Is AI the end of billable hours?

For decades, the consulting industry has made its money helping other companies through change. Now it's the industry's own turn.

Artificial intelligence isn't just another tool in the consultant's toolbox — it's a force that will change which tasks are even worth selling, who can solve them, and what clients will be willing to pay for.

That can sound like a bleak outlook for an industry built on hourly rates and specialist knowledge. I don't believe it is. But it does require that we, as an industry, dare to face this development now — not in three years, once it has really hit the bottom line.

Four macro trends will shape the industry

I believe the following four trends will be decisive for the future of consulting firms:

1. Volume and capacity work that doesn't require highly specialized skills is declining

The numbers are already starting to show it: according to job-posting data from the analytics firm Revelio Labs, overall talent demand at leading consulting firms is roughly 20 percent lower than at the 2023 peak, and demand for classic consulting roles specifically is down around 40 percent.

At the same time, the pattern isn't uniform: demand for senior consultants has risen 55 percent since 2020, while demand for junior consultants has fallen 10 percent since the 2023 peak — and in 2025, the number of AI-related roles overtook the number of junior consultant roles at major firms for the first time.

Harvard Business Review describes concrete examples of the same shift: the 50-person consulting firm DeciBio has cut its annual graduate intake from 15 in 2021 to a planned 4, despite double-digit revenue growth, and global law firms are considering cutting their intake of “summer associates” from 100 to 30. Anthropic's CEO has warned that AI could eliminate up to 50 percent of entry-level white-collar jobs within five years.

Bloomberg describes how even the three largest consulting firms are already adjusting: McKinsey now lets candidates use the firm's internal AI tool “Lilli” during job interviews, Bain is introducing its own AI tool for the same purpose, and at Boston Consulting Group — according to global people chair Alicia Pittman — the firm is more cautious about using AI in junior work than expected, precisely out of concern for quality control.

2. Demand for certain skill areas is falling sharply

Certain skill areas are set to be hollowed out in much the same way the typesetting profession effectively disappeared around the turn of the millennium. Back then, desktop publishing meant that almost anyone could do work that had previously required a specialist. We're now seeing the same pattern emerge in analysis, research, and standardized advisory work.

Within project management, for example, there are many capabilities that amount to classic controlling tasks — planning, tracking, reporting — and these are shrinking markedly because AI can take them over. In turn, more weight falls on what can't be automated: the leadership discipline itself (Project Leadership) — the ability to create direction, engagement, and decision-making momentum around a project.

3. New types of work are becoming dominant, and AI skills are decisive to them

McKinsey has estimated that generative AI alone could add up to $4.4 trillion annually to the global economy, with the greatest impact precisely within the knowledge-intensive activities that the consulting industry lives on.

4. Getting ahead of it improves the odds of survival

By the time clients discover AI's strengths on their own, it's too late for the consulting firm to adapt — because adapting takes time.

Siemens' electronics factory in Amberg is a good example of how far a client can get entirely on its own: after decades of investment in digital-twin technology and AI-based quality control, the factory today runs at a 99.9990 percent quality rate. Clients who, like Siemens, systematically build this kind of capability in-house end up needing significantly less external help with production optimization and quality assurance over time.

What's driving this?

Two underlying mechanisms, in my view, explain why this is moving so fast.

AI is taking over the execution itself. Activities with a large “best practice” component — things you'd find written up in a textbook, or “bread and butter” work with a low degree of specialization — are especially exposed.

We already see this in IT: software development, from prototyping through coding to testing, can increasingly be carried out by AI agents. But the pattern isn't unique to IT. In process optimization and quality assurance, for instance, AI will increasingly be able to support the design of production lines, quality control, and ongoing optimization. And in data mining, AI can already interpret large volumes of data and identify trends faster than a team of analysts.

Workflows can be handled by fewer roles. The task owner — the person with the problem — increasingly has less need to go through a chain of specialists (subject-matter experts), because AI itself can deliver much of the work. That's extremely attractive to the problem owner, because the iterations needed to get it right become significantly faster.

Looking ahead: how consulting firms can act proactively

The optimistic message is that disruption is also an opportunity — for the consulting firms that act now.

Rethink the pricing model. Harvard Business Review has another article on why new technologies fail to transform incumbents, built around the concepts of “unit of work” and “bottleneck”: Adobe kept treating the file as the fundamental unit of work, while challenger Figma instead made individual design elements the fundamental unit — removing the bottleneck that had limited how fast and flexibly the work could be organized.

Another Harvard Business Review article makes exactly the same point explicit about hiring in consulting: “AI starts completing much of the grunt work performed by associates, resulting in fewer billable hours. One obvious solution is to charge a fixed fee for projects based on the value delivered or what a less tech-proficient competitor would charge, not on how many hours it takes to complete the work.”

It's an uncomfortable conversation for an industry that has built its economics around hourly billing — but it's exactly the kind of conversation that separates the firms genuinely reinventing themselves from those simply using AI to do the same work a little faster.

Build AI into every discipline. Some areas will feel the impact sooner than others, but virtually every field will be affected. There are no “safe harbors” left.

Make AI part of the core offering — not a side project. My thesis is that most AI experts today come out of the IT world and tend to use AI to improve the IT service itself (“augmentation” — a developer uses AI to become a better developer).

An AI expert who comes from a specific domain instead — process optimization or quality management, say — stands much closer to the real value creation (benefit realization). That person can work both faster, because the iteration loop is shorter, and more cost- effectively, because they cover several roles at once.

Bring clients along on the AI journey. Become an active partner in the transformation rather than a bystander to it. There's a deeper reason why companies struggle to disrupt themselves from within.

This is backed up by yet another Harvard Business Review article, from February 2026, on how established companies typically use AI to make existing work marginally more efficient rather than rethinking how the work is organized — and how the resistance leaders often read as cultural inertia is, in reality, what the article calls “architectural self-preservation”: changes to the fundamental structure of work redistribute influence away from the people who currently control approvals and processes.

Boston Consulting Group's 2025 analysis puts numbers on the pattern: only 5 percent of companies worldwide achieve genuine renewal through AI — with five times the revenue growth of everyone else — while 60 percent see essentially no measurable value from the investment despite significant spending. This isn't fundamentally a new observation — Clayton Christensen's “innovator's dilemma” described the same mechanism long before AI: an organization naturally filters out initiatives that challenge its own core.

This is where I think an external partner has a genuine advantage: we aren't bound by the same internal power structures and can therefore point to — and push for — the changes an organization itself struggles to see or accept. Concretely, consulting firms should offer two tracks: being a partner in clients' disruption and being a partner in their augmentation.

This will typically require retraining consulting firms' own experienced staff, so they can apply AI effectively within their field and operate in a world where AI handles the formal part of the work, and experience becomes the decisive factor in creating real value. Consulting firms that actively take ownership of their clients' journey build trust and become part of the solution and the future — rather than being remembered as a representative of the era before AI.

Closing considerations

The consulting industry is facing its own disruption. It's uncomfortable to say out loud, but it's healthier to acknowledge it now than to discover it once clients have already moved on. In reality, this piece's focus on ‘billable hours’ is only one of several areas where the industry needs to reinvent itself.

That said, I genuinely believe that the firms willing to invest in AI capabilities rooted in their core expertise — and willing to go first alongside their clients — will come out stronger than ever.

Sources

1. Revelio Labs – declining demand for consulting roles: https://www.reveliolabs.com/news/business/the- industry-that-advised-disruption-is-being-disrupted/
2. Harvard Business Review – how AI is changing consulting firms' hiring (Tarki & Raczynski, 2025): https://hbr.org/2025/10/how-ai-is-upending-how-consulting-firms-hire-talent
3. Bloomberg Businessweek – how McKinsey, BCG and Bain are hiring now (Boyle, 2026): https://www.bloomberg.com/news/articles/2026-04-15/ai-influences-how-mckinsey-bcg-bain-hire-for- entry-level-consulting-jobs
4. McKinsey – the economic potential of generative AI: https://www.mckinsey.com/capabilities/mckinsey- digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
5. Siemens – the Amberg factory's digital twin and quality rate: https://www.siemens.com/en- us/company/insights/electronics-digital-enterprise-future-technologies/
6. Harvard Business Review – why new technologies fail to transform incumbents (Choudary, 2026): https://hbr.org/2026/02/why-new-technologies-dont-transform-incumbents
7. BCG – The Widening AI Value Gap (2025): https://www.bcg.com/publications/2025/are-you-generating- value-from-ai-the-widening-gap
8. Christensen Institute – The Innovator's Dilemma (Christensen, 1997): https://www.christenseninstitute.org/book/the-innovators-dilemma/