内容来源：https://www.technologyreview.com/2026/04/07/1134966/enabling-agent-first-process-redesign/

内容总结：

德勤微软技术实践部门：企业需转向“智能体优先”流程重塑，以释放AI代理真正潜力

随着人工智能技术预算预计在未来两年增长超70%，由生成式AI驱动的智能体（AI Agents）正从概念走向核心生产力工具。然而，德勤微软技术实践部门的专家指出，企业若想获得超越传统自动化的突破性收益，必须进行根本性的运营模式变革——从“为旧流程打补丁”转向“以智能体优先”进行流程重塑。

该部门全球首席架构师兼美国首席技术官斯科特·罗杰斯强调，智能体不同于静态的、基于规则的系统，它们能够学习、适应并动态优化流程，自主执行完整工作流。但发挥其潜力的关键在于，企业需围绕智能体重新设计流程，而非将其强行嵌入碎片化的遗留系统中。

罗杰斯指出：“运营模式必须转变为‘人类担任治理者，智能体担任执行者’。” 在“智能体优先”的企业中，AI系统负责运营流程，而人类则专注于设定目标、定义政策约束和处理例外情况。

当前许多企业面临的挑战在于，其原有流程并非为自主系统构建，且往往未能全面理解业务的经济驱动因素（如单次服务成本），导致难以优先部署能创造最大价值的智能体，反而可能沉迷于华而不实的试点项目。

罗杰斯警告称，真正的风险并非AI技术无效，而是当企业仍在试点时，竞争对手可能已经完成了运营模式的重塑。“当企业建立起以智能体为核心、人类治理、自适应协同的工作流时，才能获得非线性的收益增长。”

这一转型意味着，常规重复性任务将日益自动化，员工得以聚焦于更高价值的创造性及战略性工作，从而提升运营效率、加强协作并加速决策，在保障企业安全的同时实现工作场所的现代化。

（本文内容由MIT Technology Review定制内容团队Insights制作，经人工研究、撰写与审核完成。）

中文翻译：

赞助内容
以智能体为核心重塑业务流程
人工智能体正逐步定义组织的运作与竞争方式。
与德勤微软技术实践联合呈现

与静态的、基于规则的系统不同，人工智能体能够动态学习、适应并优化流程。它们通过与数据、系统、人员及其他智能体实时交互，自主执行完整的工作流程。

然而，要释放其潜力，必须围绕智能体重新设计流程，而非仅将其生硬嵌入传统优化方法下的碎片化遗留工作流。企业必须转向“智能体优先”模式。

在智能体优先的企业中，人工智能系统负责执行流程，而人类则专注于设定目标、定义政策约束及处理异常情况。

德勤微软技术实践全球首席架构师兼美国首席技术官斯科特·罗杰斯指出：“运营模式需转变为人类担任管理者、智能体担任执行者。”

智能体优先的必然性
未来两年，企业在人工智能技术上的预算预计增长超70%。由生成式人工智能驱动的智能体将从根本上改变组织运作方式，实现超越传统自动化的成果。这些举措有望显著提升绩效，同时将人力转向更高价值的工作。

人工智能发展迅猛，静态的任务自动化方法可能仅带来渐进式改进。罗杰斯认为，由于遗留流程并非为自主系统设计，人工智能体需要机器可读的流程定义、明确的政策约束和结构化数据流。

更复杂的是，许多组织并未完全理解其业务的经济驱动因素，例如服务成本与单笔交易成本。因此，他们难以优先部署能创造最大价值的智能体，反而聚焦于华而不实的试点项目。为实现结构性变革，管理者需要转变思维。

企业必须比竞争对手更快地统筹成果。罗杰斯警告道：“真正的风险并非人工智能无效，而是当您仍在试点智能体与辅助系统时，竞争对手已重塑其运营模式。当企业建立以智能体为核心、人类监督与自适应协调的工作流时，非线性增长才会出现。”

常规重复性任务正日益自动化，使员工能专注于更高价值、更具创造性与战略性的工作。这一转变提升了运营效率，促进了更紧密的协作，加速了决策过程，助力组织在不牺牲企业安全的前提下实现工作场所现代化。

本文由《麻省理工科技评论》定制内容部门Insights制作，并非由《麻省理工科技评论》编辑团队撰写。内容由人类作者、编辑、分析师及插画师完成调研、设计与撰写，包括问卷编写与数据收集。若使用人工智能工具，仅限经过严格人工审核的辅助生产环节。

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英文来源：

Sponsored
Enabling agent-first process redesign
AI agents are beginning to define how organizations operate and compete.
In association withthe Deloitte Microsoft Technology Practice
Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, people, and other agents in real time, AI agents can execute entire workflows autonomously.
But unlocking their potential requires redesigning processes around agents rather than bolting them onto fragmented legacy workflows using traditional optimization methods. Companies must become agent first.
In an agent-first enterprise, AI systems operate processes while humans set goals, define policy constraints, and handle exceptions.
“You need to shift the operating model to humans as governors and agents as operators,” says Scott Rodgers, global chief architect and U.S. CTO of the Deloitte Microsoft Technology Practice.
The agent-first imperative
With technology budgets for AI expected to increase more than 70% over the next two years, AI agents, powered by generative AI, are poised to fundamentally transform organizations and achieve results beyond traditional automation. These initiatives have the potential to produce significant performance gains, while shifting humans toward higher value work.
AI is advancing so quickly that static approaches to task automation will likely only produce incremental gains. Because legacy processes aren’t built for autonomous systems, AI agents require machine-readable process definitions, explicit policy constraints, and structured data flows, according to Rodgers.
Further complicating matters, many organizations don’t understand the full economic drivers of their business, such as cost to serve and per-transaction costs. As a result, they have trouble prioritizing agents that can create the most value and instead focus on flashy pilots. To achieve structural change, executives should think differently.
Companies must instead orchestrate outcomes faster than competitors. “The real risk isn’t that AI won’t work—it’s that competitors will redesign their operating models while you’re still piloting agents and copilots,” says Rodgers. “Nonlinear gains come when companies create agent-centric workflows with human governance and adaptive orchestration.”
Routine and repetitive tasks are increasingly handled automatically, freeing employees to focus on higher value, creative, and strategic work. This shift improves operational efficiency, fosters stronger collaboration, and generates faster decision-making—helping organizations modernize the workplace without sacrificing enterprise security.
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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