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Not long ago, people assumed that repetitive, blue-collar jobs would be the first to be disrupted by advancing artificial intelligence. Since the arrival of generative AI, it looks like white-collar jobs will be impacted first. Jobs like accounting, management consulting, and the law. Who would have guessed that lawyers would find themselves at the cutting edge of technology.
Shamus Rae is the co-founder of Engine B, a start-up which aims to expedite the digitisation of the professional services industry. It is supported by the Institute of Chartered Accountants in England and Wales (the ICAEW) and the main audit firms. Shamus joined the London Futurists Podcast to discuss how AI will impact professional services in the next few years.
Shamus was ideally placed to launch Engine B, having spent 13 years as a partner at the audit firm KPMG, where he was Head of Innovation and Digital Disruption. But his background is in technology, not accounting. Back in the 1990s he founded and sold a technology-oriented outsourcing business, and then built a 17,000-strong outsourcing business for IBM in India from scratch.
Data
The top priority for Engine B is data. Shamus argues that unless an organisation’s data is up-to-date, accurate, and held in standardised formats, you can’t do anything useful to it with advanced AI. So Engine B spends a lot of its time obtaining the right data from clients, and making it comply with those standards. Getting the plumbing right, Shamus says.
Most of this data is used by the kind of the kind of pattern recognition deep learning models that were introduced by the 2012 Big Bang in AI. But Engine B is also starting to use the generative AI that were introduced by the 2017 Big Bang, and is building co-pilots for its smaller clients – the larger firms are building their own.
The audit firms used to think there was competitive advantage in their data models, and their individual approaches to handling client data, but when the ICAEW reviewed the approaches, they found they were all pretty much the same. This shouldn’t be surprising: data science is not a core skill for accountants, and nor should it be.
This does not mean that data is not important and confidential. Engine B is religious about never looking at the content of client data, and never copying it, anonymising it, or storing it.
Data swamps and data lakes
Most of the data held by most companies is in a bad state, and has to be cleaned up and regularised before it can be used. To coin a phrase, data swamps have to be transformed into data lakes. For example, a large company will lease many buildings; each of these leases is likely to have evolved over time, and it may not be immediately obvious which lease is the current and applicable one. You can find this out by correlating information from payment records, and this can be done automatically, without a human nosing around in the data.
There are about 300 accounting systems used by large companies around the world, and most of them can be tailored to particular client requirements. In addition, some clients – like Tesla – actually write their own accounting systems rather than using the industry standards like SAP and Oracle. So the variety of accounting systems that Engine B has to tap into is enormous. Nevertheless, it claims to be able to start extracting useful data from almost any accounting system within an hour.
Engine B currently has paying clients in the US and the UK, and works on the audits for 50,000 of the companies that its clients work for. Its clients are global firms, and they will shortly be rolling out the service elsewhere in Europe and the rest of the world. It expects to be working on around 200,000 audits in a year’s time, so growth is fast. Shamus says the company is more advanced in the accounting sector than the legal sector, but that the arrival of generative AI is changing the balance.
Training future partners
When the prospect is raised of AI automating the simpler, more repetitive tasks in auditing, the question is always asked: how will young accountants get trained? Shamus replies that the skills acquired during years of “ticking and bashing” can be acquired less painfully and more quickly. In future, accountants might think that their predecessors who had to endure that process were put through it as a sort of therapy for those who went through it before them.
A comparable process for lawyers was to wade through thousands of legal documents in a “deal room” during the review of a transaction like an investment or an acquisition. Much of this “disclosure” work has now been automated, with no apparent loss of expertise within the legal profession.
New business models
But the automation of ticking and bashing does give the audit firms a problem, as it undermines their funding model, in which clients are charged a significant sum for a mass of juniors to carry out grunt work, and partners earn a share of this income to add to the larger fees they charge for their own more limited time.
Shamus thinks the professional services firms will have to abandon their current triangle-shaped organigrams, with a lot of junior people at the bottom, a smaller number of managers in the middle, and a very small number of partners at the top. They will have to adopt a diamond-shaped organigram, because most of the junior jobs will have been automated.
Lawyers and accountants will also have to learn how to sell more than just billable hours. They will have to sell the value of the AI systems which are replacing much of the work previously done by junior humans.
In Shamus’ experience, senior people in professional services do appreciate that GPT technology means their industries are about to experience dramatic change. But there is still a level of denial: people often think that everyone else’s job will change, but not theirs.
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