KPMG Executive Says AI Has Made Classic Consulting Advice 'Nonexistent'

KPMG's global head of consulting, Svilena Tzekova, has declared that a core revenue stream of the consulting industry — selling advice — has been erased by artificial intelligence. "We were making quite a lot of money by providing advice. I think that part of our business is literally nonexistent anymore," Tzekova, who is based in London, told CNBC.

The statement cuts to the foundation of a global industry that has historically charged clients for intellectual capital delivered by armies of analysts working billable hours. Tzekova's assessment is blunt about why: the knowledge that once justified premium fees has become widely accessible, and clients know it.

"Anyone can go and very quickly get to the level of advice that we used to deliver within whatever days or months at times," Tzekova said.

Her remarks signal that the disruption AI has brought to software development, customer service and content production has now reached the professional services sector at its most fundamental level — the fees firms charge and the graduate labor model that has powered their economics for decades.

How do consultancies charge for AI-era work?

The industry has not solved the pricing problem that AI has created. Tzekova described a structural mismatch: clients say they want to pay for outcomes, but their procurement departments still compare consultancies on headcount and hourly rates — the number of people assigned to a project and what those people cost per hour.

AI has also added cost lines that traditional pricing models never contained. Firms must now factor in computing power and the fees that AI providers charge based on how much text their models process, alongside the traditional cost of employing people.

"It's very difficult to be able to arrive at a commercially sensible outcome-based pricing model," Tzekova said. Firms are still working out the equation, she said, and that will take time.

The stakes are considerable. The Big Four — Deloitte, PwC, EY and KPMG — built their consulting businesses on time-and-materials billing, where revenue scales with the hours staff bill. If AI compresses the hours required for a given piece of work, revenue falls unless firms can reprice around outcomes. No one has yet found a formula that works at scale.

Can AI-native challengers outmaneuver the Big Four?

Smaller, AI-native consultancies are positioning themselves as the structural winners of this transition. Emiko Caerlewy-Smith, head of investor relations at London-based challenger consultancy Elixirr, argued that firms like hers start from a better place because they have never billed clients on a time-and-materials basis.

"AI is amplifying and exaggerating the way that we were already trying to show up to the market," Caerlewy-Smith told CNBC. "Clients will pay for fixed deliverables, fixed outcomes and value. They really shouldn't be caring about the amount of time it takes to create the inputs to create the outcome."

The nature of client demand has shifted as well, she said. Clients now want consultancies to build solutions, not just advise on what to do. In one recent project, Elixirr helped a global insurer build an AI-based motor insurance platform.

Speed of internal AI adoption is a second advantage the challengers claim. Elixirr took six months to get its data into a format that its AI models could fully use. Caerlewy-Smith questioned how long the same process would take at a firm employing tens of thousands of people.

"The issue they have is that they are enormous and we are nimble," she said.

The challenge for the incumbents is that they must modernize while carrying the cost base and organizational inertia of global firms. The Big Four are racing to retool their operations, but their scale — historically their greatest asset in winning multinational contracts — is now the friction point in adopting AI internally.

What happens to graduate hiring?

The second pillar under pressure is the graduate recruitment pipeline. Consulting firms traditionally hire thousands of university graduates each year and set them on a career ladder whose entry rung consists of exactly the tasks AI can now perform: research, data analysis and producing presentations.

Claire Trachet, a former Deutsche Bank mergers and acquisitions banker who founded the London-based advisory firm Trachet, told CNBC that AI helps junior employees work faster. But she raised a problem the industry has not answered: how junior staff gain the experience needed to progress if they no longer do the foundational work themselves.

Some junior professionals are already questioning why they should put in long hours on tasks AI can complete with a click of a button, she said.

"The problem is you can't form your judgment, your intuition," Trachet said. "Your professional sound judgment is built through reputation. It's a craft. The ones who will stand out later will be the ones who own that for themselves."

Her point goes to the apprenticeship structure of professional services. Senior partners formed their judgment by doing junior work for years. If AI removes that work, the industry must find a new mechanism for building expertise — or accept a generation of professionals trained differently, and perhaps less deeply.

How are EY and KPMG redesigning junior roles?

The large firms are not standing still. Paul Goodhew, EY's London-based global assurance innovation and digital leader, said AI is already changing the work traditionally done by junior auditors, such as checking figures against source documents.

"The traditional view of the auditor was someone, particularly a junior auditor coming into the profession, who would be spending a lot of their time doing manual 'ticking and tying' at work," Goodhew told CNBC, referring to the process of verifying figures.

"Now today, AI is bringing about the potential that AI can actually do the first preparation of that work on behalf of the audit professional."

EY is responding by creating new roles rather than simply cutting junior positions. Goodhew said the firm is developing data delivery professionals who prepare data for audit teams, product managers who help develop technology platforms, and specialists who understand clients' technology strategies and risks.

KPMG's Tzekova said the answer is not to abandon graduate recruitment altogether. Junior consultants will need to take on more responsibility much earlier in their careers as the traditional shape of consulting firms evolves. She added that some of KPMG's graduates and junior employees are already among the best and brightest at driving the firm's new way of working — an implicit acknowledgment that the youngest staff, not the partners, are often the most fluent with the tools forcing the change.

Is the pyramid business model still viable?

The economics underneath all of this is the consulting pyramid: a broad base of low-cost junior staff doing the groundwork that supports a narrow tier of senior partners selling and overseeing engagements. AI attacks the base of that pyramid directly.

Caerlewy-Smith said traditional firms must redeploy their employees to other tasks quickly — not as a courtesy, but as a matter of survival for the model itself.

"We just need them to do other things, otherwise the economics of the pyramid models don't work anymore," she said.

The industry now faces three simultaneous transitions: repricing work that clients no longer accept as billable hours, absorbing new AI compute and token-processing costs into contracts, and rebuilding an entry-level career path whose tasks have been automated. Tzekova's assessment — that the advice-selling business is "literally nonexistent" — sets the deadline for all three.