The Rise and Fall of Corporate Consulting

Swiss Air was more than an airline to the Swiss people. It was a national symbol, an icon that embodied the very quality Switzerland prided itself on: precision, punctuality, service, and above all, financial property. For decades, the airline was so financially solid that it earned the nickname the flying bank. Generations of Swiss children received shares of Swiss Air as gifts. The airline operated as Switzerland's global ambassador until October 2001.

The collapse began a decade earlier when Swiss voters rejected to join the European Union. Switzerland's vote to stay out of the EU left Swiss Air locked out of the big alliances with other airlines. As an outsider, the airline could only watch as British Airways, Luansa, Air France, and KM kept adding flights to their schedule. Swiss Air's own attempts to grow independently were hampered by the EU countries, and the airline was trapped. Too small to compete in its tiny home market, and yet blocked from the broader EU market.

Desperate for a way in, Swiss Air hired McKenzie. McKenzie proposed something called a Hunter strategy. Instead of joining or forming an alliance, which is an uphill battle in and of itself, buy minority stakes in multiple European airlines and stitch together a network from the outside. Swiss Air followed the playbook and spent hundreds of millions to acquire 49% of Belgium's Sabina, as well as big stakes in Germany's LTU and several struggling French carriers. And that plan sounds pretty solid, except for one tiny detail. EU law did not allow a non-EU airline to take majority control. So Swiss Air could not truly influence these airlines that it had bought, but it was still on the hook for their losses. Sabina's unions blocked cost cuts. Headcount ballooned, and Swiss Air had to write off its entire equity stake within a year.

Instead of backing off, management doubled down because they hit record profit in 1998. And that was the apparent validation of McKenzie's strategy. When fuel prices rose and demand went down, the House of Cards collapsed. By 2000, Swiss Air posted a 2.9 billion franc loss. And a KPMG audit in 2001 showed 17 billion francs of debt supported by 555 million in equity, which comes to a 30:1 debt to equity ratio. After 9/11, with cash almost gone and banks unwilling to extend more credit, Swiss Air collapsed on October 2nd, 2001, and ended the airline's existence overnight.

McKenzie's Hunter strategy advised Swiss Air to pursue an expansion model that looked brilliant in a deck, but was impossible in reality under EU laws. The acquisition strategy was framed as the only way to stay competitive, but never asked the hard question: Can you actually control and fix these struggling airlines if you're legally barred from owning them? Swiss Air executed the strategy exactly as advised, and it destroyed them, wiping out about 15 billion Franks. [music] And a national icon. McKenzie, excluded from execution, walked away with its fees and reputation largely intact. While Switzerland was left to ask whether the most prestigious consultants in the world had effectively helped kill their flag carrier.

Today's episode is about the rise and fall of corporate consulting. [music] And where it's headed now that AI is part of the equation. The good, the bad, and the ugly. Let's dive in.

The golden age of management consulting was basically a well-orchestrated arbitrage. You get a lot of junior labor. You put them to work at scale and benefit from a pretty sizable margin. Yes, consultancies sold their services as strategic insights and partner-level expertise. But in reality, a lot of it was marketing lingo. Just like the word "gold" in a gold-plated jewelry.

There is a professor by the name of David Maester who formalized a theory that for a consulting firm to function, it needs to have a very high margin, around 70%. This 70% margin transformed consulting from a professional service into a labor arbitrage machine. In a traditional consulting firm, the hierarchy goes like this: Analyst, Consultant, Manager, Principal, and Partner. Partners make between $1 and $5 million annually, but not through their own billing hours. They make it through what Professor Maester calls the surplus generated from non-partner staff. Once again, partners make millions selling knowledge and expertise through the surplus generated from non-partner staff. In plain English, this means that I am selling my partner-level knowledge, except I'm not doing it myself. If you're listening to this and thinking, "This sounds awfully familiar, but I can't put a finger on what it is," let me help you. It's called the good old pyramid scheme. A business where the pyramid is the business model itself.

The underlying economic model is based on the revenue per employee metrics. And McKenzie, BCG, and Bain are strikingly consistent with it. As of the last two years, revenue per consultant averages around $430,000 per year. But entry-level business analysts at McKenzie earn around $120. That is their base, and around $150,000 total, which means that there is a 3x markup.

Now, let's zoom out. What is the typical staffing structure on a typical consultant project? You may ask, "What do you mean by typical?" Let's say a most common mid-range project. Now, who are the biggest consumers of consulting services? Mid-market enterprises. And the reason for it is because they have a unique gap that makes them hyperdependent on outside resources. Mid-market enterprises deal with big-boy problems: M&A, market expansion, new territories. But what they don't have is the big-boy enterprise resources. They have the budget for consulting firms, but not the budget or time to build their own in-house expertise.

A typical staffing model for a consulting project puts one senior partner who dedicates 20% of their time, meaning that they manage four other clients. One engagement manager, two consultants, two analysts over a three-month period. The staffing depends on the project type, and Professor Maester calls it the leverage decision. High-leverage projects put armies of junior staff at 4 to 6x cost multiples and bring the firm around 70% gross margins. Low-leverage projects are staffed with senior partners, and yes, they cost more to the client, but they also compress margins to about 30% to 40% for a consultancy, which means that the unit economics of a consulting firm favors high-leverage projects. And this is why partner compensation derives not from partners' own billable hours, but from the leverage they have underneath. A firm with 100 junior consultants, 30 managers, 10 partners achieves 10-to-one leverage, and each partner extracts margin from 10 subordinates underneath.

This structure gave life to a myriad of cultural artifacts associated with consulting: client site visits, weakened slide deck preparations, enormous stress. And it makes sense because you have to somehow justify costs that are blown out of proportion. Junior consultants would log thousands of billable hours to justify their $75 an hour rate and generate the $300 an hour margin, which would fund the partner compensation.

To put this in perspective, you guys know I love analogies, so let's use one. Let's say I start a company called Daria's Lemonade Stand. I make lemonade by squeezing lemons, adding sugar and water. The cost to make one cup is $0.50. I sell each cup for $5. Profit per cup $4.50. That is a 90% margin. My business model scales linearly. The more lemonade I make, the richer I get. Then one day I wake up and go, "What am I, an amateur?" I'm going to make more lemonade stands, and I'm going to sell my lemonade to restaurants. I hire Maria, my bar manager. She runs the stand daily. She trains staff. She handles the quality. And then I hire Lauren, who makes the lemonade. She would serve customers, and she would restock the cups. Lauren is my 77% margin. And then I hire Jack, who stocks shelves and prepares the lemons. Now, instead of individual cups, I sell lemonade packages, a continuous supply of lemonade to offices nearby. And I finally find my big corporate contract for $200,000 a month of continuous lemonade supply. This contract requires my oversight, Maria managing communications with offices, Lauren making lemonade, and Jack preparing the ingredients. My only major constraint is people. Lauren could only make so many cups a month. If I want to scale, I need to hire more baristas and buy more lemons. But then I buy an automated lemonade machine for 50 grand. It produces the same quality as Lauren or Jack combined. And it costs me $2 a month to operate, an electricity bill, hypothetical one, but still. Fulfills orders 24/7 without any breaks. And guess what? Now my margin is no longer 77%. It's 99%. You may think, "What a profitable business." Yes, but it has limitations.

The profitability of the leverage model depends on maximizing billable hours. The more hours I put into a project, the better. The cheaper the labor, the better. But what this also means is linear scaling. A partner could only supervise only 10 to 20 direct reports effectively. And if I have a larger team of five to seven people or more, I would have to hire additional management. This model generated extraordinary returns for three decades.

But the most fundamental impact of AI on consulting is in realizing that it disrupts the unit economics of the consulting business model. It doesn't even have to fully automate the work. All it has to do is to boost a mid-level consultant. But if it automates fully, it destroys the margin. And the margin is the reason why consultancies operate in the first place. Consulting firms sell insights but profit from leverage. Remove the leverage, and the economics of a $16 billion McKenzie fully evaporate.

The economics of management rest on a premise that clients pay premium rates because the work that consultants provide requires human expertise deployed at scale. But this very notion was proven wrong. Guess by whom? By McKenzie's very own internal AI platform called Lily. July 2023, McKenzie launches Lily, their internal AI platform trained on 100 years of the firm's intellectual property. Hundreds of thousands of documents, cases, interview transcripts, frameworks. And within a year and a half, 75% of McKenzie's consultants, which, let me remind you, it's 45,000 people we're talking about, are using it monthly. Once again, 3/4 of the firm uses AI as a core research tool, not for writing emails or summarizing notes. It serves as an essential tool for research. And research is what consulting business sells.

Put yourself in the shoes of a junior consultant. You've got to answer a question like, "How to get rid of rats in New York?" You break down the problem. You make it digestible. And then what? Then you want to know how similar problems were solved in the past. Now, you work for a major consultancy, and that's exactly what consultants do. You have the data. You have the data from the past 100 years. You have frameworks. You have methods. You have case studies. You have networks decks. But you need time to sort through piles and piles of cases and piles and piles of data.

Here's what used to happen. When a client sent McKenzie a request for proposal, a junior consultant would get assigned to write it. They would then spend hours or even days digging through the internal knowledge base, trying to figure out whether it has been done before and what can we learn from previous cases, how to approach a problem, what worked and what didn't. But with Lily, they get a summary of it right away. They go through precedents, they find contacts, they find frameworks, synthesize data, all kinds of sources with links. And this research can be done in 3 hours. They would then prepare for a kickoff call, and someone called an engagement manager would structure the entire engagement, work streams, scope, staffing, timelines. All of this would require two to three days of planning. With Lily, you get answers: what project structure worked, what trade-offs you had, resources, and everything you need to know about previous cases. You take it, you refine it, and it's done in less than a day.

And now we get to the legendary part that defines consulting as an industry: the decks. And Lily can auto-generate an entire PowerPoint from prompts. Anyone who has prepped a deck before knows that a deck is a lot less about content and a lot more about moving the freaking colors and circles around. And sure, we all have company-approved templates, but let's be honest, half the time it doesn't work. Lily wiped around 20% of a junior analyst's work: research, synthesis, deck creation, client interaction. And for a 45,000-people firm, that's about 8,000 full-time employees eliminated. McKenzie cut 5,000, and the rest got absorbed into higher-level work. And no surprise, when they did that, they did not lose any revenue, which in turn proved that the clients were paying for artificial scarcity.

What's remarkable is the culture shift within the consultancy itself. It's one thing when your customer prompts AI because they're curious if they can do it cheaper and get the answers quickly. It's another thing when AI becomes part of your staff. When you start a meeting with, "Have you asked Lily?" you're recognizing that the value is no longer research. It's what you do with Lily's output. And therefore, the old consultant skill set, which is exhaustive research, becomes obsolete. The irony is the fact that the business that sells optimization optimized itself. When they introduced AI and cut 30% of their own work, they proved that their own business model was artificially inflated.

So what does this mean for consulting as an industry? Is it dead? No, it's not, and it won't be. But yes, it is branching into two universes that will barely recognize each other within 5 years.

The first branch is boutique consultancies, which is essentially going back to what consulting was always supposed to be: the elite expertise that costs a lot of money. Five to 10 people, extreme specialization, for example, healthcare AI ethics, ESG compliance, supply chain resilience. And they will definitely be heavily skewed towards senior staffing, working prototypes instead of decks, proof of concept over frameworks.

Or the second path: they will become software companies with a consulting wrapper. They cut the base of the pyramid, which is a bunch of junior consultants and recent MBA grads without any specialization. And the second path is basically boutique consultancy at scale, much higher percentage of domain and technical specialists.

You know what's also interesting? Which stock do you think went up surprisingly or unsurprisingly high? Palantir. Before 2022, Accenture or Deloitte would win a federal contract, for example, an overhaul of a defense agency's logistics, and they would get it as a prime contractor. Prime meaning they owned the relationship. They signed the main $300 million contract, and they deployed a few hundred consultants for implementation. They treated the actual software, SAP, Salesforce, Oracle, as something that they bought as a subcontracted item. But in the AI configuration, the contract first goes to Palantir as a technology provider, and then Accenture or Deloitte become preferred implementation partners, which is essentially a subcontractor. And this is how the pyramid inverts. Palantir makes almost $1.2 billion a quarter with a 50% margin, which is pure profit. In software as a service, investors use a term called the Rule of 40, where if you add growth rate and profit margin together, anything over 40 is considered elite. Palantir is sitting at 114, which is almost an unbeatable number in the software business at scale.

Once again, the reason this gap in margins exists is because consulting is a service business which scales linearly. If you want to add another million-dollar in revenue, you hire three more consultants. Each consultant costs you around $130K fully loaded, and then your costs scale proportionally. The more people, the more revenue, the more expenses. But software scales exponentially. To add a million-dollar in revenue, you need to sell more of the software. If your software costs $100,000 a year, you make 10 sales, and you've got a million. Marginal cost of a new customer: $10 to $20K. And that's going to be basically cloud infra, which you're going to be paying for cloud bills.

So, what does this mean for a career in consulting? My good friend once showed me a meme that illustrates the consulting industry pretty well, in my opinion. I think the fact that the market is changing is definitely for the better, especially for early-career folks, because it will no longer create an illusion of easy money. I've had experience working and studying with people whose entire career was built in consulting. And I formed an impression that it's a very specific and a very narrow skill set with low transferability of skills, because you're locking yourself into working with companies of a very certain profile and a certain size. And your entire career is about dealing with the problems that happen at that level. But if you're working in a market with extreme uncertainty, a market where you need a wide variety of skills, that doesn't do you any good.

Consulting was once one of a few elite careers in business that could land you $100,000 in starting salary right out of school. But objectively, a generic skill set should not cost that much. What we're seeing in '26 is the entry-level hiring collapse. Consulting job postings are down. Entry-level consultant hiring plunged 54%. PwC cutting 30% of entry-level roles by 2028. The infamous and prestigious by social standards grind is exactly what gets automated. The career path is fairly eroded, and a 20-year-old should probably think twice before entering the field.

But on the flip side, consulting is finally coming back to what it was always supposed to be: an elite domain expertise. We hope this was helpful. Let us know what you think in the comments. Till next time. Bye.