It’s becoming increasingly clear to most people that AI is the most important technology of the next decade. If the last technology mega-trend was the digital revolution, the next is the AI revolution. Clearly, this is going to be a huge factor for the City of London and Wall Street alike.
Think of it like this: these financial capitals embraced the quant revolution with great success. Beginning in the late 20th century, this transformative period saw quantitative analysis become increasingly integral to investment strategies. Trillions of pounds were won and lost off the back of smart algorithms operating on numerical data. But, through that entire period, language data was mostly intractable to computers. This was a major limitation, since about 90% of data is textual – everything from emails to documents – or similarly unstructured.
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The second quant revolution
But this has changed. The huge shift, most visibly in ChatGPT, is that large language models have made this language data accessible to computers. This will fuel a second quant revolution, as this language data is now consumed by financial organisations. And beyond language, AI is advancing to include and produce images, video, and code, which can drive efficiency gains everywhere across an organisation from creating marketing content to speeding up the delivery of software delivery.
Front and middle offices will have an entirely new set of signals. Consuming human analysis or other novel text data sources will drive alpha in many different domains. But, unlike the first quant revolution, where back office functions were relatively untouched, they are also going to be transformed this time, with generative AI allowing many processes to become better and cheaper. Customers will be the ultimate beneficiaries as these digital services become more intelligent and responsive to their needs.
So where’s the problem? Well, take a look back at the digital transformation. That was a bumpy road for many organisations, as they wasted large amounts of money trying to learn which technologies and methodologies actually worked. While AI is arguably just better software, there are important differences in the way AI systems need to be designed and built.
3 core lessons
As someone who’s been working in this industry for a decade, here are three core lessons from my successes and failures.
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By GlobalDataFirst, AI solutions must be designed to serve business outcomes. If you treat this as a technology looking for a problem, you’ll waste a lot of time and money. Start by thinking about the critical decisions that you have to make, and think about how a system that lets you look ahead could help you shape the future. This will focus your attention on where AI can meaningfully drive value.
Second, your AI should be human-centric. It should support you, not supplant you. Just like ATMs rejigged roles in branches, AI can remove mundane work and allow people to focus on the strategic elements of their roles. This requires the AI to be built in specific ways, with interpretability, safety and governance built in from the ground up.
Finally, think about the wider transformation as a series of these decision-focused, human-centric AI solutions that are individually valuable, but collectively transformative. It is important to be ambitious because your competitors, big or small, will be. But don’t try to boil the ocean. Pick off individually valuable solutions, but build them in a common architecture that enables them to be connected over time. This has all the benefits of a wider transformation but delivers value along the way.
UK consumers benefit from a successful City of London
Ultimately, this is an exciting time. Enormous quantities of market share will be won or lost on decisions made over the next few years. In the UK, the whole nation’s citizens need the City to succeed. Since the 2008 financial crisis, our GDP has been stagnant, with public services struggling as a result – as seen in overcrowded A&Es and growing queues at food banks. A thriving City is part of the answer to fixing that.
This stands in stark contrast to the US, which thrives off San Francisco tech talent just as much as its New York Stock Exchange. So, strangely, whether organisations can safely and successfully implement AI will actually have great consequences for everyone in the country.
Marc Warner is CEO and co-founder of Faculty
