麦肯锡：什么是 FDE（Forward-deployed engineers）前沿部署工程师

前线部署工程师：AI 时代的“驻场”破局者

Forward-deployed engineers work inside another company’s own systems. Three McKinsey leaders explain how FDEs help companies derive impact from AI.
前线部署工程师（Forward-deployed engineers, 简称 FDE）深入客户公司的内部系统开展工作。三位麦肯锡高管为您解析 FDE 如何助力企业从 AI 中获取实际业务价值。

Interest in forward-deployed engineers, or FDEs, has surged in recent months as companies try to get real value from AI. The term may be a buzzword for a service that consulting firms and technology providers have offered for years: embedding engineers at a client’s company, inside its systems and data, to build software alongside the people who will use it.
近几个月来，随着企业努力从 AI 中获取真正的价值，人们对前线部署工程师（FDE）的兴趣激增。这个词可能是咨询公司和科技提供商多年来一直提供的一项服务的“流行语”：将工程师派驻到客户公司，深入其系统和数据内部，与最终用户并肩开发软件。

As the AI era gets underway, companies are finding that off-the-shelf AI tools often don’t fit their specific needs. Getting real value from them means adapting tools to a specific business problem and helping teams change how they work. An FDE does both, which is why this form of collaboration seems particularly well suited for business today.
随着 AI 时代的开启，企业发现现成的 AI 工具往往无法满足其特定需求。要从中获得真正的价值，意味着必须使工具适应特定的业务问题，并帮助团队改变工作方式。FDE 恰好能同时做到这两点，这就是为什么这种协作形式似乎特别适合当今的商业环境。

In this video Explainer, McKinsey’s Holger Hürtgen, Martin Harrysson, and Nick Robson set out what an FDE does and how AI has changed the job. The three agree that FDEs can help companies derive value from AI by tailoring it to a company’s specific problems; their views on the separation between an FDE and a traditional consultant suggest the role is still evolving.
在这期视频解说中，麦肯锡的 Holger Hürtgen、Martin Harrysson 和 Nick Robson 阐述了 FDE 的工作内容以及 AI 如何改变了这一职业。三人都认同，FDE 可以通过针对公司的特定问题定制 AI，帮助企业从 AI 中创造价值；而他们对 FDE 与传统顾问之间界限的看法，表明这一角色仍在不断演进。

(These interviews have been edited for length and clarity. / 以下访谈内容已根据篇幅和清晰度进行了编辑。)

什么是前线部署工程师？

What is a forward-deployed engineer?

Holger Hürtgen: First and foremost, it’s an engineer, so you basically do data science, data engineering, software engineering. You work with data, you work with technology. The forward-deployed part is the interesting part to me personally, meaning you work directly on client systems. You don’t work in a lab. You work with the real problem.
Holger Hürtgen： 首先，这是一名工程师，所以你的基本工作是数据科学、数据工程和软件工程。你与数据打交道，与技术打交道。就我个人而言，“前线部署”这部分是最有趣的，这意味着你直接在客户的系统上工作。你不是在实验室里闭门造车，而是直面真实的业务问题。

Martin Harrysson: It’s someone at a company whose main task is to make the technology work, solve a problem, and reach the outcome we’re trying to achieve. So, as opposed to a standard engineer who might be working on a product and to make the technology work, a forward-deployed engineer is not primarily about making the solution just run; it’s about reaching the right people to do the job, and in a different way, to reach some kind of outcome. I think the forward-deployed part probably comes from its military meaning. But it’s more that they’re embedded in the company.
Martin Harrysson： 他们是派驻到企业的人，主要任务是让技术落地、解决问题并达成我们预期的成果。因此，与可能专注于研发产品、让技术跑通的标准工程师不同，前线部署工程师的首要目的不仅仅是让解决方案运行起来；而是要接触到对的人，以不同的方式完成工作，从而达成某种业务成果。我认为“前线部署”这个词可能源于其军事含义。但更多是指他们深度嵌入到了客户公司中。

Nick Robson: To me, a forward-deployed engineer is someone who is able to not just apply software fit to a business problem or fit into a company’s current business process. Rather, it’s someone who can work hand in hand with clients to figure out how to change their business process, along with software, technology, and tools, to create a whole new way of working. It’s a unique skill set that requires a ton of creativity and a pretty generalist tool kit.
Nick Robson： 对我来说，前线部署工程师不仅是将软件应用于业务问题或适应公司现有业务流程的人。相反，他们能够与客户携手，弄清楚如何通过结合软件、技术和工具来改变业务流程，从而创造一种全新的工作方式。这是一种独特的技能组合，需要极强的创造力和相当全面的工具箱（综合能力）。

FDE 与传统顾问有何不同？

How is an FDE different from a consultant?

Martin Harrysson: The traditional meaning of a consultant was probably more incentivized around a recommendation, say, or coming up with a plan for action. For instance, here’s a recommendation; this is the direction we’re going to go. Whereas a forward-deployed engineer is much more about implementing some technology that changes how you do work and has a more direct impact on a particular workflow.
Martin Harrysson： 传统意义上的顾问，其激励机制可能更多是围绕提出建议或制定行动计划。比如，“这是我们的建议，这是我们要走的方向”。而前线部署工程师则更多地关注实施某些能改变工作方式的技术，并对特定的工作流程产生更直接的影响。

Holger Hürtgen: A difference between a forward-deployed engineer and a consultant is that as an engineer, you have to work in the system of a client and develop a product, so you leave behind the product. A difference between an FDE and a software developer is that a developer has predefined the product they will build. A forward-deployed engineer, like a consultant, needs to understand the problem and discuss with clients what the solution should look like.
Holger Hürtgen： FDE 与顾问的一个区别在于，作为工程师，你必须在客户的系统中工作并开发产品，所以你会留下一个实际的产品。FDE 与软件开发者的区别在于，开发者构建的产品是预先定义好的。而前线部署工程师就像顾问一样，需要理解问题，并与客户讨论解决方案应该是什么样的。

Nick Robson: The roles are merging a lot more nowadays, with the forward-deployed engineer and consultant roles kind of becoming one. You can’t just go in, deliver insights, and walk away. You have to work with clients, dissect a business problem, figure out who the right people are to work with, drive the technology, and rewire processes. I don’t think about it as two roles. I think a really good consultant makes a really good forward-deployed engineer.
Nick Robson： 如今，这两个角色正在加速融合，前线部署工程师和顾问的角色某种程度上正合二为一。你不能只是进去提供些洞察，然后拍屁股走人。你必须与客户合作，剖析业务问题，找出合适的合作对象，推动技术落地，并重塑流程。我不认为这是两个截然不同的角色。我认为一个非常优秀的顾问，也能成为一名非常出色的前线部署工程师。

为什么我们最近经常听到 FDE？

Why are we hearing more about FDEs right now?

Holger Hürtgen: I’ve been doing this job for 20 years, and I would admit that the FDE terminology itself is maybe a bit of a buzzword. The concept, however, is more relevant than ever because the new LLMs [large language models] themselves are not sufficient. It’s about tailoring them to the problem at hand and making sure you get the business impact out of it. Otherwise, you’re just implementing AI for the sake of AI. You will see token spend going up, but you won’t see any bottom-line impact. This is why FDEs really solving the problem is so important.
Holger Hürtgen： 我做这行已经 20 年了，我承认“FDE”这个术语本身可能有点像个流行热词（buzzword）。然而，这个概念现在比以往任何时候都更具现实意义，因为新的大语言模型（LLM）本身是不够的。关键在于根据手头的问题对其进行定制，并确保你能从中获得业务影响。否则，你只是为了 AI 而做 AI。你会看到 token 消耗在增加，但看不到任何对底线（利润）的实际影响。这就是为什么 FDE 真正去解决问题如此重要。

Nick Robson: I think people are talking about it more than ever, and the demand for [McKinsey’s AI arm] QuantumBlack is higher than ever. It is the longest wait list I’ve ever seen for that kind of talent, and part of it is because clients are really seeing its value. We’re able to truly meet clients where they are. And the benefits of adoption and the client experience are astronomical. Three years ago, everybody was just installing enterprise-wide AI platforms. But they then realized they weren’t getting value from them, and people weren’t using them. So today, my clients realize that if we can rewire an entire domain end to end and fully reimagine a business process, that will drive adoption.
Nick Robson： 我认为人们比以往任何时候都在更多地谈论它，而且对 [麦肯锡旗下 AI 团队] QuantumBlack 的需求也达到了前所未有的高度。这是我见过的针对此类人才最长的等待名单，部分原因是客户真正看到了它的价值。我们能够真正做到“在客户所处的位置与他们接轨”。而且，其在应用落地和客户体验方面带来的好处是惊人的。三年前，每个人都只是在安装企业级的 AI 平台。但随后他们意识到并没有从中获得价值，人们也不去使用它们。所以今天，我的客户们意识到，如果我们能端到端地重塑整个领域，并彻底重构业务流程，那将极大地推动 AI 的采用。

Martin Harrysson: The core problem of trying to get technology to do something useful, productive, and good for a company or for one of our clients is not new. What is new is that AI has amplified a lot of these problems. We have more technology tools to solve more problems, but they don’t come ready and packaged. Clients will roll out a new AI tool and hope that the impact will just happen, and that’s just not the case. This is where the leap from individual impact to team impact to organization-wide impact is not automatically happening because you need to collaborate. Just because I can do one part of the problem faster doesn’t mean we have figured out how to run meetings more effectively or how to communicate faster.
Martin Harrysson： 试图让技术为企业或我们的客户做些有用、高效且有益的事情，这个核心问题并不新鲜。新鲜的是，AI 放大了其中的许多问题。我们有了更多的技术工具来解决更多的问题，但它们并不是现成打包好的。客户会推出一个新的 AI 工具，并期望影响会自然发生，但事实并非如此。这就是为什么从个人影响跨越到团队影响，再到全组织影响，并不会自动发生，因为你需要协作。仅仅因为我处理问题的某一部分变快了，并不意味着我们已经弄清楚如何更有效地开会或如何更快地沟通。

AI 如何改变了 FDE 的工作？

How has AI changed the job of an FDE?

Holger Hürtgen: AI is changing the job on two fronts. One is the way you develop software, by using agentic AI and agentic engineering to code. It’s less coding than I did 20 years ago; AI does it for you. You need to think more about the specifications, or what you want AI to do. The other part is that the end product itself is becoming AI. So you are building agentic AI systems and not just machine learning systems like we did a couple of years ago.
Holger Hürtgen： AI 正在从两个方面改变这项工作。一是开发软件的方式，通过使用智能体化 AI（agentic AI）和智能体工程来编写代码。比起 20 年前，现在自己手写的代码少了；AI 会替你完成。你需要更多地思考规格说明，或者你希望 AI 做什么。另一方面是最终产品本身正在变成 AI。所以你现在构建的是智能体化 AI 系统，而不仅仅是几年前我们做的那种机器学习系统。

Nick Robson: Before, we would staff a team of many computer engineers or data scientists, but with the technology we have today, with AI, analytics, and software engineering, that’s all really becoming democratized. Today, our business analysts are almost acting like managers for AI. The manager that we had two years ago is now acting like a director and working across a team of people running their own teams.
Nick Robson： 以前，我们会配备一个由许多计算机工程师或数据科学家组成的团队，但凭借我们今天拥有的技术，加上 AI、分析和软件工程，这一切真的正在走向平民化（普及化）。今天，我们的业务分析师几乎就像是 AI 的管理者。我们两年前的经理，现在正扮演着总监的角色，并跨越管理那些带领自己团队的人员。

Martin Harrysson: AI has changed what deploying technology looks like, to some extent. Previously, it was about deploying more prepackaged software at a client and maybe customizing it a little bit, or configuring it in certain ways. AI is a little different because, in some ways, it is a general-purpose tool. But you have to put a few things together. You usually need a model, but you also need a data layer and some understanding of the organization. You need to build in a harness to make sure it behaves in the way you expect. And you usually need to change the workflows and have people do something different to get the full value out of it.
Martin Harrysson： 在某种程度上，AI 改变了技术部署的面貌。以前，部署技术更多是在客户那里部署预打包的软件，可能稍微进行一点定制，或者以特定方式进行配置。AI 有点不同，因为在某些方面，它是一个通用工具。但你必须把几样东西拼凑在一起。你通常需要一个模型，但你还需要一个数据层以及对组织的一些了解。你需要构建一个“安全护栏（harness）”来确保它的行为符合你的预期。而且，你通常需要改变工作流程，让人们做些不同的事情，才能充分发挥它的价值。

FDE 在驻场的前几周会做什么？

What does an FDE do in the first few weeks of a job?

Martin Harrysson: A typical first week or initial phase of a forward-deployed engineer’s work usually involves sitting next to people performing a task. Say we have a team working on supply chain planning. They get a bunch of data, already have a system in place, and are making decisions about whether to reroute ships or trucks to make sure the right things show up in their warehouse. What a forward-deployed engineer does is insert themselves in that team, try to understand who makes decisions, what makes a difference, and what data matters. Just really immerse themselves in understanding how this works, because a lot of those things may not always be documented very well.
Martin Harrysson： 前线部署工程师典型的第一周或初始阶段的工作，通常是坐在执行任务的人旁边。假设我们有一个负责供应链规划的团队。他们获取大量数据，已经有了现成的系统，并且正在决定是否重新规划船只或卡车的路线，以确保正确的物资出现在仓库中。前线部署工程师要做的就是融入这个团队，试图了解谁在做决策、什么因素能产生实质性影响、哪些数据最重要。就是要真正沉浸其中去理解这一切是如何运作的，因为很多事情往往并没有被很好地记录在案。

Holger Hürtgen: It’s difficult to describe a typical FDE job, because it really depends on the type of project. What’s always the same is that it starts with defining a problem—what do we want to solve—and finding the data. This means identifying the core systems in client environments where data sits and then determining how to take it out of those systems and put it into, for example, cloud infrastructure. That’s more the data engineering part. At some point, you then also add software engineering components or front-end engineering components, because you eventually want to build a real tool. Then there are iterations, because you continuously have to demo what you build to the client, get feedback, and possibly start over from scratch.
Holger Hürtgen： 很难描述一个典型的 FDE 工作，因为它真的取决于项目的类型。始终不变的是，它始于定义问题——我们想解决什么——以及寻找数据。这意味着要识别客户环境中存放数据的核心系统，然后确定如何将这些数据提取出来，并放入（例如）云基础设施中。这更多是数据工程的部分。在某个阶段，你还会加入软件工程组件或前端工程组件，因为你最终想要构建一个真实的工具。然后是迭代，因为你必须不断向客户演示你构建的内容，获取反馈，并有可能从头再来。

Nick Robson: The job is more focused on the business problem. How do we scope this? How do we know what will drive business impact? FDEs spend much of their early work time scoping problems, dissecting the actual business itself, spending time with folks, figuring out where the value is, et cetera.
Nick Robson： 这项工作更侧重于业务问题。我们如何界定范围？我们如何知道什么能驱动业务影响？FDE 在早期的工作中，会把大量时间花在界定问题、剖析实际业务本身、与员工相处、找出价值所在等方面。

FDE 解决什么问题？

What problems do FDEs solve?

Nick Robson: One of my clients is an industrials player that makes complex products with a specialized supply chain. Due to geopolitical circumstances, they are experiencing a demand ramp-up for their products that is many times what they’ve delivered before. They needed something made exactly for their supply chains that could drive visibility into whether they have the right parts at the right time, provide the sequence in which to action things, drive accountability, and perform complex analytics. We were challenged on Tuesday to create this capability, and we got it working on Thursday. This tool that we built in basically a day and a half is something that in the past has taken hundreds of people many years to build.
Nick Robson： 我的一个客户是一家工业企业，生产复杂产品，拥有专业的供应链。由于地缘政治环境的影响，他们正经历着对其产品需求的激增，这是他们以往交付量的数倍。他们需要一套专门为其供应链量身定制的系统，该系统能够提高可见性（了解是否在正确的时间拥有正确的零部件），提供执行任务的优先级顺序，推动责任落实，并执行复杂的分析。我们在周二接到了创建这一能力的挑战，并在周四就让它运转了起来。我们基本上在一天半内构建的这个工具，在过去需要数百人花费数年时间才能建成。

Holger Hürtgen: FDEs have to be able to make judgment calls. Consultants may identify a problem and define what the best solution should look like. But as an FDE, you should decide what the best methodology is. What’s fit for purpose? Is gen AI the best tool? Is agentic AI? Or is it machine learning? Or is it a very simple regression? It doesn’t matter how advanced the tool is if it has an impact.
Holger Hürtgen： FDE 必须能够做出判断和决策。顾问可能会识别问题并定义最佳解决方案应该是什么样子。但作为 FDE，你应该决定什么是最好的方法论。什么最适合当前目的？生成式 AI 是最好的工具吗？智能体化 AI 呢？还是机器学习？或者是一个非常简单的回归分析？只要能产生业务影响，工具多么先进并不重要。

The focus now is on agentic AI systems. For instance, we all know how annoying it can be to interact with a call center. We basically“agentified”the call center of a utility, and it was quite interesting solving that problem to make sure that the agents would always have the proper answer, that they wouldn't hallucinate, and that customers would really like it. It was a real call center that you could call, and agents would support your journey through that call center.
现在的重点是智能体化 AI 系统。例如，我们都知道与呼叫中心打交道有时多么令人烦恼。我们基本上将一家公用事业公司的呼叫中心“智能体化（agentified）”了。解决这个问题非常有趣，我们要确保智能体总能给出正确的答案，不会产生幻觉，并且客户真的会喜欢它。这是一个真实的呼叫中心，你可以打电话进去，而智能体会支持你完成整个呼叫流程。

为什么改变人们的工作方式如此困难？

Why is changing how people work so difficult?

Martin Harrysson: Often, ironically, one can say that the more difficult part is the human aspect of it. People are not always open to change or to new things. This is sometimes even trickier than getting the actual technology to work.
Martin Harrysson： 具有讽刺意味的是，通常可以说，更困难的部分在于“人”的方面。人们并不总是对变化或新事物持开放态度。这有时甚至比让实际技术运转起来还要棘手。

I think an interesting example of this came from a client recently, as we’re transforming how they develop software and use more agents. One of the most experienced software developers at this client went through agentic training and then went back to his job. He started using agents, but they didn’t work. Because he was a really experienced and qualified engineer, he put the agentic part aside. He fired up the IDE [integrated development environment] and fixed the code. But the agentic part of it went sideways. The next time, he might not even try to do the work using AI and agents. Sometimes, if you have people who are really good at what they do, it almost becomes harder to make that change.
我认为最近一个客户提供了一个有趣的例子，当时我们正在改变他们开发软件和使用更多智能体的方式。该客户最有经验的软件开发人员之一接受了智能体化培训，然后回到了工作岗位。他开始使用智能体，但没起作用。因为他是一位经验非常丰富且合格的工程师，他把智能体部分放在了一边。他启动了 IDE（集成开发环境）并自己修复了代码。结果智能体化转型就偏离了轨道。下一次，他甚至可能都不会尝试使用 AI 和智能体来工作了。有时候，如果你拥有在本职工作上非常优秀的人才，做出这种改变反而会变得更难。

Holger Hürtgen: You have to solve the problem with real data in real systems and make sure people who are supposed to use it like the solution and do use it. This is the only way to get the business impact.
Holger Hürtgen： 你必须使用真实系统中的真实数据来解决问题，并确保那些本该使用该解决方案的人喜欢它并真正去使用它。这是获得业务影响的唯一途径。

Nick Robson: We had a client who was against agentic engineering or agentic coding. He explicitly told us, “I don’t really trust this stuff. I don’t like AI.”We spent an hour and a half teaching him some of the fundamentals of AI and how to apply it to a specific problem that he needed to solve in the next 24 hours. The next week, he came in and said, “I’ve used all my tokens.”He asked us to teach his entire team how to use AI and become their own forward-deployed engineers.
Nick Robson： 我们曾有一位客户反对智能体工程或智能体编码。他明确地告诉我们：“我真的不信任这些东西。我不喜欢 AI。”我们花了一个半小时教他一些 AI 的基础知识，以及如何将其应用于他需要在未来 24 小时内解决的一个具体问题。第二周，他进来说：“我的 token 额度都用光了。”他要求我们教他的整个团队如何使用 AI，并让他们成为自己的前线部署工程师。

什么样的人适合这份工作？

What kind of person is good at this job?

Holger Hürtgen: If you are, like me, an extrovert nerd, if you like teaching people, if you like not only the machine room but also talking to human beings, I think that’s the perfect profile for an FDE. It’s the perfect combination of coding in the machine room, explaining to people what you did, collecting feedback, and doing it again. That’s the beauty of the job.
Holger Hürtgen： 如果你像我一样，是一个“外向的极客（extrovert nerd）”，如果你喜欢教人东西，如果你不仅喜欢待在机房，还喜欢和人类交谈，我认为那就是 FDE 的完美画像。这是在机房写代码、向人们解释你做了什么、收集反馈然后再做一次的完美结合。这就是这份工作的魅力所在。

Martin Harrysson: You need to be something of a multisport athlete or someone who is bilingual or trilingual. What I mean is, it’s not necessarily about multiple languages; you just need to be technical enough. You need to be an engineer, to some extent, in the sense that you understand how a technical system works. But if you’re only technical and you can’t extract how decisions are being made, and all the informal connections that are happening, to solve a problem for a client, you won’t be very good at solving the problem. That requires a lot more interpersonal experience: being able to communicate well, ask the right questions, and empathize.
Martin Harrysson： 你需要在某种程度上像个“多项全能运动员”，或者是一个会说双语或三语的人。我的意思是，这不一定是字面上的多种语言；你只需要具备足够的技术能力。在某种程度上，你需要是一名工程师，因为你得懂技术系统是如何运作的。但是，如果你只懂技术，却无法洞察决策是如何做出的，也无法理清所有正在发生的非正式人际联系，从而为客户解决问题，那你在解决问题方面就不会太出色。这需要丰富得多的人际交往经验：能够良好沟通、问对问题，并具备同理心。

Nick Robson: I think it’s a combination of a few different things. One is actual content knowledge and content expertise. The second is good people skills. Delivering business impact, consulting—it’s all a people sport. The last is being good with technology, yes, but the more important skill is the ability to learn quickly. I think the best FDEs are the ones who can be super creative or think outside the box for something that has never been created before. And a big part of the job is inspiring clients.
Nick Robson： 我认为这是几种不同特质的结合。第一是实际的业务领域知识和专业素养。第二是出色的人际交往能力。创造业务影响、做咨询——这都是一项“与人打交道的运动”。最后是精通技术，没错，但更重要的技能是快速学习的能力。我认为最优秀的 FDE 是那些极具创造力，或者能为前所未有的事物打破常规思考的人。而且，这项工作的很大一部分在于启发和激励客户。

我虽然行过死荫的幽谷，也不怕遭害，因为你与我同在。
诗篇 23:4

1. 角色定位：FDE 是谁？