麦肯锡：AI赋能职场，企业如何跨越管理障碍，实现智能化未来？员工对 AI 的适应速度远超领导层的预期
AI 如何重塑职场？
人工智能（AI）正在以惊人的速度重塑职场生态，许多企业正试图利用 AI 提高生产力、优化决策流程并增强市场竞争力。然而，AI 技术的广泛应用远非一蹴而就，企业的 AI 部署不仅涉及技术升级，更考验管理者的战略眼光和执行力。
麦肯锡的《Superagency in the Workplace》 这份报告深入研究了 AI 在职场中的应用现状，基于对 3,613 名员工和 238 名 C 级高管 的调查，揭示了企业在 AI 落地过程中的机遇与挑战。报告认为，AI 在职场的变革潜力堪比蒸汽机之于工业革命，但当前的最大障碍并非技术问题，而是领导层的行动力不足。
尽管 92% 的企业计划在未来三年增加 AI 投资，但只有 1% 认为自己 AI 发展成熟，表明大多数企业仍停留在 AI 试点阶段，尚未实现全面部署。更值得注意的是，报告发现员工对 AI 的接受度远超管理层的预期，但企业的 AI 发展速度依然滞后。领导者的犹豫和执行力缺失，正成为 AI 规模化应用的最大瓶颈。
本文将从员工接受度、领导层挑战、组织架构变革、AI 治理、商业价值实现等多个维度，介绍报告的核心观点，并补充对 AI 发展的进一步思考。
一、员工比领导更快接受 AI，企业行动缓慢
报告的核心发现之一是：员工已经在积极使用 AI，而领导者仍然低估了 AI 的普及度。
数据显示：
员工使用 AI 的频率比领导层预期高出 3 倍，但许多企业尚未提供系统性培训；
70% 以上的员工认为 AI 在未来两年内将改变至少 30% 的工作内容；
94% 的员工和 99% 的高管都表示对 AI 工具有一定熟悉度，但只有 1% 的企业认为 AI 应用已成熟。
这一现象表明，AI 在企业中的主要障碍并非员工适应能力，而是管理层的滞后决策。许多企业高管仍然停留在探索 AI 价值的阶段，而员工已经在日常工作中广泛使用 AI 工具，如自动生成文档、数据分析、代码编写等。员工在推动 AI 发展方面的主动性，远远超出管理层的认知。
然而，企业未能为员工提供足够的 AI 培训和资源，导致 AI 的应用仍然停留在浅层次，难以转化为真正的生产力提升。例如，48% 的员工认为 AI 培训是 AI 规模化应用的关键，但许多公司仍未建立 AI 学习机制。企业如果不采取措施缩小这一认知鸿沟，可能会错失 AI 带来的长期竞争优势。
二、AI 领导力挑战：速度焦虑与执行落差
尽管 AI 的发展潜力巨大，但报告指出，47% 的企业高管认为公司 AI 发展过于缓慢，主要原因包括：
AI 技术成本的不确定性：短期 ROI（投资回报率）难以量化，导致企业不敢大规模投资；
AI 人才短缺：AI 相关技术人才供不应求，企业缺乏相应的招聘和培养体系；
监管与安全问题：企业在数据隐私、算法透明度等方面的担忧阻碍了 AI 落地。
这种“速度焦虑”让企业在 AI 发展过程中陷入试点—停滞—观望的循环：
试点阶段：部分企业已启动 AI 试点项目，如客服自动化、数据分析等；
停滞阶段：由于短期收益不确定，试点项目难以规模化推广；
观望阶段：企业倾向于等待行业先行者经验，而非主动探索 AI 的商业价值。
报告强调，AI 的落地不仅是技术问题，更是企业管理问题。领导者需要具备更强的战略决心，加快 AI 投资，并明确 AI 在企业中的角色，才能真正推动 AI 规模化应用。
三、如何实现 AI 规模化落地？
1. AI 人才培养
AI 的大规模应用依赖于系统性的 AI 人才培训。然而，报告发现，近一半的员工认为企业提供的 AI 支持有限。企业需要采取措施：
建立 AI 培训体系，涵盖 AI 基础知识、业务应用和 AI 伦理等内容；
推广 AI 试点项目，让员工亲身参与 AI 工具的开发和使用；
设立 AI 激励机制，鼓励员工利用 AI 提升工作效率。
2. 组织架构调整
AI 不能仅仅作为 IT 部门的创新项目，而应当成为企业整体战略的一部分。报告建议：
设立 AI 战略委员会，确保 AI 发展与企业长期战略保持一致；
推动 AI 在各业务部门落地，提升 AI 在实际业务流程中的应用深度；
强化 AI 风险管理，确保 AI 应用在数据安全和监管方面的合规性。
3. AI 治理：平衡速度与安全
虽然 AI 带来了极大的商业价值，但报告指出，企业在 AI 治理方面仍存在诸多挑战：
51% 的员工担心 AI 可能带来的网络安全风险；
43% 的员工关注 AI 可能导致的数据泄露；
企业需要建立 AI 伦理标准，确保 AI 透明、公正、合规。
四、AI 时代的商业价值：企业如何真正实现 ROI？
尽管企业对 AI 充满期待，但报告显示，目前仅 19% 的企业 AI 投资带来了 5% 以上的收入增长，表明大多数企业的 AI 应用尚未转化为可观的商业回报。为了提升 AI 价值，企业需要：
从“技术驱动”转向“业务驱动”，确保 AI 应用直接创造商业价值；
优化 AI 目标设定，明确 AI 在核心业务中的定位；
加强 AI 应用场景探索，特别是在客户服务、供应链管理等高回报领域进行深入部署。
AI 成败的关键在于管理层
AI 的成功不仅依赖技术本身，更取决于企业领导者的执行力和战略眼光。企业若要真正迈向 AI 时代，需要：
加速 AI 战略落地，推动组织变革；
加强 AI 人才培养，提高员工 AI 适应能力；
建立 AI 治理体系，确保 AI 安全合规发展。
在 AI 时代，最危险的不是迈得太快，而是思考得太小、行动得太慢。
附录：《Superagency in the Workplace》 下载
AI investments
2025年03月14日
AI investments
Josh Bersin: When Will The Trillions Invested In AI Pay Off? Sooner Than You Think.近年来，生成式人工智能（GenAI）的投资已达数万亿美元，但围绕其回报问题的争论不断升级。一些分析师，如麻省理工学院教授达隆·阿西莫格鲁（Daron Acemoglu）和纽约大学心理学与神经科学教授加里·马库斯（Gary Marcus），对AI的经济影响持悲观态度，认为其对美国生产力和GDP增长的推动作用有限，甚至可能导致市场崩溃。相反，另一派如高盛的全球经济学家则乐观地认为，AI有望在未来十年内大幅提高生产力。然而，文章指出，生成式AI的真正价值在于其特定领域的应用。例如，Paradox和Galileo等HR技术平台通过高度专业化的解决方案，显著提升了招聘和人才管理的效率。最终，文章强调，AI行业仍处于早期阶段，成功的关键在于找到具有专注性和精确性的创新解决方案。
In the last few weeks there has been a lot of concern that Gen AI is a “bubble” and companies may never see the return on the $Trillion being spent on infrastructure. Let me cite four analyst’s opinions.
Will Today’s Massive AI Investments Pay Off?
MIT professor Daron Acemoglu estimates that over the next ten years AI will impact less than 5% of all tasks, concluding that AI will only increase US productivity by .5% and GDP growth by .9% over the next decade. As he puts it, the impact of AI is not “a law of nature.”
On a similar vein, Gary Marcus, professor emeritus of psychology and neural science at New York University, believes Gen AI is soon to collapse, and the trillions spent will largely result in a loss of privacy, increase in cyber terror, and a lack of differentiation between providers. The result: a market with low profits and big losses.
Goldman Sachs Head of Equity Research Jim Covello is similarly pessimistic, arguing simply that the $1 Trillion spent on AI is focused on tech that cannot truly automate complex tasks, and that vendors’ over-focus on “human-like features” will miss the boat in delivering business productivity. (He studies stocks, not the economy.)
And Goldman Sachs Global Economist, who is a fan, estimates that AI could automate 25% of work tasks and raise US productivity by 9T and GDP by 6.1% over the next decade. He follows the traditional business meme that “AI changes everything” for the better.
What’s going on? Quite simply this new technology is very expensive to build, so we’re all unsure where the payoffs will be.
Buyers Are Looking For A Return Soon
If we discount the work going on at Google, Meta, Perplexity, and Microsoft to build AI-based search businesses, which make money on advertising (Zuckerberg essentially just said that in a few years AI will guarantee your ad spend pays off), corporate IT managers are asking questions.
An article in Business Insider pointed to a large Pharma company that cancelled their Microsoft Copilot licenses because the tool was not adding any significant value (Chevron’s CIO was quoted similarly in The Information).
Another quoted a Chief Marketing Officer who stated Google Gemini’s email marketing tool and the new AI-powered ad-buying tool performed worse than the human workers it was intended to replace (or support).
Given that these tools almost double the “price per user” for the productivity suites, I think it’s fair that CIOs, CMOs, to expect them to pay for themselves fairly quickly.
What’s Going On? The Big Wins Will Be Domain Specific
As with all new technologies that enter the market quickly, “the blush on the rose” is over. We’ve been dazzled by the power of ChatGPT and now we’re searching for real solutions to problems. And unlike the internet, where research was funded by the government, there’s going to be a lag (and some risk) between the trillions we spend and the trillions we save.
Given that ChatGPT is less than two years old and OpenAI has morphed from a research company into a product company, it’s easy to see what’s happening. Every vendor and tool provider is narrowing its AI “strategy” and not just pasting little AI “stars” on their websites, looking for useful things to do. And this process may take a few years.
In the world of HR, I think we can all agree that a “push the button job description generator” is a bit of a commodity. However if the AI analyzes the job title, identifies the skills needed through a large skills engine, and tunes the job description by company size, industry, and role, then it’s a fantastic solution. (Galileo does this, as does SeekOut, SAP, and some other vendors.)
The more “specific” and “narrow” the AI is, the more useful it becomes. Generic LLMs that aren’t highly trained, optimized, and tuned to your company, business, and job are simply not going to command high prices. So while we all thought ChatGPT was Nirvana, we’re now figuring out that highly specialized solutions are the answer.
Let me give you some examples.
The first is the platform built by Paradox, a pioneering company that started work on AI-based recruiting agents in 2016. Paradox, now valued at around $2 Billion, delivers an end-to-end recruitment platform that automates the entire process of candidate marketing, candidate experience, assessment, selection, interview scheduling, hiring, and onboarding. Most people believe its a “Chatbot” but in reality it’s an AI-powered end-to-end system that radically simplifies and speeds the recruitment process in a groundbreaking way. Companies like 7-11, FedEx, GM, and others see massive improvements in operational efficiency and both candidates, managers, and recruiter adore it. It took Paradox eight years to build this level of integrated solution.
The second is our platform Galileo. Galileo, which is now licensed by more than 10,000 HR professionals, is a highly tuned AI agent specifically designed to help HR professionals (leaders, business partners, consultants, recruiters, and other roles) do the “complex work” HR professionals do. It’s not a generic LLM: it’s a highly specialized solution designed specifically for HR professionals, and we’ve added specialized content partners and are building special integrations with other HR platforms. Our clients tell us it’s saving them 1-2 hours a day.
The third is the platform HiredScore, that was recently acquired by Workday. Founded in 2012, the HiredScore team built tools to help identify “fit” between individuals and jobs, and tuned its AI to be highly explainable, unbiased, and very easy to use. It took Athena Karp and the team a few years to nail down the use-cases and user interface but now HiredScore is considered one of the most powerful recruitment “orchestration” tools in the market, and is also used for internal hiring and many other applications. Every customer I talk with tells me it’s essential and saves them months of manual, error-prone effort.
The fourth is the platform Eightfold, which was invented in 2016 as a way to build “Google-scale” matching between job seekers and jobs. Through many years of engineering, product management, and ongoing sales process the company has become the leader in a new space called “Talent Intelligence,” now a billion dollar rapid-growing category. The company is about ten years old and now has some of the world’s largest companies building their hiring, career management, and talent management processes using AI. Companies like EY, Bayer, and Chevron now use it for all their strategic talent programs.
Each of these vendors, including others like Gloat, Sana, Arist, Lightcast, Draup, Uplimit, Firstup, and hundreds of others have patiently taken the power of Generative AI and applied it with laser precision to their solutions. Each of these companies is different, and as we work with them we see lightning bolts of innovation: not in AI itself, but in finding new ways to solve problems and do what I call “crawling up the value curve.”
This is the path for AI in the coming years. As with all new technologies, the “trough of disappointment” is always followed by the “bowling pin” of hitting the nail on the head. Innovators, entrepreneurs, and startup founders are the ones who will take GenAI and apply it in unique ways to solve problems. And soon enough, “AI-powered” will be a phrase we barely even need to say.
The Best Solutions Will Be Narrow Not Wide
GenAI solutions require a large “platform” of data, infrastructure, and software. That alone is not where the value resides. Rather, the big productivity advantages come after years of effort, focusing the data sets and working with customers to find the features, UI designs, and data sets that add enormous value. And we are still in the early stages.
If you want to learn more about HR Technology and AI, join me at the HR Technology Conference on September 24-25 in Vegas, or at Unleash in Paris in October 16-17. While I can’t predict who will win the core AI platform game (Microsoft, OpenAI, Google, Meta, Amazon will fight it out), I can predicts this: Generative AI will deliver massive improvements in business productivity. You just have to shop around a bit and wait for just the right solutions to arrive.
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