重新思考智体AI时代的组织设计
内容总结：
AI代理时代组织设计需系统性重塑：企业面临战略与执行脱节困境
随着企业级AI代理应用加速普及，一项严峻挑战浮出水面：多数企业的组织架构与运营模式尚未为技术变革做好准备。调查显示，尽管85%的企业希望在三年内实现"代理化"，但76%坦言现有运营和基础设施无法支撑这一转型，人员、流程及工作流均存在准备不足问题。
"贴胶带"式整合成效率陷阱
普华永道英国咨询部门首席AI官普拉森·沙阿指出，许多企业将AI代理简单叠加至现有运营中，如同"在断裂的运营模型上贴胶带"，而非重构工作模式。这种"将AI员工嵌入人类运营模型"的做法，导致企业难以释放AI代理的核心价值——独立执行完整工作流、协调复杂任务、自主决策并持续优化。初步验证显示，在客服、人力资源、销售等领域规模化部署AI代理，可使业务流程提速30%-50%，低价值工作时间缩减25%-40%，但这也要求企业进行系统性变革。
"代理式业务转型"概念应运而生
企业AI平台Ema联合HFS Research提出"代理式业务转型"（ABT）框架，填补现有技术术语空白。Ema首席执行官苏罗吉特·查特吉强调，ABT绝非数字化或AI转型的延续，而是将AI代理融入组织肌理的本质变革。该框架聚焦三大支柱：技术栈重构、劳动力重塑及成功指标重定义。
技术栈：从线性流程到"连接组织"
在技术层面，AI代理不应是现有系统的附加层，而应成为贯穿各层级的"连接组织"，跨系统协调任务、检索解读数据。企业需改造技术架构，使AI代理能同时访问多数据集与应用，形成隐性知识。实现架构转型后，新业务需求从提出到部署的周期将从数月缩短至数天。
劳动力：管理角色从执行转向协调
AI代理的介入正在模糊传统科层制边界。管理者将从执行任务中解放，转向管理混合团队中的信任、解释权、心理安全及地位动态等新议题。麦肯锡预测，到2030年，75%现有岗位需重新设计、技能升级或重新配置，企业须快速调整招聘、留任及薪酬体系。
指标革命：从关注"产出"转向关注"成果"
传统以活动量（如处理电话数、提交报告数）为核心的考核体系将失效。查特吉举例，当AI代理处理客户交互量达人类百倍时，单纯考核交互量会导致虚假繁荣。Ema某大客户将指标从"单次查询成本"转向"无需人工介入的合同审查率"后，AI代理投资回报率两季度内增长三倍。专家指出，新指标体系需配套重构激励机制、问责制及人才管理流程，尤其在人机协作团队中，伦理与信托责任仍属人类，但操作责任将明显分散。
渐进式变革：夯实系统性基础
专家建议，领导者应围绕ABT三大支柱启动内部对话，为企业从系统层面拥抱AI代理奠定基础，弥合战略雄心与执行能力之间的鸿沟。当前，这些复杂议题仍待业界持续探索，但系统性变革的搭建已刻不容缓。
中文翻译：
广告特辑
智能体AI时代，组织架构需要重新设计
要让智能体AI为组织带来实质性收益，就不能简单将其叠加到现有运营体系之上。企业领导者必须将其视为一次系统层面的变革。
与Ema联合呈现
随着企业级AI智能体的采用率迅速增长，在雄心与执行力之间出现了脱节现象。
尽管85%的组织表示希望在未来三年内采用智能体AI，但76%的组织认为其当前的运营模式和基础设施无法支撑这一变革。他们指出，人员、流程和工作流方面均存在准备不足的问题。
"胶带"困局
普华永道英国咨询公司劳动力咨询全球首席技术官兼首席AI官Prasun Shah解释说，挑战在于许多组织往往将AI智能体叠加到现有运营体系上，而不是重新构想运营模式及工作流程的重构方式。"他们正在将AI员工嵌入到人类运营模式中"，在现有职场结构上叠加AI智能体，而"这就像给一个正在断裂的运营模式贴上胶带"。
这样做可能会阻碍组织释放智能体AI的全部价值，从而让失望情绪迅速蔓延。智能体AI的真正价值在于，它们能够在很少的人工干预下执行完整的工作流程。它们可以协调复杂任务、做出独立决策、适应不断变化的条件并迭代优化性能。
在客户服务、人力资源和销售等早期试点领域，据估计，当AI智能体大规模部署时，可以将业务流程加速30%至50%，并将低价值工作耗时减少25%至40%。但伴随这种能力而来的是更大的复杂性，以及企业级变革的需求。
扩充AI词汇表
企业级智能体AI平台Ema将这种变革称为"智能体业务转型"(ABT)，这是该公司去年与HFS Research合作创造的一个术语，旨在填补其所认为的现有AI智能体词汇表中的空白，并为企业提供一个思考如何采用该技术的新框架。
Ema首席执行官兼创始人Surojit Chatterjee解释道："现有的词汇都无法完全捕捉到这种变革的全貌。数字化转型是从纸质转向软件。AI转型是为现有流程增加人工智能。Copilot是AI协助完成各种人类任务。但ABT是截然不同的：它是将AI智能体整合到组织的架构之中。"
对于Shah而言，"ABT"这个专用术语"有助于推动全面重新设计组织的需求：包括其运营模式、工作流程、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智能体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手段"。
据Ema介绍，ABT包含三个核心支柱：组织的技术栈、劳动力队伍以及衡量成功的指标。
AI智能体作为"连接组织"
ABT的第一个支柱是技术栈。Chatterjee说："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程设计的。当行为主体是能够以机器速度跨多个系统同时运行的AI智能体时，就需要重新考虑。"
Shah解释说，随着AI智能体被整合到组织中，企业需要从一系列线性流程和步骤转向以一种截然不同的方式重构工作。这是因为AI智能体的价值并非作为现有技术栈的另一个层级，而是作为一种"连接组织"，在不同层级之间移动或穿梭，以协调高层级任务，或从多个独立应用中检索和解读数据。他说，AI智能体可以基于这种情境化能力做出决策，从而为企业"创造真正的竞争优势"。"那将是下一个战场所在。"
为了构建这种"连接组织"，领导者需要调整其技术栈，以从AI智能体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，以发展隐性知识。Chatterjee说："做出这种架构转变的组织会变得真正更具适应性。当出现新的业务需求时，你不需要等待六个月让软件供应商来开发一个功能。你可以用自然语言配置一个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程的时间将从数月缩短到数天。"
劳动力队伍，重新设计
随着AI智能体被部署到更多应用场景中，企业领导者必须考虑这对整个劳动力队伍动态意味着什么——这是ABT的第二个支柱。
当前的劳动力结构与工业化初期的层级模式几乎没有差异。为了最大限度地提高效率和规模，流程被标准化，任务在战略业务单元之间被清晰划分，员工根据其优化下级团队产出的能力在组织内逐级晋升。但是，随着AI智能体能够执行、协调和优化任务——通常无需管理层协调——这种既定层级结构的界限变得模糊。
在一个融合了AI智能体和人类员工的劳动力队伍中，管理者将从许多执行性任务中解放出来，但将承担与管理混合团队相关的新职责。Shah说，管理者"将需要能够处理围绕信任、可解释性、心理安全感甚至地位动态等问题"，以应对混合劳动力中可能出现的新的紧张关系。
智能体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，目前四分之三的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来调整招聘、留任和薪酬体系。
从产出到成果
衡量成功的指标是ABT的第三个也是最后一个支柱。
随着AI智能体承担起更多核心企业流程的所有权，与人类员工一起扮演协作角色，传统上关注活动或产出（如处理的通话量或提交的报告量）的劳动力指标已不再适用。
Chatterjee说："当你在劳动力队伍中加入AI员工时，活动指标就变得毫无意义，甚至具有误导性。一个AI员工处理一千次客户互动的时间，人类只能处理十次。如果你以处理的互动次数来衡量成功，你会得出AI工作出色的结论，却忽略了这些互动中是否有任何一次真正驱动了客户满意度、留存率或收入。"为了纠正这一点，企业必须开发一套新的指标，专注于成果而非产出。也就是说，要衡量所取得的更广泛的收益或变化，而不是单个可交付成果。
例如，当Ema的一个大型企业客户彻底改革其指标体系，从每次查询成本和AI准确率等工具指标，转向无需人工介入即可审查的合同百分比等成果指标时，智能体AI带来的投资回报率在两个月内增长了两倍。Chatterjee说，这些变化意味着"这家客户不再在高量、低复杂性的工作流程中构建点状解决方案，而是开始在成果价值最高的地方部署AI员工"。
Shah指出，整合新指标可能还需要彻底重新配置奖励和人才管理流程，以及组织内的问责制和所有权。例如，在人机协作团队中，尽管道德和受托责任可能仍由人类员工承担，但运营问责制将变得更加分散，以反映AI智能体的系统性角色。
Shah补充说，这种变化将引发高级领导团队必须应对的新问题。他们需要考虑：当AI员工犯错时，谁来负责？当AI和人类意见不一时会发生什么？应该建立怎样的防护栏来保护客户？
为系统性变革奠定基础
系统性变革是渐进式的。这些都是专家们仍在努力应对的复杂探究课题。但是，通过启动关于ABT核心支柱——劳动力队伍、技术栈以及衡量成功的指标——的内部对话，领导者可以为组织奠定基础，使其更好地在系统层面拥抱AI智能体，并开始弥合雄心与执行力之间的鸿沟。
本文由MIT Technology Review定制内容部门Insights制作。并非由MIT Technology Review编辑团队撰写。由人类作家、编辑、分析师和插画师完成研究、设计和撰稿。这包括调查问卷的撰写和调查数据的收集。可能使用过的AI工具仅限于经过严格人工审核的辅助制作流程。
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Rethinking organizational design in the age of agentic AI
For agentic AI to deliver material benefits to organizations, it can’t be layered onto existing operations. Instead, enterprise leaders must approach it as a systems-level change.
In partnership withEma
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.
The sticky tape problem
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.”
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.
Growing the AI vocabulary
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.
“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.”
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.
AI agents as connective tissue
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.”
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. 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. “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.”
The workforce, redesigned
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. 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.
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.
From output to outcome
Success metrics are the third and final pillar of ABT.
As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output—such as calls handled or reports filed—no longer make sense.
“When you add AI employees into the workforce, activity metrics become meaningless or actively misleading,” says Chatterjee. “An AI employee can handle a thousand customer interactions in the time it takes a human to handle ten. If you measure success by interactions handled, you'll conclude the AI is working brilliantly while missing whether any of those interactions actually drove customer satisfaction, retention, or revenue.” To correct this, enterprises must develop a new set of metrics that focus on outcome rather than output. That is, metrics on the broader benefits or changes achieved, rather than individual deliverables.
For example, when one of Ema’s large enterprise customers overhauled its own metrics, switching from tool metrics like cost per query and AI accuracy, to outcomes like the percentage of contracts reviewed without human escalation, the measured ROI from agentic AI tripled within two quarters. The changes meant “this customer stopped building point solutions in high-volume, low-complexity workflows and started deploying AI employees where the outcome value was highest,” says Chatterjee.
Integrating new metrics may also require a complete reconfiguration of reward and talent management processes, as well as accountability and ownership within organizations, points out Shah. In human-AI teams, for example, although ethical and fiduciary responsibilities will likely remain with human employees, operational accountability will become significantly more diffused to reflect the systemic role of AI agents.
This change will raise new questions that senior leadership teams will need to wrestle with, Shah adds. They’ll need to consider: Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? What guardrails should be erected to safeguard customers?
Laying the groundwork for systems-level change
Systems-level change is gradual. These are complex lines of inquiry that experts continue to grapple with. But in kickstarting internal dialogue about the core pillars of ABT—the workforce, the technology stack, and the metrics by which success can be gauged—leaders can lay the groundwork for an enterprise better poised to embrace AI agents at a systems level and start to close the gap between their ambition and execution.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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