276,000 New Colleagues
On May 19, 2026, KPMG and Anthropic announced a global alliance. Every one of KPMG’s 276,000 employees across 138 countries now gets access to Claude — Anthropic’s AI — integrated directly into the firm’s client-delivery platform. Claude isn’t a side tool or a pilot program. It’s embedded into the core workflow: tax, advisory, private equity, cybersecurity.
This follows Anthropic’s earlier alliance with PwC. Two of the Big Four are now anchored to the same AI lab. Deloitte and EY are making their own moves. The pattern is clear: the consulting industry isn’t experimenting with AI anymore. It’s restructuring around it.
Think about what that means. KPMG advises companies on digital transformation for a living. Now it needs someone else’s AI to transform its own work. The firm that tells clients what technology to adopt just became a client itself.
Which raises a structural question: if the real capability lives inside the AI model, what exactly is the consulting firm selling?
What Consultants Actually Sell
To understand what’s shifting, you need to understand what consulting firms actually do. Strip away the branding and the acronyms, and a consulting engagement follows a remarkably consistent pattern:
- Gather information — interviews, documents, data requests, industry research
- Analyze — find patterns, compare benchmarks, identify gaps
- Synthesize — turn analysis into a structured narrative with a recommendation
- Present — deliver the narrative to a client in a way that enables a decision
A junior analyst at a Big Four firm spends most of their time on steps 1 and 2. A senior partner mostly does step 4. The value chain is a pyramid: lots of people at the bottom doing research and analysis, fewer people at the top making judgments and maintaining relationships.
AI disrupts this pyramid from the bottom up.
Minutes, Not Weeks
Here’s the line from the KPMG announcement that matters most: a tax regulation adjustment that “used to take weeks” now completes in “minutes” with Claude integrated into their platform.
Think about what that sentence means. A tax regulation adjustment is a well-defined analytical task — read the new regulation, compare it to the current compliance posture, identify the gaps, draft the updated guidance. It requires expertise, but it’s fundamentally a pattern-matching and text-generation problem. Exactly the kind of work LLMs are built for.
Weeks to minutes isn’t an incremental improvement. It’s a categorical change. The same work still needs to happen — the regulation still needs to be read, the gaps still need to be found — but the labor required collapses by orders of magnitude.
Now multiply this across every engagement type. Due diligence for an acquisition. Market sizing for a new product. Regulatory compliance audits. Competitive benchmarking. Risk assessments. These are all variations of the same pattern: gather structured information, analyze it against a framework, produce a document. Every one of these tasks is about to get dramatically faster.
The Body Shop Problem
Consulting has always had a tension at its core. Clients hire consultants for their judgment and expertise. But firms bill by the hour and staff by the headcount. The business model incentivizes putting more people on a project for longer, even when the intellectual work could be done by fewer people in less time.
This is the “body shop” critique that has followed the industry for decades: consulting firms often sell labor disguised as insight.
AI exposes this tension completely. If Claude can do in minutes what a team of analysts did in weeks, the labor-arbitrage model breaks. You can’t bill 200 analyst-hours for a deliverable that an AI produced in an afternoon. Or rather, you can — but only until your competitor doesn’t.
The firms that move first will be able to offer the same quality of analysis at a fraction of the cost and timeline. The firms that don’t will find themselves defending a pricing model that clients can see through.
What Survives
Not everything in consulting is pattern-matching. The parts that survive AI are the parts that were always the real value:
Relationships and trust. A CEO doesn’t hire McKinsey or KPMG because they need someone to read a regulation. They hire them because they need a trusted advisor who understands their business context, their political dynamics, their risk tolerance. That’s a human judgment call, and it’s the reason senior partners get paid what they do.
Domain expertise under ambiguity. AI is good at well-defined analytical tasks. It’s less good at navigating situations where the problem itself isn’t clearly defined — where you need to figure out what question to ask before you can answer it. The best consultants do this intuitively. They walk into a messy situation and see structure that others don’t.
Accountability. When a board approves a $2 billion acquisition based on a due diligence report, someone needs to stand behind that report. AI can generate the analysis, but a human still signs their name to it. That accountability — and the liability that comes with it — is worth paying for.
Implementation. Analysis is only half the job. The other half is getting organizations to actually change. Change management, stakeholder alignment, political navigation — these are deeply human activities that AI can inform but not perform.
What Changes
If the bottom of the pyramid automates, the shape of consulting firms changes. Fewer analysts, more senior advisors. Fewer people doing research, more people doing judgment. The business model shifts from billing hours to billing outcomes — “we’ll deliver this analysis in 3 days, validated by our experts, for a fixed fee.” The client pays for the result, not the labor.
This is already implicit in the KPMG-Anthropic deal. KPMG Blaze, their new Claude Code-powered offering for legacy IT modernization in private equity, isn’t “consultants plus AI.” It’s AI-first delivery with consultant oversight.
The consulting industry is a $300+ billion market built on selling structured thinking. AI can now do structured thinking at near-zero marginal cost. One scenario: the Big Four absorb the efficiency gains and pocket the margin. Another: competition forces prices down, because a boutique firm with the same AI tools and a few domain experts can deliver comparable analysis. A third: the definition of “consulting” expands — when analysis is cheap, you can apply it to problems previously too small to justify hiring a consultant, and the market grows because the cost floor drops.
But there’s a fourth scenario — the most radical one.
The AI-Native Firm: Selling Results, Not Advice
Someone builds an AI-native consulting firm from scratch — one that doesn’t sell advice at all. It sells outcomes.
Traditional consulting delivers a slide deck. “Here’s what we found. Here’s what we recommend. Good luck implementing it.” The client pays for the thinking, then has to do the doing themselves — or hire more consultants for that, too.
An AI-native firm skips the middle. You don’t get a 200-page report on how to restructure your supply chain. You get a restructured supply chain. The AI does the analysis, identifies the optimization, generates the implementation plan, and executes the changes — with human experts providing oversight at key decision points, not producing the work product.
This is a fundamentally different business. The deliverable isn’t a recommendation. It’s a result. The client doesn’t pay for “what you should do.” They pay for “it’s done.”
Consider what this looks like in practice:
- Tax compliance: Instead of a report identifying regulatory gaps, the firm files the amended returns directly. The AI reads the regulation, maps the gaps, drafts the filings, a licensed CPA reviews and signs off. The client never sees a slide deck.
- Due diligence: Instead of a binder of findings for the board to interpret, the firm delivers a go/no-go recommendation with a risk-adjusted valuation model that updates in real time as new data comes in. The analysis isn’t a one-time document — it’s a living system.
- IT modernization: Instead of an architecture roadmap, the firm migrates the codebase. KPMG Blaze is already pointing in this direction — Claude Code refactoring legacy systems, not consultants drawing diagrams of how they should be refactored.
The economics are radically different. A traditional firm needs 50 people for 6 months to deliver a strategy project. An AI-native firm might need 5 domain experts for 3 weeks — because the AI does the research, the analysis, the drafting, and a significant portion of the execution. The humans are there for judgment, accountability, and the things that require a handshake.
This firm doesn’t exist yet at scale. But the pieces are all available: frontier AI models, domain expertise that can be packaged as context, and a generation of clients who would rather pay for outcomes than for hours. The first team to assemble these pieces into a credible offering will have a structural advantage that incumbents — burdened by headcount, legacy processes, and partner economics — will struggle to match.
Weeks to Minutes, Minutes to Seconds
Two days ago, KPMG put AI at the center of its global operations. The stated reason is efficiency: do the same work faster with fewer resources. But the deeper implication is structural. An industry that sells thinking by the hour is adopting a technology that thinks in seconds. The economics of that mismatch will play out over the next few years, and they won’t be subtle.
The consultants who thrive won’t be the ones who can analyze faster — the machines already won that race. They’ll be the ones who can do what machines can’t: sit across from a CEO, understand what they’re actually worried about, and help them make a decision they can live with.
That’s not a task you can automate. At least, not yet.
中文翻译
276,000 个新同事
2026 年 5 月 19 日，毕马威与 Anthropic 宣布全球战略联盟。毕马威遍布 138 个国家和地区的 276,000 名员工全部获得 Claude——Anthropic 的 AI——的使用权限，直接集成到公司的客户交付平台中。Claude 不是一个辅助工具，也不是试点项目。它嵌入了核心工作流：税务、咨询、私募股权、网络安全。
此前 Anthropic 已经与普华永道建立了类似联盟。四大中的两家现在锚定在同一个 AI 实验室上。德勤和安永也在各自布局。趋势已经很清楚：咨询行业不再是在"尝试" AI，而是在围绕 AI 进行重组。
想想这意味着什么。毕马威靠教别人做数字化转型吃饭，现在它需要别人的 AI 来转型自己的工作。那个告诉客户该用什么技术的公司，自己也变成了客户。
这就引出一个结构性问题：如果真正的能力藏在 AI 模型里，那咨询公司到底在卖什么？
咨询公司到底在卖什么
要理解正在发生的变化，你需要理解咨询公司实际上在做什么。剥去品牌和缩写，一个咨询项目遵循惊人一致的模式：
- 收集信息 — 访谈、文档、数据请求、行业研究
- 分析 — 发现规律、对标比较、识别差距
- 综合 — 将分析转化为带有建议的结构化叙事
- 呈现 — 以促成决策的方式把叙事传达给客户
四大的初级分析师大部分时间花在第 1 步和第 2 步。高级合伙人主要做第 4 步。价值链是一个金字塔：底部大量的人做研究和分析，顶部少数人做判断和维护关系。
AI 从底部开始颠覆这个金字塔。
分钟，不是周
毕马威公告中最重要的一句话：一项税务法规调整，之前**“需要数周”的工作，现在在 Claude 集成到平台后“几分钟”**完成。
想想这句话意味着什么。税务法规调整是一项定义明确的分析任务——阅读新法规，与现行合规状况比对，识别差距，起草更新指引。它需要专业知识，但本质上是一个模式匹配和文本生成问题。正是 LLM 擅长的工作。
从数周到几分钟，不是渐进式改进，而是范畴性的变化。同样的工作仍然需要完成——法规仍然要读，差距仍然要找——但所需的人力投入骤降了几个数量级。
现在把这个效率提升乘以每一种项目类型。并购尽职调查、新产品市场规模评估、合规审计、竞争对标、风险评估。这些都是同一模式的变体：收集结构化信息，用框架分析，产出文档。每一项任务都即将大幅加速。
“人头工厂"问题
咨询行业核心一直有一个拧巴的地方。客户雇顾问是为了他们的判断力和专业知识。但公司按小时计费、按人头配置。商业模式激励把更多人放到项目上、做更久——即使智力工作本可以由更少的人在更短时间内完成。
这就是困扰行业数十年的"人头工厂"批评：咨询公司经常在卖包装成洞察的劳动力。
AI 把这个矛盾彻底摊开了。如果 Claude 能在几分钟内完成一个分析师团队几周的工作，劳动力套利模式就崩了。你不能为一个 AI 一下午就产出的交付物计 200 个分析师工时。或者说，你可以——但只能撑到你的竞争对手不这么做为止。
先行动的公司将能以更低成本和更短周期提供同等质量的分析。不行动的公司会发现自己在捍卫一个客户已经看穿的定价模型。
什么能存活
咨询中并非一切都是模式匹配。能在 AI 时代存活的部分，恰恰是一直以来真正有价值的部分：
**关系与信任。**CEO 雇麦肯锡或毕马威不是因为需要人帮忙读法规。他们需要一个理解业务背景、政治动态、风险偏好的可信顾问。这是人类的判断，也是高级合伙人薪酬的来源。
**模糊环境下的领域专长。**AI 擅长定义明确的分析任务。它不太擅长应对问题本身还没被清晰定义的场景——你需要先搞清楚该问什么问题，然后才能回答。最优秀的顾问凭直觉做到这一点。他们走进一团混乱，看到别人看不到的结构。
**责任。**当董事会基于一份尽调报告批准 20 亿美元的收购时，需要有人为这份报告背书。AI 可以生成分析，但人类要签上自己的名字。这份责任——以及随之而来的法律风险——值得付费。
**落地执行。**分析只是工作的一半。另一半是让组织真正做出改变。变革管理、利益相关者协调、政治斡旋——这些是深度人类活动，AI 可以提供信息支撑，但无法代替执行。
什么会变
如果金字塔的底部被自动化，咨询公司的形态就会变。更少的分析师，更多的高级顾问。更少做研究的人，更多做判断的人。商业模式从按小时计费转向按结果计费——“我们会在 3 天内交付这份分析，经专家验证，固定费用。“客户为结果付费，不为劳动力付费。
这在毕马威-Anthropic 的交易中已经隐含了。毕马威 Blaze——他们基于 Claude Code 的新产品，用于私募股权的遗留 IT 现代化——不是"顾问加 AI”，而是 AI 优先交付加顾问监督。
咨询行业是一个 3000 多亿美元的市场，建立在出售结构化思维之上。AI 现在可以以近零边际成本进行结构化思维。一种可能：四大吸收效率收益，获取利润率改善。另一种可能：竞争迫使价格下降，因为一家拥有相同 AI 工具和几位领域专家的精品公司也能交付同等质量的分析。第三种可能：咨询的定义扩大——当分析变得廉价，可以应用到以前太小不值得雇顾问的问题上，市场因为成本底线下降而增长。
但还有第四种可能——最激进的一种。
AI 原生咨询公司：卖结果，不卖建议
有人从零开始打造一家 AI 原生的咨询公司——一家根本不卖建议的公司。它卖的是结果。
传统咨询交付的是一份 PPT。“这是我们的发现，这是我们的建议，祝你实施顺利。“客户为思考付费，然后得自己去执行——或者再请更多顾问来帮忙执行。
AI 原生公司跳过中间环节。你不会拿到一份 200 页的供应链重组建议报告。你拿到的是一条已经重组好的供应链。AI 完成分析、识别优化点、生成实施方案并执行变更——人类专家在关键决策节点提供监督，而不是亲手生产工作成果。
这是一种本质不同的业务。交付物不是建议，而是结果。客户不为"你应该怎么做"付费，而为"已经做好了"付费。
看看这在实践中是什么样：
- 税务合规：不是一份指出法规缺口的报告，而是直接提交修正后的申报。AI 阅读法规、映射差距、起草文件，持牌注册会计师审核签字。客户永远不会看到 PPT。
- 尽职调查：不是一本供董事会解读的发现汇编，而是交付一个包含风险调整估值模型的买/不买建议，且随着新数据流入实时更新。分析不是一次性文档——而是一个活的系统。
- IT 现代化：不是一份架构路线图，而是直接完成代码迁移。毕马威 Blaze 已经指向了这个方向——Claude Code 重构遗留系统，而不是顾问画图说明应该怎么重构。
经济模型截然不同。传统公司需要 50 人做 6 个月来交付一个战略项目。AI 原生公司可能只需要 5 位领域专家做 3 周——因为 AI 完成了研究、分析、起草和相当部分的执行。人类的存在是为了判断、问责，以及那些需要握手才能完成的事。
这样的公司目前还没有大规模出现。但所有拼图已经就位：前沿 AI 模型、可以打包为上下文的领域专业知识、以及一代更愿意为结果而非工时付费的客户。第一个把这些拼图组装成可信产品的团队，将拥有结构性优势——那些被人员规模、遗留流程和合伙人经济所拖累的现有巨头，将很难追上。
从数周到分钟，从分钟到秒
两天前，毕马威把 AI 放在了全球运营的中心。表面原因是效率：用更少资源更快完成同样的工作。但更深层的含义是结构性的。一个按小时出售思维的行业，正在采用一种以秒为单位思考的技术。这个错配的经济学将在未来几年展开，而且不会是微妙的。
能脱颖而出的顾问不是那些分析更快的人——机器已经赢了那场比赛。而是那些能做机器做不到的事的人：坐在 CEO 对面，理解他们真正担心的是什么，帮他们做出一个能安心的决定。
这不是一项可以自动化的任务。至少，目前还不是。