Rethinking organizational design in the age of agentic AI

重新思考代理式 AI 时代的组织设计

Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution. Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows. 随着企业级 AI 代理的采用率迅速增长，雄心与执行力之间出现了脱节。尽管 85% 的组织表示希望在未来三年内实现“代理化”（agentic），但 76% 的组织表示其当前的运营和基础设施无法支持这种变革。他们指出，在人员、流程和工作流方面都缺乏准备。

The sticky tape problem

“胶带”问题

The challenge is that many organisations are often layering AI agents onto existing operations, rather than reimagine the operating model and how work will need to be rewired, explains Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting. “They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking.” 普华永道英国咨询公司劳动力咨询全球 CTO 兼首席 AI 官 Prasun Shah 解释说，挑战在于许多组织往往只是将 AI 代理叠加在现有运营之上，而不是重新构想运营模式以及如何重塑工作方式。“他们将 AI 员工嵌入到人类运营模式中，”在现有的工作场所结构上叠加 AI 代理，这“就像是在一个正在崩溃的运营模式上贴胶带。”

Doing so may be preventing organizations from unlocking the full value agentic AI offers, creating circumstances where disillusionment can quickly creep in. That full value lies in agents’ capacity to execute entire workflows with limited human input. They can coordinate complex tasks, make independent decisions, adjust to changing conditions, and iterate performance. In early proving grounds that span customer service, HR, and sales, it’s already estimated that AI agents could accelerate business processes by as much as 30% to 50% and low-value work time by 25% to 40% when deployed at scale. But with this capability comes greater complexity and the need for an enterprise-wide change. 这样做可能会阻碍组织充分释放代理式 AI 的价值，从而导致幻灭感迅速蔓延。其全部价值在于代理能够在极少人工干预的情况下执行整个工作流。它们可以协调复杂的任务、做出独立决策、适应不断变化的条件并迭代性能。在涵盖客户服务、人力资源和销售的早期试验场中，据估计，当大规模部署时，AI 代理可以将业务流程加速 30% 到 50%，并将低价值工作时间减少 25% 到 40%。但这种能力也带来了更大的复杂性，以及进行全企业范围变革的必要性。

Growing the AI vocabulary

扩展 AI 词汇表

Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research, in an attempt to plug what it sees as a gap in the existing lexicon about AI agents, and to provide enterprises with a new framework with which to think about their own adoption of the technology. 企业级代理式 AI 平台 Ema 将这种变革描述为“代理式业务转型”（Agentic Business Transformation，简称 ABT）。这是该平台去年与 HFS Research 合作创造的一个术语，旨在填补其认为在现有 AI 代理词汇表中的空白，并为企业提供一个思考自身技术采用的新框架。

“None of the existing vocabulary captures the full scope of the change,” explains Ema CEO and founder Surojit Chatterjee. “Digital transformation was about moving from paper to software. AI transformation was about adding artificial intelligence to existing processes. Co-pilot is about AI assisting in various human tasks. But ABT is something categorically different: It’s the integration of AI agents into the fabric of the organization.” “现有的词汇都无法涵盖变革的全部范围，”Ema 首席执行官兼创始人 Surojit Chatterjee 解释道。“数字化转型是从纸质转向软件；AI 转型是将人工智能添加到现有流程中；Copilot 是指 AI 辅助人类完成各种任务。但 ABT 在本质上是不同的：它是将 AI 代理整合到组织的结构中。”

For Shah, the dedicated term (ABT) “helps drive the need to redesign an organization in its entirety: its operating model, its workflows, decision rights, and performance management systems.” He emphasizes that “everything that’s needed to ensure those agents are actually active participants in value creation, rather than just point tools or productivity aids.” According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success. 对于 Shah 来说，这个专用术语（ABT）“有助于推动全面重新设计组织的需求：包括其运营模式、工作流、决策权和绩效管理系统。”他强调，“所有这些都是为了确保这些代理真正成为价值创造的积极参与者，而不仅仅是点状工具或生产力辅助工具。”据 Ema 称，ABT 包含三个核心支柱：组织的技术栈、劳动力以及用于衡量成功的指标。

AI agents as connective tissue

AI 代理作为连接组织

The first pillar of ABT is the technology stack. “Your existing tech stack was designed for human-operated, application-centric workflows,” says Chatterjee. “It needs to be reconsidered when the actor is an AI agent operating at machine speed across multiple systems simultaneously.” ABT 的第一个支柱是技术栈。“你现有的技术栈是为人类操作、以应用程序为中心的工作流而设计的，”Chatterjee 说。“当执行者变成以机器速度同时跨多个系统运行的 AI 代理时，就需要重新考虑这一点。”

As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. Shah 解释说，随着 AI 代理被整合到组织中，企业需要从一系列线性的流程和步骤，转向以一种截然不同的方式重塑工作。这是因为 AI 代理的价值不在于作为现有技术栈的另一层，而在于作为一种“连接组织”，在各层之间移动以协调高级任务，或从多个离散的应用程序中检索和解释数据。

AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.” To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. 他说，AI 代理可以通过基于这种情境化能力做出决策，为企业创造“真正的竞争差异化”。“这将是下一个战场。”为了构建这种连接组织，领导者需要调整其技术栈，以从 AI 代理中获得更高质量的决策，优先考虑同时访问多个数据集和应用程序，从而发展隐性知识。

“Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a new business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.” “进行这种架构转变的组织会变得真正更具适应性，”Chatterjee 说。“当出现新的业务需求时，你不需要等待软件供应商花费六个月来开发功能。你只需使用自然语言配置一名 AI 员工，并将其连接到所需的系统。从业务需求到生产工作流的时间将从几个月缩短到几天。”

The workforce, redesigned

重新设计的劳动力

As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT. Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. 随着 AI 代理被部署到更多的用例中，企业领导者必须考虑这对劳动力动态意味着什么，这是 ABT 的第二个支柱。当今的劳动力结构与工业化早期的等级模式几乎没有区别。为了最大限度地提高效率和规模，流程被标准化，战略业务单元（SBU）之间的任务被明确划分，员工根据其优化下属团队产出的能力在组织中晋升。

But with AI agents that can execute, coordinate, and optimize tasks—often without managerial coordination—the lines of that established hierarchy become blurred. In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on new responsibilities associated with managing hybrid teams. Managers “will need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics” to navigate new tensions that could arise in a hybrid workforce, says Shah. 但是，随着 AI 代理能够执行、协调和优化任务（通常无需管理协调），既定等级制度的界限变得模糊。在混合了 AI 代理和人类员工的劳动力中，管理者将从许多基于执行的任务中解放出来，但会承担与管理混合团队相关的新职责。Shah 说，管理者“需要能够处理围绕信任、可解释性、心理安全甚至地位动态的问题”，以应对混合劳动力中可能出现的新紧张关系。

The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration. 代理式 AI 对现有劳动力结构的影响也远不止管理层。麦肯锡预测，到 2030 年，四分之三的现有工作岗位将需要重新设计、技能提升或重新部署，组织需要迅速采取行动，修改招聘、留任和薪酬制度。

From output to outcome

从产出到成果

Success metrics are the third and final pillar of ABT. As AI agents assume greater… 成功指标是 ABT 的第三个也是最后一个支柱。随着 AI 代理承担更大的……