AI 代理时代的业务分析师

AI 可以在一分钟内生成用例大纲，在一小时内将简单想法转化为基础原型，并在过去所需时间的一小部分内生成需求文档。这种转变已经显现在日常业务分析中，实践者使用 AI 来准备需求获取会议、起草需求并在验证前识别差距。只看这些交付物，结论似乎显而易见：业务分析师正在消失。

I do not share that conclusion. I have worked in business and IT analysis, solution design and digital transformation for years, watching continuous change and different stages of adaptation. What is disappearing is not the role. What is disappearing is its periphery. And that is something entirely different.

分析师的职责

当你向客户询问他们需要什么时，他们会告诉你他们想要什么。这两者并不总是相同。并不是因为他们不了解自己的工作——而是他们往往无法充分表达自己的需求。日常的例行活动常常被忽略，因为客户每天都会自动执行这些工作。边缘案例容易被遗漏，因为人们往往只在它们发生时才记得。

部门之间的连接往往是从个人视角理解的，而不一定是从整体的角度。当客户决定一次性更改多个系统时，他们常常没有意识到更改一个系统几乎总会同时影响一个业务流程。

分析师的工作不是把客户说的话记录下来，而是找出客户没有说的内容，并不断提问直至所有信息浮现。分析师会追踪系统、数据和业务流程之间的连接、依赖和后果。

That is a diagnostic capability, not an administrative activity.

业务分析师的三大方向

业务分析的边界从未固定。分析师早已向产品所有权、项目管理以及更技术性的角色转变。AI 只是通过缩短例行交付物所需时间，加速了这一趋势。

A recent Gartner survey found that more than half of organizations had redesigned or redefined roles because of AI, while 78% of HR leaders agreed that workflows and roles would need to realize the value of their AI investments.

From my observations and direct experience, three main trajectories are taking shape.

1. 向产品与业务方向发展

热衷于塑造产品的分析师可能会转向更广泛的角色，涵盖产品所有权、业务分析和交付协调等职责。这类组合在小团队中已经存在。随着 AI 减少了生成例行工件的时间，这些职责之间的界限可能会变得更加流动。

This should not mean handing four jobs to one person simply because AI can write the documents. The opportunity lies in bringing together responsibilities that already depend on the same understanding of customer needs, product priorities and business outcomes.

This direction also requires a stronger understanding of data. Analytical work has often focused on processes, functionality and requirements while leaving the data layer in the background. AI systems make that separation increasingly difficult.

An analyst who understands both the business context and the data can recognize when a model is correctly answering the wrong question. That may happen because the available data does not represent the situation accurately, the business definition behind a metric has changed or the original question was poorly framed.

AI can help almost anyone create a dashboard or report. Interpreting what the result means for a particular business, at a particular moment, still requires context and judgement. Forrester describes a similar change across software roles, with AI moving time away from repetitive artifact production and toward activities such as validation, orchestration and control.

2. 向技术原型方向发展

对开发有强烈兴趣的分析师可能能够先与客户沟通，然后构建一个可运行的原型——不仅仅是模型，而是客户可以测试和验证的真实作品。

This changes requirements validation: customers can interact with an idea instead of trying to interpret it from a document. Misunderstandings can surface earlier, while changes are still relatively inexpensive to make.

But a prototype is not a production system.

Security, integration, scaling and performance under load are not solved by “AI helps me code”. Building enterprise software for production requires a different level of engineering expertise. Some analysts may gradually develop those skills and move closer to production-level development. It is a considerably more demanding path than rapid prototyping suggests, but it is a realistic one.

3. 向项目与交付管理方向发展

分析师已经在协调客户讨论、参与估算以及管理范围、需求和变更相关的问题。这自然与项目管理产生交叉。

On smaller projects, one person may be able to combine analytical and coordination responsibilities because both depend on understanding scope, stakeholders and dependencies. That can shorten communication paths and make it easier to connect day-to-day delivery decisions with the original business need.

This does not make the project manager redundant. On larger projects, where coordinating people, risks and dependencies is genuinely complex, project management remains a specialist discipline. The blended approach makes sense only where the scope allows it.

The distinction matters. AI may reduce the effort required to create plans, meeting summaries or status reports, but producing those artifacts was never the full value of project management either.

能够出色驾驭这些方向的特质

所有三大方向都要求分析师突破熟悉的角色边界。好奇心让他们愿意尝试新工具和新方法。持续学习可以防止我所说的“知识债务”: 过时的假设和技能的逐渐累积，正如遗留系统中的技术债务。

Perhaps most important, however, is the ability to verify. Analysts are accustomed to asking “Is this actually correct?” before asking “Is this done?” In an environment full of fluent, professionally presented AI outputs, that habit becomes more valuable.

AI can produce incorrect answers in a confident and persuasive form – a pattern highlighted by MIT Sloan researchers as one of the key reasons human oversight remains essential. Its outputs can be structured, fluent and polished.

Failure does not happen at the point of using AI. It begins when a plausible output is accepted without verification.

问题不在于业务分析师是否会消失

许多关于分析师角色未来的文章都是以预测的口吻写成的。这不是预测，而是观察: 角色正如以往一样在变化。不同之处在于速度——以及其边缘正变得几乎对所有人都可触及。

A developer can use AI to conduct requirements analysis. A manager can use it to generate user stories. They may not do either particularly well, but an analyst whose value rests solely on producing deliverables will have a difficult time.

What remains at the center of the role is the ability to uncover unstated needs, connect business and technical contexts, verify outputs and see connections that the customer cannot. According to IIBA’s 2025 Global State of Business Analysis Report, 74% of practitioners say AI is positively affecting their careers, while human skills such as communication, strategic thinking and adaptability are becoming more important.

The question is not whether business analysis will disappear. It is what remains when its periphery becomes available to everyone.

The answer is the judgment that takes years of practice, varied projects and experience with failure to develop. It is the ability to recognize that a documented requirement is incomplete, a working prototype solves the wrong problem, or a polished answer rests on a false assumption.

That is the part of business analysis that cannot be acquired quickly.