McKinsey Sucks: Why Big Consulting Is Losing Its Edge

McKinsey sucks. The once-revered consulting giants are seeing their shine wear off. Scandals and missteps have tarnished their reputations, and even their own analysts admit things aren’t what they used to be.

AI Is Draining the Value Out of Big Consulting

For decades, big consulting firms sold access to rare knowledge. You paid McKinsey money because you assumed they had something you didn’t: secret frameworks, best practices, and a bench full of people who thought like an MIT engineer with a Harvard MBA.

That used to be hard to reach. Now it lives in your browser.

With modern AI, almost anyone can get instant access to the same kind of technical, numbers-driven thinking that used to be locked up in expensive slide decks. Ask a good AI tool, and you can get market analyses, industry frameworks, competitive breakdowns, and “strategic options” that look a lot like what a junior consultant would produce after a long weekend (my students produce these almost every day in class).

That means a huge chunk of what big firms used to sell as high-value is now low-value and common. The “smart report” is no longer special. The real question becomes:

If AI can give me 70-80% of a McKinsey-style answer in a few minutes, what exactly am I paying McKinsey for?

AI is forcing every consulting firm to rethink its value proposition. If your value is built on secret jargon, generic frameworks, and thick PowerPoint decks, AI will chew through that and make it cheap. The only consulting that actually grows in value is the kind AI can’t copy: models that don’t live in its training data, and work that depends on real relationships, local context, and human judgment.

That’s where Value Engine lives.

A Model AI Can’t Copy

Value Engine’s model is different. We don’t play by the old rules, so we aren’t caught in the same trap. Our approach isn’t something you can just Google or have an AI clone. We bring a humanistic, value-first philosophy (not the usual profit-at-all-costs playbook), which is it’s grounded in deep understanding of how organizations and societies really work (see End Note 2). It’s not the kind of strategy you’d get from a robot consultant trained on decades of business jargon.

To understand the contrast, consider what happens when you rely on a cookie-cutter strategy versus a value-driven strategy:

The Generic Playbook (Old Consulting & AI)

Focus on short-term wins. Cut employees to reduce costs. Squeeze suppliers and customers for a better margin. Copy-paste the same “best practices” everyone else uses. This is the dominant logic taught to every MBA (see End Note 1). It often treats a business like a spreadsheet: optimize the numbers and ignore the human factor.

An AI, drawing on thousands of similar reports, will happily give you this kind of advice because it’s the most common pattern in its training data. It’s business by template – bland and one-size-fits-all…and often something that destroys value in the long run.

The Value Engine Playbook (New Consulting)

Focus on long-term value creation for all stakeholders. Invest in your people instead of treating them as costs; a motivated team will drive innovation and growth. Partner with your customers; understand their needs and co-create solutions, rather than viewing them as wallets to extract money from.

Look at the entire system your company operates in to find ways to make that system stronger while you grow (your community, your partners, the environment). This strategy is human-centric: it’s about relationships, trust, and sustainability. It’s also highly specific to each organization – no two companies have the exact same values and system, so no two Value Engine plans are identical.

In practice, that means Value Engine’s model produces unique, tailored recommendations that an AI wouldn’t think of. Why? Because we’re not drawing from the same old pool of data and clichés that everybody else is. Our model was developed from original research and real-world testing, not just the “most statistically likely” corporate answer.

In fact, if a typical business leader or McKinsey consultant tried to use a general AI to replicate what we do, they’d hit a wall. The AI would churn out the usual answers based on mainstream theories – it literally can’t reproduce an approach that breaks from the dominant logic. It’s like expecting a calculator to write a poem; it can only work with the formulas it knows.

The Bottom Line

Big consulting firms are facing a reckoning. Their old tactics are being commoditized by AI, and their credibility is eroding every time a news story breaks about a phony AI-generated report or a strategy debacle (See Deloitte’s $290,000 USD report for the government of Australia).

The good news is that this opens the door for new approaches that actually think differently. Value Engine’s humanistic model is one of those approaches – it’s not about churning out another pretty slide deck, it’s about genuine insights and creating value that lasts.

So yes, we’ll say it again: McKinsey (and its peers) suck – at least, they suck if you want fresh thinking and honest, people-centered strategy. You can get McKinsey’s brand of cookie-cutter advice from an AI or any business textbook. But if you want something truly innovative, grounded in human values and not replicable by a machine, you’re going to have to break away from the old way.

That’s exactly what we’ve done.

In a world where everyone else is feeding the same data into the same algorithms and getting the same answers, Value Engine offers a different path – one that no AI and no traditional playbook can steal or imitate. And that, ultimately, is why we’re not worried about AI coming for our jobs.

End Notes:

1. “Dominant logic” of MBA strategy – This refers to the mainstream business frameworks and theories taught in most business schools and used widely by consulting firms. They include things like classic competitive strategy (e.g., Michael Porter’s models of competition), resource-centric views of the firm (e.g., the Resource-Based View, which emphasizes controlling valuable assets), and transaction cost economics (which focuses on efficiency and cost reduction). These approaches all tend to focus on maximizing the company’s advantage or efficiency, often by exerting power – squeezing out competitors, driving down costs, leveraging bargaining power over customers or suppliers, etc. These ideas have dominated corporate thinking for decades, so much that they’ve become “common knowledge.” An AI trained on business literature will naturally default to these kinds of recommendations because they appear so frequently in the data.

   
   

2. Value Engine’s theoretical backbone – Value Engine’s model is rooted in institutional theory and systems theory, but we apply those big ideas in down-to-earth ways. Institutional theory means we consider the broader norms, values, and long-term social factors that impact a business – not just immediate profits. In other words, a company’s strategy should align with human values and the “rules of the game” in its industry and society. Systems theory means we see a business as part of a larger, interconnected system: a change in one part (like how you treat employees, or the experience customers have) can ripple through the whole network and back to your bottom line. This perspective leads us to strategies that emphasize sustainable, mutual value creation over time. It’s a more humanistic approach – treating the company as a community of people and a member of society, not just a profit machine. These concepts go beyond what traditional frameworks cover, which is why a generic AI (or a conventional consultant) doesn’t capture them well. They’re not the dominant narratives in case studies or MBA lectures, but they’re incredibly powerful in practice when crafting a resilient, values-driven strategy.

   
   

Sources:

• The Economist, “How McKinsey lost its edge.” The Economist questions whether McKinsey, as it nears its 100th anniversary, has passed its prime[4]. Key points noted include McKinsey’s slowing growth and workforce cuts since 2023[2], as well as controversies that hurt its reputation (e.g., involvement in the opioid crisis and South African corruption, which McKinsey later admitted and settled).

  
  

• Associated Press, “Deloitte Australia to partially refund $290,000 report filled with suspected AI-generated errors.” This AP News report describes how Deloitte used Azure OpenAI to help write a 237-page government consulting report, resulting in fabricated quotes and nonexistent references. Deloitte had to refund the final payment (~A$290k) for the project.

  
  

• The Guardian, “Deloitte to pay money back... after using AI in $440,000 report.” The Guardian confirms Deloitte’s partial refund and notes a senator’s remark that the firm has a “human intelligence problem,” underscoring the embarrassment of relying on flawed AI content.

  
  

• Wall Street Journal, “AI Is Coming for the Consultants. Inside McKinsey, ‘This Is Existential.’” (August 2025). As summarized by industry analysts, this WSJ piece reveals that McKinsey is heavily investing in AI – using bots to draft reports and presentations – and has cut over 10% of its staff in two years. About 40% of McKinsey’s revenue now comes from AI and tech-related projects. The article portrays AI as an existential challenge for the consulting business, since clients might not pay high fees for work that AI can do cheaply.

  
  

• Slashdot/Strat-Bridge commentary on consulting disruption (August 2025) – Highlights that McKinsey’s revenue growth slowed to 2% in 2024 and it cut 5,000 jobs. Meanwhile, competitor BCG grew 10%, shrinking McKinsey’s lead and possibly overtaking it by 2027. It also notes that tech firms like Palantir and OpenAI are encroaching on consulting by offering services to implement AI models, with Palantir’s revenue up 39% year-on-year. This trend threatens the traditional model, as clients may question paying premium consulting fees if AI can handle much of the analytical heavy lifting.

  
  

• Workfast Consulting Blog, “AI is Disrupting – And that’s a Good Thing.” (Aug 5, 2025). Written by a former Deloitte consultant, this piece (based on the WSJ article above) gives practical insight into how AI is changing consulting. It notes that tasks which used to require weeks of consulting work and hefty fees can now be done 80% by a good prompt in minutes. Clients are moving away from paying for a “suit with a PowerPoint” and instead seek partners who can help implement changes on the ground. In the author’s words, “AI can generate a thick report with high-level advice. That’s not the hard part. The hard part is helping organizations through messy, human change when technology moves faster than they can absorb it.” This sentiment echoes why Value Engine’s human-centric approach is so crucial – the real value is in execution and human insight, not just analysis.