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Within weeks of Donald Trump’s “liberation day” in April and the ensuing widespread panic after the sweeping escalation of the US president’s trade policy, KPMG had a tariff calculation model ready that it says saved its clients “hundreds of millions of dollars”.

The Big Four firm was “first to market” with the model, according to Stephen Chase, KPMG’s global head of AI and digital innovation, a feat he says would have been impossible without sustained investment in AI technology for over a decade. 

Being first to advise has become the new competitive edge for accounting and consulting firms as they push out AI across their businesses, particularly for large legacy firms competing with nimble AI-native start-ups. From trawling mass data sets during audits to find high-risk transactions to drafting consulting documents in minutes, the technology is expected to reorganise how professional services firms work.  

Soon after, Chase adds, KPMG snatched an audit bid from the jaws of a rival firm by showing off its AI capabilities to the client. “They were going to go with one of our competitors,” he says.

The rival firm had planned to send the customary “army of people” to manage the major task of transitioning the audit, but KPMG showed the client their AI audit platform, Clara, which the firm says can consume the same volume of information faster, with fewer people. “They went from sceptical to KPMG client,” he adds.

For smaller accounting firms, AI is proving just as helpful. Nearly half of UK firms with turnover up to £500mn reported at least a small rise in productivity from using AI, equivalent to reclaiming almost half a 40-hour week, a study by Xero and the Centre for Economics and Business Research found.

About half of finance and accounting professionals now use AI every day, compared with 33 per cent last year, according to a study by the Wharton School of the University of Pennsylvania published in October.

“The efficiency gains are real, though uneven — as you’d expect with any new technology,” says Dhiren Rawal, managing director and head of global shared services at consultancy Alvarez & Marsal. “In practice, this is what AI adoption looks like: small, practical changes that add up to a genuine shift in how people work. The technology is easy to acquire; the differentiation comes from how well it’s applied.”

Consultants are already using AI to test new scenarios, extract figures and draft complex materials in “minutes rather than hours”, Rawal says. “In transaction advisory, AI tools surface patterns in financial and operational data that would have taken days to uncover manually. In restructuring, they can classify and compare thousands of contracts within hours.”

He added that the biggest impact will come from depth, not scale. “Most firms, including ours, are learning that the hard part isn’t adopting AI, it’s integrating it safely and intelligently into complex workflows. That means tightening data foundations, building clear lines of accountability, and ensuring people understand both the strengths and limits of the tools they use.”

At KPMG, experiments on contained work groups show that AI productivity gains so far tend to range between 10 and 15 per cent, and up to 80 per cent for specific tasks, says Chase. 

But for global firms — which mostly operate through separate partnerships operating under a shared brand — the pace of AI adoption varies by country.

“The honest truth is we have member firms . . . where we have an adoption rate of 100 per cent . . . and we have countries where the adoption rate is . . . maybe 70 per cent,” says Christian Stender, global head of AI for tax and legal at KPMG International. “There are differences, maybe also from a cultural perspective.”

Partners tend to use AI less than employees, Chase adds. “I always give my partners a hard time, like ‘you got to lead from the front’,” but “they do different work, so you would expect the usage patterns of an associate to be different than they would be for a partner.”

Aiming for productivity gains alone will not move the needle, says Jonathan Keane, strategy and consulting lead at Accenture for UK, Ireland and Africa. “Gains only come when companies use AI to redesign processes and ultimately rethink whole business domains. That’s where the step-change in efficiency and growth will come from. To get there, the foundations must be right — clean, well-governed data; secure and interoperable systems; and people who understand how to work alongside AI.”

For KPMG’s Chase, the debate has already moved on from productivity. “I think we’ve long since passed the question of are we getting productivity, right? One of the questions everybody wants to know is, are we getting ROI [return on investment] out of it?”

He adds: “We’re all feeling tremendous pressure for ROI delivery . . . This is the year of ROI.”

Companies’ rapid embrace of artificial intelligence tools to write software is driving demand for systems to ensure the code these tools produce is not riddled with bugs and security flaws.

Start-up Antithesis on Wednesday announced a $105mn funding round led by trading firm Jane Street, the latest in a series of software testing and security groups to raise capital this year.

Will Wilson, Antithesis’ co-founder and chief executive, said “everybody adopting AI coding tools . . . will produce a huge volume of software . . . and put current approaches to software testing and software validation under enormous strain”.

Jane Street’s investment highlights how users of “vibe coding” tools — which write software with little to no human oversight — are increasingly concerned about costly errors that could be lurking in the AI-generated code.

“It’s not a coincidence that Jane Street is a massive user of AI coding,” Wilson added.

The quantitative trading firm uses custom software to make lucrative bets across financial markets. It is among Antithesis’ largest customers and requested to lead the round, according to the companies.

Jane Street also holds stakes in AI groups Anthropic and Thinking Machines Lab. Doug Patti, a software engineer at the firm, said Antithesis’ technology “has helped [Jane Street] uncover issues that no other testing method could find”.

Tech groups from Microsoft, Google and Nvidia to Coinbase and Klarna have led adoption of AI tools such as Anthropic’s Claude Code, Anysphere’s Cursor and Lovable over the past year, claiming they have supercharged their developers’ productivity.

A recent Gartner survey of software engineers found that almost two-thirds of organisations were using AI coding assistants in some form.

AI coding start-ups have raised billions of dollars as investors back them as a leading practical application of AI for businesses. Anysphere’s valuation has shot up from $2.5bn at the start of 2025 to $29bn last month, making it one of the fastest-growing start-ups of all time.

Start-ups such as Antithesis, which promise to vet this AI-generated software, are now attracting investment too.

In May, New York-based OX Security raised $60mn from investors including Microsoft and IBM Ventures to scale up its “VibeSec” security testing system. Palo Alto-based Endor Labs raised $93mn in April after releasing a tool used by companies — including OpenAI — to test for bugs and suggest or make fixes.

Antithesis tests software by creating a simulation of a company’s IT system and exposing the new code to automated user behaviour that would take months to test in the real world.

Gartner predicts that the US application security testing market will grow to $5.1bn this year, up from $3.4bn in 2023, as some studies show code written by AI systems is prone to errors.

“The problems with vibe coding stem from the sheer volume of code they produce,” said Michael Fertik, an early investor in Anysphere who also runs Modelcode.ai, a start-up that uses generative AI to rewrite ageing applications. “AI produces 10,000 times more code than any given human per year, so the risks are amplified and multiplied.”

That challenge is greater when AI coding is used in the labyrinthine IT systems upon which many large companies rely, where small changes can trigger an unforeseen cascade of problems.

Testing of dozens of advanced AI models across 80 coding tasks by Veracode, which makes application security tools, found that almost half of AI-generated software contained security flaws.

Another study published in the journal Empirical Software Engineering in December 2024 found vulnerabilities in at least half of programmes generated by the AI models from OpenAI, Google and Meta available at that time.

“While [large language models] can be useful for automating simple tasks . . . directly including such codes in production software without oversight from experienced software engineers is irresponsible and should be avoided,” researchers wrote in the paper.

AI programming systems continue to improve, but even early advocates have begun to sound the alarm over the reliability of these systems.

Andrej Karpathy, the influential former OpenAI and Tesla AI researcher who coined the term “vibe coding” in February, said he believed these AI-based systems were underdelivering on their creators’ promises.

“I kind of feel like the industry is making too big of a jump and is trying to pretend like this is amazing and it’s not. It’s slop,” Karpathy said, using a pejorative term for unhelpful or low-value AI-generated material.

“We’re at this intermediate stage,” he told the Dwarkesh Podcast in October. “The models are amazing [but] they still need a lot of work.”

Antithesis’ Wilson compared the AI coding boom to the offshore outsourcing trend of the 1990s and early 2000s, when early enthusiasm for shifting software development to locations with lower salaries was undermined by “the cost of telling whether the software that you received does exactly what you wanted it to do.”

Josh Albrecht, co-founder of Imbue, which offers a tool that coordinates coding assistants, said that if used correctly AI systems could “write much more robust code than in the past”.

“The problem comes where someone doing this doesn’t really understand software engineering. That’s where you get the security vulnerabilities,” he said.

AI coding providers are starting to tackle the problem themselves. Anthropic in August added automated security reviews to Claude Code.

Some in the industry see much of the revenue from securing AI-generated software ultimately flowing back to companies such as Anthropic, OpenAI and Google that develop the foundation models upon which vibe-coding tools are built.

“Vulnerabilities are happening faster thanks to AI, and we are selling software to squash those vulnerabilities faster thanks to AI,” said Dipto Chakravarty, chief product and technology officer at Black Duck, an application security company.

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HSBC has appointed the former KPMG partner Brendan Nelson as its new chair after a chaotic search process that left the job vacant for several weeks.

Nelson, who has been serving as interim chair since the start of October, is a surprise choice for a position that requires the diplomatic skill to bridge China and the west as well as deep banking experience.

HSBC’s failure to secure a high-profile chair will raise questions about the effectiveness of the board at one of London’s largest listed companies.

Nelson — who is 76 years old and has spent most of his career in the UK — may be seen as a temporary appointment until the bank can secure another candidate.

Speaking at the Financial Times Global Banking Summit on Tuesday, before the announcement was made, chief executive Georges Elhedery said Nelson did not want to serve a full term of six to nine years.

The former accountant would stay in the role “for as long as it takes until the board and the nomination committee identify the right chair”, Elhedery said.

HSBC said the decision was made after a “robust process” that looked at both internal and external candidates.

Among those in the mix, analysts said, had been former chancellor of the exchequer George Osborne and Goldman Sachs Asia boss Kevin Sneader.

“Since assuming the role of interim group chair, Brendan has demonstrated his excellent leadership capabilities backed by his strong banking and governance credentials,” said Ann Godbehere, the senior independent director who led the recruitment process.

Nelson spent 25 years at KPMG, rising to become the head of its global financial services practice, and has previously served as a non-executive director at BP and NatWest. He joined HSBC’s board in September 2023.

He replaces Sir Mark Tucker, who stepped down in September this year to become chair of insurer AIA.