There’s No Such Thing as an AI-Ready Culture根本不存在所谓的“AI 就绪”文化

Most companies are transforming for AI faster than their cultures can keep up.大多数公司进行 AI 转型的速度，远超其文化所能跟上的步伐。

Eighty-six percent of employers believe AI will fundamentally change their businesses by 2030. Yet the organizations adopting it were largely built for a different era, one in which information was scarce, expertise accumulated slowly, predictability was prized, and hierarchy determined who made decisions. The result is a familiar frustration for leaders: Technology can change in weeks, while the behaviors, norms, and ways of working needed to capture its value can take far longer.86% 的雇主认为，到 2030 年，人工智能将从根本上改变他们的业务。然而，那些采用人工智能的组织大多建立于另一个时代——彼时信息匮乏、专业知识积累缓慢、推崇可预测性，且由层级结构决定决策权。这导致领导者们陷入了一种熟悉的挫败感：技术可以在几周内发生变革，而捕捉其价值所需的行为、规范和工作方式却需要更长的时间。

So what kind of culture enables an organization to keep evolving as AI changes what the business requires?那么，什么样的文化才能使组织在 AI 不断改变业务需求的情况下持续进化呢？

We initially set out to define the profile of an “AI-ready culture.” But as we examined organizational culture data, interviewed CHROs and culture leaders, studied patterns across client work here at Korn Ferry, and compared them with emerging research on successful AI deployments, we reached a different conclusion: There is no such thing as an AI-ready culture. Readiness suggests a fixed destination; AI keeps moving the destination.我们最初着手定义“AI 就绪文化”的特征。但当我们审查组织文化数据、采访首席人力资源官（CHRO）和文化领导者、研究光辉国际（Korn Ferry）客户案例中的模式，并将其与关于成功部署 AI 的新兴研究进行对比时，我们得出了不同的结论：根本不存在所谓的“AI 就绪”文化。因为“就绪”暗示着一个固定的终点，而 AI 却在不断改变这个终点。

What organizations need instead is a culture of adaptability: the capacity to flex as strategy, technology, and work evolve. That does not mean constant change or a culture where every norm is negotiable. Too much change creates cultural whiplash; too little leaves the organization unable to respond. Adaptable organizations are capable of both “performing”—running the business reliably today—and “transforming”—changing the business for tomorrow. They’ve also developed the judgment to know which mode the moment requires.组织真正需要的是一种适应性文化：即随着战略、技术和工作内容的演变而灵活调整的能力。这并不意味着持续不断的变动，也不是指一种所有规范都可以协商的文化。过多的变动会造成文化上的“鞭打效应”；而变动太少则会导致组织无法做出响应。具备适应力的组织既能“执行”——为当下可靠地经营业务，也能“转型”——为未来变革业务。它们还培养出了判断力，能够识别当下时刻所需要的模式。

The challenge is that most organizations are much better wired to perform than to transform. Korn Ferry’s culture research shows that attributes critical to transformation—including speed, agility, and action orientation—are among those least characteristic of organizations today.挑战在于，大多数组织的架构更适合执行，而非转型。光辉国际的文化研究表明，对于转型至关重要的属性——包括速度、敏捷性和行动导向——在当今组织中恰恰是最不显著的。

So what expands an organization’s capacity to adapt? Across the evidence we examined, five recurring patterns of behavior repeatedly surfaced. Again, they are not a permanent description of the “right” culture for AI. Rather, they are habits that make it easier for an organization to change how work gets done as strategy, technology, and market conditions evolve. In practice, these behaviors represent shifts away from cultural defaults that slow adaptation and toward the muscles organizations will need more of: impact, experimentation, decision systems, enterprise thinking and learning velocity.那么，什么能增强组织的适应能力呢？在我们考察的证据中，五种反复出现的行为模式不断浮现。再次强调，它们并非对 AI 所需“正确”文化的永久性描述，而是让组织在战略、技术和市场条件演变时，能够更轻松地改变工作方式的习惯。在实践中，这些行为代表了从阻碍适应的文化默认模式，向组织未来所需核心能力的转变：即影响力、实验性、决策系统、企业思维和学习速度。

From Activity to Impact从活动到影响力

Where process-centric organizations ask, “Did we follow the prescribed steps?” outcome-centric organizations ask, “Did we achieve the desired result?”流程导向型组织会问：“我们是否遵循了规定的步骤？”而成果导向型组织会问：“我们是否实现了预期的结果？”

An orientation toward outcomes can make organizations more adaptable because it reduces attachment to existing workflows. If a different approach delivers a better result, the method can change. Adaptation becomes a means to performance rather than a threat to it.以成果为导向可以使组织更具适应性，因为它减少了对现有工作流程的依赖。如果采用不同的方法能带来更好的结果，那么方法就可以改变。适应成为实现绩效的一种手段，而非对其的威胁。

In the case of AI transformation, this is where many organizations stall. Leaders celebrate pilots, licenses, and training completion because they are easy to count. But those measures can create a false sense of progress: People appear busy with AI, while the work, operating model, and sources of value remain largely unchanged. The real test of adaptability isn’t whether employees are using AI. It’s whether AI is changing how work gets done—how quickly people decide, how readily they experiment, and how effectively they turn possibilities into better outcomes.在 AI 转型的案例中，这是许多组织陷入停滞的地方。领导者们庆祝试点项目、许可证获取和培训完成，因为这些指标很容易统计。但这些指标可能会制造一种进展的假象：人们看起来在忙于 AI，而实际工作、运营模式和价值来源却基本没有改变。适应性的真正考验不在于员工是否在使用 AI，而在于 AI 是否正在改变工作完成的方式——人们决策的速度、尝试的意愿，以及将可能性转化为更好结果的效率。

At a pharmaceutical company client, AI adoption was being tracked through usage, making activity itself the implied definition of success. We helped shift the intervention upstream into its talent system. More than 500 senior executives were assessed against an AI-ready leadership profile; leadership roles were redefined to include explicit AI accountabilities, and AI outcome indicators were incorporated into performance management and promotion criteria. The message changed from “use AI” to “create value differently with AI.”在一家制药公司客户那里，AI 的采用情况是通过使用率来追踪的，这使得“活动”本身成为了隐含的成功定义。我们协助将干预措施前移至其人才体系中。超过 500 名高管根据“AI 就绪”领导力画像进行了评估；领导岗位被重新定义，明确纳入了 AI 责任，并将 AI 成果指标纳入了绩效管理和晋升标准中。信息从“使用 AI”转变为“利用 AI 以不同方式创造价值”。

The broader principle is simple: Measure work by the value it creates, not the activity it generates, and use those measures for continuous learning and adaptation.更广泛的原则很简单：以工作创造的价值而非其产生的活动来衡量工作，并将这些衡量标准用于持续的学习和适应。

From Certainty to Experimentation从确定性到实验性

When conditions shift, adaptable organizations do not wait for uncertainty to disappear before acting. Nor do they treat ambiguity as an excuse to act rashly. They make experimentation a cultural norm: starting small, learning quickly, and using evidence to decide what to expand, change, or stop. AI can accelerate that cycle by helping teams sense markets, synthesize customer insights, generate options, and simulate trade-offs.当条件发生变化时，具备适应力的组织不会等到不确定性消失才采取行动。他们也不会将模糊性视为鲁莽行事的借口。他们将实验作为一种文化规范：从小处着手，快速学习，并利用证据来决定哪些方面需要扩展、改变或停止。AI 可以通过帮助团队感知市场、综合客户洞察、生成选项以及模拟权衡，来加速这一循环。

A recent Stanford study found a striking pattern: Every successful AI project it examined used an iterative, test-and-learn approach rather than traditional waterfall planning. Sixty-one percent had experienced at least one prior failure, yet in none of those cases was anyone punished for failing.斯坦福大学最近的一项研究发现了一个惊人的模式：其考察的每一个成功的 AI 项目都采用了迭代式的“测试-学习”方法，而非传统的瀑布式规划。61% 的项目曾经历过至少一次失败，但在这些案例中，没有任何人因失败而受到惩罚。

That matters because experimentation is a behavior leaders can design for. Today, only half of employees feel they are encouraged to experiment with new ideas. Willingness grows with explicit permission and psychological safety. Guardrails matter too. Our client work has found that teams often move faster when people understand exactly where they have discretion and where they do not. Clear boundaries create the confidence to experiment inside them.这一点很重要，因为实验是领导者可以进行设计的行为。今天，只有半数员工感到自己被鼓励去尝试新想法。实验意愿随着明确的许可和心理安全感而增长。护栏也很重要。我们的客户工作发现，当人们清楚地了解自己在哪里拥有自主权、在哪里没有时，团队往往行动得更快。清晰的边界创造了在边界内进行实验的信心。

HubSpot is actively developing an experimentation capability for the AI era. The company added “be bold, learn fast” to its company values; held 20 company-wide AI learning days in 2025; gave employees protected learning time; created channels for employees to share experiments; and shortened its organizational planning cycle from annual planning to six-week sprints. The company’s focus is not on succeeding every time but on reducing uncertainty as they go. AI usage metrics are strong, as are outcomes: Its talent acquisitions team embedded AI into the hiring funnel, resulting in a 10-day reduction in time to hire, and AI-powered marketing workflows led to an 82% improvement in email conversions.HubSpot 正在积极为 AI 时代开发实验能力。该公司将“大胆尝试，快速学习”加入了公司价值观；在 2025 年举办了 20 场全公司范围的 AI 学习日；为员工提供了受保护的学习时间；创建了供员工分享实验的渠道；并将组织规划周期从年度规划缩短为六周一期的冲刺。该公司的重点不在于每次都成功，而在于在过程中减少不确定性。其 AI 使用指标表现强劲，成果亦然：其人才招聘团队将 AI 嵌入了招聘漏斗，使招聘时间缩短了 10 天；AI 驱动的营销工作流程使电子邮件转化率提高了 82%。

When experimentation is expected and intelligent failure is treated as learning, organizations build the muscle to adapt.当实验成为预期，且明智的失败被视为学习时，组织就构建起了适应的肌肉。

From Decision Makers to Decision Systems从决策者到决策系统

Adaptable cultures have fewer decision bottlenecks. Rather than pushing difficult calls upward, they build decision systems that put better intelligence around the people closest to the decision. AI makes that increasingly possible—but distributing information is easier than distributing authority.具备适应力的文化拥有更少的决策瓶颈。他们不会将困难的决策推向上级，而是构建决策系统，为最接近决策的人员提供更好的情报。AI 使这一点越来越可行——但分发信息比分发权力更容易。

Moving faster without sacrificing quality requires cultures where people are trusted to exercise judgment, clear about the boundaries of their discretion, and accountable for the decisions they make.在不牺牲质量的前提下实现更快的速度，需要一种信任员工发挥判断力、明确其自主权边界并对其所做决策负责的文化。

An investment firm we worked with offers an early example. Its AI tools draw on more than a decade of committee materials—including investments made and those rejected—to challenge assumptions behind new deals. If a deal team projects a margin the firm has never seen achieved in a comparable business, for example, the system can flag it. But the AI does not get a vote. Investment decisions remain human.我们合作的一家投资公司提供了一个早期示例。其 AI 工具借鉴了十多年来的委员会资料——包括已完成和被拒绝的投资——来挑战新交易背后的假设。例如，如果一个交易团队预测的利润率是该公司在类似业务中从未实现过的，系统就会发出警示。但 AI 没有投票权。投资决策依然由人做出。

By making the firm’s collective experience available, it reduces dependence on any one senior investor while reinforcing constructive dissent, evidence-based challenge, and accountability for the ultimate call.通过利用公司集体经验，它减少了对任何单一资深投资者的依赖，同时强化了建设性的异议、基于证据的挑战以及对最终决策的问责制。

A global financial services firm we’ve advised applies the same principle. Its insights platform analyzes thousands of signals and pushes relevant intelligence to thousands of financial advisors, allowing judgment to sit closer to clients. The firm estimates it saves 1,200 hours of meeting preparation each week and generated more than 20 million AI-identified client opportunities in 2025, 50% more than the year before. But the human advisor still decides how to act.我们建议的一家全球金融服务公司应用了同样的原则。其洞察平台分析数千个信号，并将相关情报推送给数千名理财顾问，从而使判断力更贴近客户。该公司估计，每周可节省 1,200 小时的会议准备时间，并在 2025 年产生了超过 2,000 万个由 AI 识别的客户机会，比上一年增加了 50%。但最终如何行动，仍由人类顾问决定。

This combination of distributed intelligence and human accountability is the cultural foundation of a decision system: People have the information and authority to exercise judgment—and the responsibility to own the outcome.这种分布式情报与人类问责制的结合，是决策系统的文化基础：人们拥有行使判断力的信息和权力，并承担对结果负责的责任。

From Local Optimization to Enterprise Thinking从局部优化到企业思维

In most organizations, people are rewarded for optimizing what they control: their function, budget or process. But organizations are most adaptable when they connect work across those boundaries. The risk is that, as jobs feel threatened, people do the opposite: They protect information, defend expertise, and retreat into local priorities.在大多数组织中，员工因优化其所控制的内容（职能、预算或流程）而获得奖励。但当组织将跨边界的工作连接起来时，其适应力最强。风险在于，当工作感到受到威胁时，人们会反其道而行之：他们保护信息、捍卫专业知识，并退守到局部优先级中。

At one semiconductor manufacturer, resolving an issue required experts to search separately across six functional repositories, routinely consuming more than 40 hours. AI agents reduced that work to less than an hour while improving completeness—creating a shared enterprise view of the problem rather than six functional ones.在一家半导体制造商处，解决一个问题需要专家分别在六个职能存储库中搜索，通常耗时超过 40 小时。AI 代理将这项工作缩短至不到一小时，同时提高了完整性——从而形成了关于问题的共享企业视图，而非六个职能视图。

But technology alone does not create enterprise thinking. For a client struggling with silos across hundreds of group companies, we placed leaders on cross-regional, cross-functional teams and challenged them to solve shared business problems using resources and expertise from across the enterprise. The experience exposed people to capabilities and colleagues they previously did not know existed and helped build a “one company” mindset. Within just a year, perceptions of trust, transparency, inclusion, and collaboration inside the company rose by an average of three percentage points globally.但技术本身并不能创造企业思维。对于一家在数百家集团公司之间挣扎于“孤岛”问题的客户，我们将领导者安排在跨区域、跨职能的团队中，并要求他们利用整个企业的资源和专业知识来解决共同的业务问题。这一经历使人们接触到了他们以前不知道存在的职能和同事，并有助于建立“一家公司”的心态。仅在一年内，公司内部对信任、透明度、包容性和协作的认知在全球范围内平均提高了三个百分点。

AI can connect the knowledge; leaders have to connect the organization.AI 可以连接知识，但领导者必须连接组织。

From Accumulated Expertise to Learning Velocity从积累的专业知识到学习速度

AI is making information abundant. That does not mean expertise no longer matters. It means the half-life of expertise is shrinking, making the cultural ability to learn, unlearn, and relearn increasingly valuable.AI 正在使信息变得极其丰富。这并不意味着专业知识不再重要，而是意味着专业知识的半衰期正在缩短，这使得学习、去学习和再学习的文化能力变得日益宝贵。

The shift requires more than offering courses. Organizations can make learning part of the cultural operating system through regular reflection on what changed and what failed, rapid feedback cycles, short-term projects that stretch skills, mentoring in multiple directions, and talent systems that reward mobility rather than tenure alone.这种转变需要的不仅仅是提供课程。组织可以通过对变化和失败进行定期反思、快速反馈循环、扩展技能的短期项目、多向指导，以及奖励流动性而非仅仅奖励任期的激励制度，将学习纳入文化操作系统。

Mastercard has done exactly that through Unlocked, its AI-powered internal talent marketplace. Rather than defining people by their current title, the platform matches employees to projects, mentors, learning, and open roles based on skills they have and skills they want to build. By 2025, 93% of employees were registered, the platform had surpassed one million project hours and a third of employees who engaged with projects or mentoring subsequently made a career move. People acquire skills by doing real work, and the organization builds adaptability by redeploying capability as needs change.万事达卡（Mastercard）通过其 AI 驱动的内部人才市场 Unlocked 实现了这一点。该平台不再以员工当前的头衔来定义他们，而是根据员工拥有的技能和他们想要建立的技能，将员工与项目、导师、学习和开放岗位进行匹配。到 2025 年，93% 的员工完成了注册，该平台已超过 100 万个项目工时，三分之一参与项目或指导的员工随后实现了职业变动。人们通过实际工作获得技能，组织则通过在需求变化时重新部署能力来构建适应性。

Our research with the World’s Most Admired Companies points in the same direction. These companies identified learning as the cultural attribute most important to develop, and learning agility as the most important competency when hiring new leaders. In adaptable cultures, credibility belongs not simply to those with the most knowledge, but to those who can acquire and apply new knowledge the fastest.我们对“全球最受推崇公司”的研究也指向了同样的方向。这些公司将学习确定为最需要发展的文化属性，并将学习敏捷性确定为招聘新领导者时最重要的能力。在具备适应力的文化中，信誉不仅属于那些拥有最多知识的人，还属于那些能够最快获取和应用新知识的人。

. . .. . .

These five shifts are not another blueprint for the “right” culture. What organizations need from their cultures will continue to change as AI, strategy, and work evolve. The advantage lies in building the capacity to evolve with them—to recognize when existing ways of working no longer serve the organization and adapt before they become constraints.这五种转变并不是通往“正确”文化的又一份蓝图。随着 AI、战略和工作的演变，组织对文化的需求将持续变化。优势在于构建与它们共同进化的能力——即在现有的工作方式不再服务于组织时能够识别出来，并在它们成为制约因素之前进行调整。

In the age of AI, the advantage will not belong to organizations that declare themselves ready. It will belong to those that build the cultural muscles to keep getting ready: impact over activity, experimentation over certainty, decision systems over decision makers, enterprise thinking over local optimization, and learning velocity over expertise. Organizations that build these behaviors will not simply adopt AI faster. They will become better at changing when the next technology, market disruption, or inflection point arrives.在 AI 时代，优势将不属于那些宣称自己已经“就绪”的组织。它将属于那些构建了持续保持就绪状态的文化肌肉的组织：以影响力取代活动，以实验取代确定性，以决策系统取代决策者，以企业思维取代局部优化，以学习速度取代专业知识。构建这些行为的组织不仅会更快地采用 AI，当下一种技术、市场颠覆或转折点到来时，它们也将更善于做出改变。