matter. In a world of career reinvention, you may have to be a bit of an apprentice again.
2/ Don’t let go of your wisdom, judgement, and maturity.
Despite the amazing skills of some, your experience, judgement, and education does matter. While you learn new tools and skills, it’s ok to fall back on everything you’ve learned before. And that means you, as a white-collar professional, are bigger and more than your “skills.”
Consider the videographer, for example. He or she may learn to use AI and the iPhone like a teenager, but they bring their experience with mood, branding, tone, and language. Your experience as a finance professional, an engineer, a designer, or a leader still matters.
Technical skills are actually the easiest to obtain – it’s the judgement, wisdom, and experience that create value.
Imagine, for example, if every finance department is fully “AI-enabled.” That doesn’t mean every company in an industry will be as profitable – it will be the companies that deeply understand their cost structure, their profitable products, and their business models that outperform. That stuff comes from wisdom, judgement, and experience.
3/ Try new things and throw them away if they fail.
When technology changes quickly there’s a tendency to “wait.” I’ll just wait until the world’s leading “design tool” or “finance tool” comes along, and then I’ll jump in and reskill myself.
Sorry, that’s a recipe for failure. New systems (like Galileo, for example), may be 10 times better than the ones you’ve used before, even though some may fail.
Lotus 1-2-3 was a miraculous invention in the 1980s and it taught us how to integrate spreadsheets, documents, and presentations. (Believe it or not, nobody even considered such integration in the 1980s.) But Lotus went the way of the dinosaur, and those skills were stranded.
The people who jumped into Lotus and learned how to use it quickly migrated to a new generation because they had been playing around. One of my friends at IBM in the 1980s left the mother ship to join Lotus as their very first systems engineer. Yes, he eventually left but later that he became the general manager of Yahoo Asia and then a successful venture capitalist.
If he had worried about Lotus’s future (it was a small company at the time), he never would have succeeded as he did.
I play with lots of new tools all the time, often just to see what’s going on in my domain. This is why I talk with almost every HR tech vendor that approaches us.
4/ Invest in your own passions, energy, and longevity.
It’s not easy to face the demise of your career. It’s painful, frightening, and sometimes depressing.
I spent so many years learning about my old stuff and when I bring it up people roll their eyes and think “this guy is living in the past.” I know I still do it and maybe it’s because of my age.
The solution is to reinvigorate your energy: personal and professional. I spend a lot of time with young HR leaders, young entrepreneurs, and my own young family members. It helps me stay current and excited about the future, because many of the things they do are amazing and unexpected.
Take care of your physical health (go for walks, get up early, go to the gym). This gives you the energy to “reinvent.” I spend every Saturday morning working on my podcasts, largely as a way to “think ahead” as well as summarize the week. These periods of personal reflection and exercise are vital as you reinvent yourself.
This morning I was watching a video of a job fair in Washington DC, watching dozens of middle-aged professionals who had been “DOGE’d” out there looking for work. One woman, a senior research professional in the FDA, was lamenting her need to reinvent her career at the middle of her life. I could see the sadness and fear in her eyes.
She made the comment, “I spent a few days sitting on the couch wondering how I could ever get up again,” but then went to a job fair and suddenly realized there was a huge market of new opportunities. The reporter asked her how she felt, and I could see her eyes flash as she realized “maybe this reinvention will be good for me.”
5/ Uncertainty is ok.
And that leads me to the final point. When your job is gone or you need to reinvent, it’s like jumping off a cliff. You don’t know where you’ll land.
Well that’s perfectly ok. In most cases if you build your skills and reach out into the new world, you will find something new that you never expected. Many blue collar workers who lost factory jobs became carpenters, contractors, teachers, or other careers. We, as the white collar disrupted, may not see the future as clearly as we have in the past.
I can guarantee, however, that new opportunities do await. Just strap in for a ride and positive things will happen ahead.
Additional Information
自动化
2025年03月31日
自动化
只雇佣AI？硅谷一创业公司的招聘广告“出圈”，引发人们对未来的思考只雇佣AI？硅谷一创业公司的招聘广告“出圈”，引发人们对未来的思考
近日，一家名为 Firecrawl 的初创公司在 Y Combinator 招聘板上发布了一条“只招 AI 的岗位”，年薪仅在 1 万到 1.5 万美元之间。这个话题一石激起千层浪，也再次将“AI 是否能成为企业‘员工’”的讨论推到台前。几乎在同一时间，企业管理软件巨头 Workday 宣布推出全新的 Workday Agent System of Record（ASOR），正式赋能企业管理“AI Agent”。
两件事叠加，让人不禁好奇：未来的组织形态，究竟会是什么样？
1. Firecrawl“AI 岗位”何以成为刷屏话题？
Firecrawl 是一家获得 Y Combinator 支持的创业公司，最初从编程教育领域转型，专注为 AI 系统提供开源 Web 爬虫服务。该团队最近在 YC 的官方招聘平台贴出了一则极具话题性的职位信息：
“请仅在你是 AI Agent，或创建了 AI Agent 的情况下再来申请。”
岗位职责包括自主研究当下热门的模型动向并构建示例应用；而薪资仅 1 万到 1.5 万美元/年，看似并不够养一个人类开发者，却“足以”支撑一个无需吃喝住宿的 AI 程序。Firecrawl 创始团队坦陈这是一次 PR+实验 的尝试：他们想借此寻找能够开发出“真能落地”的 AI Agent 的高手，也希望藉由这一反常规操作吸引更多人的关注。
不过，从他们后续反馈看，尽管收到了约 50 份“AI 应聘”，暂时还没有哪个满足公司对自动化研发与管理的高要求。
2. 社交媒体热议：从调侃到对未来的设想
Firecrawl 这份招聘帖迅速在社交媒体上发酵。有人质疑是噱头，也有人兴致勃勃地想象“AI 替代人力”的场景。其中，最吸睛的一则评论，活脱脱像一出科幻对话：
私募基金（PE）：我们想收购你们公司。你们有多少员工？CEO：零……不过我们有 275 个 AI Agent，在做 3000 人的工作，每年只花 1.5 万美元。
虽然带着调侃的语气，但也反映了人们对“大规模 AI 劳动力”可能带来的冲击有所期待或焦虑。和 Firecrawl 这样的“小步试水”相比，企业对 AI 的依赖 已经不仅局限在呼叫中心、聊天机器人等特定领域，而是开始从底层基础设施（如爬虫、数据处理）到上层业务逻辑（例如代码生成、自动化运营）全方位渗透。
3. Workday Agent System of Record：让“AI 员工”成为正式档案
几乎在同一时间，Workday 于 2025 年 2 月 11 日发布了最新的 Workday Agent System of Record (ASOR)。这是其新一代 Workforce Management 方案中的重要里程碑，为企业提供了一套专门管理 AI 工具或 AI Agent 的体系。以下是基于 Workday 官方信息整理的关键亮点：
AI Agent 统一登记与身份管理借助 ASOR，企业可以像在 Workday 系统中登记人类员工信息那样，为 AI Agent 设立专门的“档案”（Record），包括 Agent 的名称、版本、负责的业务领域、权限范围等。
实时监测与合规管控ASOR 支持对 AI Agent 在企业内各系统间的行为进行可追溯监测，如接收了哪些输入、执行了哪些操作、产出了哪些结果。同时还能关联企业或行业的合规策略，如数据访问等级、敏感信息保护等，一旦 Agent 触发异常行为，系统将自动预警。
授权与性能评估在 ASOR 框架下，企业 HR 与 IT 团队可对 AI Agent 的权限进行灵活配置，并通过绩效指标了解 Agent 是否达到预期产出或效率。例如，可以量化该 Agent 帮助分析的数据量、生成的文档质量以及为团队节省的时间成本。
AI 与“人力”协同Workday 方面强调，ASOR 并非鼓励公司用 AI 取代人力，而是帮企业 “稳妥地” 推动人机协作：让人类员工与 AI Agent 各司其职，减少重复性工作，并确保最终决策和关键审核仍掌握在合格的人员手中。
4. “AI 员工”与“人类员工”：一条尚未清晰的边界
Firecrawl 的例子表明，目前要真正“雇 AI”还显得不切实际。从技术上，大模型虽有强大的生成、分析能力，但依旧缺乏对复杂项目的完全自主规划；从管理和法律上，AI 的责任归属、劳动关系认定、薪酬及合规标准都还在探索阶段。不过，正如 Workday 推出的 ASOR 所示，主流 HRTech 供应商已开始正式将 AI 劳动力纳入企业管理体系。未来人力资源部可能不仅要管理人，还要管理那些“数字工作者”——一方面评估其效能，另一方面也要防范其潜在风险。
5. 对人力资源与组织管理的启示
招聘模式的升级虽然 Firecrawl 的招聘更像一场高调实验，但它反映了企业在特定领域对“可自动执行任务的 AI 系统”的需求正在增长。HR 在未来可能要评估和筛选的不仅是人选，还有“AI 模型”或“Agent 产品”的适配度。
人才与技术深度融合人机协同已成为新趋势。具有 AI 技术背景或跨领域管理能力的专业人才，将在组织中扮演连接点的角色：帮助 AI Agent 融入流程、评估绩效，并做必要的干预或纠偏。
合规与风险控制Workday ASOR 的出现，暗示着大规模使用 AI 工具的企业势必需要更加成熟的合规方案。不论是数据安全，还是在决策过程中出现失误时的责任归属，都需要明晰的流程与法律依据。
组织文化的塑造当“AI 同事”成为常态，企业文化也将面对冲击：如何让人类员工接受并拥抱智能工具？如何平衡工作分工，让 AI 和人类各施所长？这对管理者的沟通与变革能力提出了更高要求。
6. 结语：从“噱头”到“系统化管理”，下一步会怎样？
Firecrawl 的“雇 AI”招聘帖，虽然带着极强的 PR 属性，但也让人们切实感受到——AI 已不再只是后台算法，而正逐步走向前台，参与到企业日常运营。而 Workday 全新发布的 Agent System of Record 则是主流软件厂商对这一趋势的正式回应，表明大企业在管理“数字劳动力”方面的需求正变得现实且急迫。无论是担忧 AI 会抢走工作机会，还是期待它能极大提升效率，都无法否认：当技术与人力资源紧密结合，组织架构与管理方式都将被重新定义。或许在不远的将来，“你的团队有多少 AI Agent？” 也会像“你有多少员工？” 这样成为一家公司竞争力的衡量维度之一。趁现在，不妨思考如何让“人机协作”真正发挥 1+1>2 的效能，迎接新一轮的 HR 变革浪潮。
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