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How McKinsey and PwC Are Reshaping Consulting with AI Talent

Paul Allen

19 Dec 2025 • 7 min read

Consulting firms spending billions to hire technologists and retrain staff signal a seismic shift in talent economics. McKinsey, PwC, and Boston Consulting Group have ramped AI and tech hiring to the point where technologists now represent up to 10% of the workforce at firms like Accenture. This isn't a routine talent upgrade—it's a strategic bet on rewiring consulting around AI expertise.

But the real transformation isn’t just hiring engineers; it’s about blending consulting and technical roles to unlock ongoing leverage in multi-year client transformations. “We’re building a tech company inside a consulting firm,” says BCG X leader Sylvain Duranton, revealing a structural repositioning.

Elite consulting is no longer a pure-play advisor business—it’s an AI-powered system with compound returns on skillsets and automation. That’s the leverage that changes the game.

“We want 5Xers—deep in one skill but strong across four,” explains McKinsey partner Alex Singla, highlighting the pivot toward hybridity as the “new consultant.”

Why Traditional Consulting’s Staffing Model Fails AI’s Demands

Conventional wisdom treats consulting as a pyramid with junior analysts doing manual grunt work for partners. Firms hired aggressively during the pandemic to scale advisory manpower, but AI adoption revealed the limits of sheer headcount. Proposals backed by armies of consultants now fall flat without embedded AI tech.

This headcount-heavy model hits a leverage wall when clients want tools, not just slides. Firms like PwC and Deloitte are cutting junior hiring and focusing on upskilling to maximize impact per employee—prioritizing catalytic talent over volume.

Demand for technologists rockets: Accenture added nearly 40,000 AI/data pros in two years; EY onboarded 61,000 technologists since 2023, yet most projects still require hybrid skill sets.

This dynamic exposes a key constraint—pure technical or advisory talent alone can’t drive AI transformation leverage. Firms must unlock hybrid expertise that can bridge strategy, execution, and coding, a structural shift absent in older models.

How Hybrid Consultants Multiply Impact with Forward Deployed Engineering

Boston Consulting Group’s “forward deployed consultants” embody this hybrid model. Inspired by Palantir engineers, they code solutions live on client projects that scale when adopted firm-wide. This turns client engagements into R&D pipelines fueling reusable AI tools—minimizing duplicated effort and human dependency.

McKinsey’s QuantumBlack team of 1,700 AI specialists leads 40% of the firm’s work, but the leverage spreads further through 4,000+ consultants using technical fluency to champion AI from the business side. This creates layered leverage: highly specialized talent incubates tools, while generalists diffuse AI value across clients.

Unlike traditional consulting firms that rely on expensive external tech partners, firms like McKinsey and BCG internalize tech creation. This structural integration compresses feedback loops and accelerates productization of AI assets.

Unlike firms dumping $8-15/install marketing spends to grow products, these consultancies capture leverage by investing years to build proprietary AI capabilities embedded in client workflows.

Further reading: Why AI Actually Forces Workers To Evolve, Not Replace Them

Why Upskilling Unlocks Compounding Returns Across Consulting Workforces

Scarcity of AI experts makes upskilling the lever with the highest ROI. PwC, KPMG, and EY have launched sweeping AI literacy programs and digital badges to build large internal talent bases without constant external hiring.

EY trained nearly 100,000 employees—one quarter of its entire workforce—in AI skills through intensive 15-hour courses on AI engineering, compliance, and applied AI, enabling deep knowledge at scale. KPMG’s global AI leader calls it “literacy and learning before headcount change.”

This approach reshapes constraints from “hire more experts” to “raise the whole team’s technical baseline.” It reduces dependency on scarce AI engineers and amplifies internal adaptability—a foundational lever for sustainable AI-driven consulting growth.

Upskilling transforms workforce leverage from linear to exponential, as every consultant increasingly embeds AI tooling into workflows, amplifying client impact with fewer people.

What Consulting Leaders Must Shift to Capture AI Leverage

The constraint now is talent fluidity—the ability to move seamlessly between consulting and technology. Firms that treat AI as a plug-in feature or bolt-on skill will miss the compounding impact generated by fully hybrid roles.

Geographies with strong engineering talent pools, like the US and Europe, can double down on this hybrid consulting model to leapfrog competitors. Meanwhile, firms focused solely on junior hiring or one-dimensional consulting risk structural obsolescence.

Leverage comes from creating self-reinforcing systems: few engineers codify tools that elevate many consultants, who in turn strengthen client relationships and expand AI adoption. This loop cuts reliance on constant hiring, lowers marginal costs, and accelerates scale.

“AI’s biggest leverage isn’t replacing consultants but amplifying ones who can learn, unlearn, and relearn fast,” says McKinsey’s Kate Smaje.

For organizations looking to enhance their internal capabilities in AI and reap the transformative benefits discussed, tools like Blackbox AI can provide the necessary coding assistance for developers. By incorporating AI into their workflows, consulting firms can effectively bridge the gap between technical and advisory roles, ensuring they stay ahead in the evolving landscape. Learn more about Blackbox AI →

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Frequently Asked Questions

How are McKinsey and PwC changing their consulting workforce with AI talent?

McKinsey and PwC are investing billions to hire technologists and upskill existing staff, blending consulting and technical skills. For example, McKinsey's QuantumBlack team has 1,700 AI specialists leading 40% of its projects, while PwC focuses on AI literacy to create hybrid consultants.

What is a hybrid consultant and why is this model important?

A hybrid consultant combines deep expertise in one area with strong skills across multiple others, like combining coding with consulting. Firms like BCG and McKinsey use this model to embed AI expertise into client projects, increasing leverage and accelerating AI adoption in consulting workforces.

Why is traditional consulting staffing failing to meet AI demands?

The traditional pyramid model with many junior analysts is less effective for AI-driven projects. Firms like PwC and Deloitte are cutting junior hiring and focusing on upskilling to maximize impact since pure headcount cannot deliver AI-based leverage.

How has Accenture responded to the need for AI talent?

Accenture has added nearly 40,000 AI/data professionals in two years, showing the high demand for technologists. This investment supports a workforce where technologists may represent up to 10% of employees.

What role does upskilling play in AI-driven consulting?

Upskilling is critical due to scarcity of AI experts. EY trained about 100,000 employees in AI skills through 15-hour courses, transforming workforce capabilities to embed AI tooling across projects and reduce reliance on external hires.

How do firms like BCG use forward deployed engineering in consulting?

BCG's forward deployed consultants code live AI solutions on client projects, which then scale firm-wide. This reduces duplicated efforts and creates reusable AI tools, turning projects into ongoing R&D pipelines.

What is the biggest talent constraint for consulting firms embracing AI?

Talent fluidity—the ability to seamlessly move between consulting and technology—is the key constraint. Firms must foster hybrid roles rather than treating AI as a bolt-on skill to capture compounding leverage.

How does internalizing tech creation benefit consulting firms like McKinsey and BCG?

Internalizing technology accelerates productization of AI assets and tightens feedback loops, allowing these firms to build proprietary AI capabilities embedded in client workflows, reducing dependency on external tech partners.