When I first started work, there were no signs of computers in the office. Then, the first standalone PCs appeared with their large floppy drives and very limited programming ability; principally, we used Lotus, a forerunner of Excel. Not many people knew what to do with it, but it helped do straightforward calculations like adjusting margins on loans to achieve a return on capital. The takeoff, if I can call it that, of computing was relatively slow, and most people were sceptical.
A big change occurred around the same time as the 1987 stock market collapse. I remember we started to use Lotus or Excel in pricing transactions, particularly repos and swaps. The 1987 collapse was also my first real experience of seeing people made redundant in dealing rooms. It was ruthless, and those made redundant were just given a bin liner and told to empty their desks and vacate the office. It put the fear of God into quite a lot of my colleagues, particularly those with mortgages and families. Suddenly, the lucrative business of trading in the City became a lot more precarious.
Against this backdrop, the derivatives markets started to open-up, with more and more people trading in swaps and options. Here, Excel spreadsheets transformed dealers' fortunes; if you had a reasonable understanding of numbers and markets, you could find opportunities to make almost risk-free money. It seemed like the gravy train was on the rails again, but very soon this vision became less realistic as margins were competed away. Two different types of traders emerged: those who used PCs and those who didn’t.
Why do I relate to this story?
I think certain parallels are happening today with artificial intelligence. It's not dissimilar to our use of spreadsheets all those years ago. If you work in treasury and risk, you must now be wondering how your job will be impacted by the new technology, and you may also worry about your future, particularly as some people suggest that much of what you do will be done by artificial intelligence at some point in the not-too-distant future.
Let's consider for a moment the sorts of things you do on a day-to-day basis in Treasury. You're involved in things like liquidity and cash flow management, funding and investment strategies, interest rate risk modelling and monitoring, foreign exchange exposure management and dealing regulatory compliance and oversight. You may also be involved in identifying and assessing risks, developing policy, managing and monitoring limits, stress testing and scenario analysis, and reporting on all of this to your management.
What this involves is a solid foundation in finance and an understanding of the business, in order not only to look at things in detail but also to get the big picture. Increasingly, you're expected to be tech-savvy, and that's about working with complex financial software and tools for data analysis and forecasting.
So, where are we today with AI?
Here I'm going to draw on a survey called the 2024 Work Trends Index Annual Report. Some of the key aspects of this report show that AI adoption in the workplace is accelerating very quickly. Some 75% of global knowledge workers now use AI, and 46% of AI users started using it less than six months ago. Employees are generally enthusiastic, with 90% of AI users saying it saves time, and 79% of leaders agree that AI adoption is necessary to stay competitive. However, some 60% of them worry that their organisation lacks an implementation plan.
In short, AI is transforming roles rather than replacing them, leading to a notable change in skills in the next five years, and new AI-specific roles are starting to emerge in companies.
How could things change for us?
Let's just assume for the time being that AI continues to progress as it has done over the past 18 months, but there is no significant breakthrough, it just gets better. Here are a few things we could point to in terms of where AI would become more involved.
Liquidity and cash flow management become more precise, possibly with AI providing some real-time, accurate forecasting and suggesting optimal cash positions. AI starts to help you understand market data and recommend funding and investment strategies. Interest rate risk monitoring becomes something more dynamic, with AI helping you assess potential impacts and suggesting some hedging strategies. Regulatory compliance becomes more streamlined in the normal workflow of updating policy documents. Risk identification becomes something that is looked at more comprehensively, with AI now processing more data and spotting potential risks that may have been harder for us as humans to analyse. Stress testing and scenario analysis become more sophisticated and less set in stone, with more frequent help from AI to run complex simulations. Risk reporting is enhanced by data visualisation from AI, which now allows complex risk profiles to be more understandable to stakeholders across the business.
AI becomes a very helpful assistant to those of us working in treasury and risk. We spend less time on data gathering and routine analysis, and more on how the business works and the strategic thinking around it.
The skills required will need to evolve. While a strong background in finance will remain important and understanding the business will be crucial, professionals in treasury and risk will need to become more adept at working alongside AI tools. Think in terms of understanding the basics of programming and prompting to oversee and interpret information that is generated using AI. It's not just about using the AI itself, but looking at what AI generates and turning that into something that is an actionable business strategy.
What happens further out?
I'm just going to play out a couple of scenarios here that are relevant. Let's suppose that there is a successful transition to AI agents—agentic AI. This is where AI can take over a lot of your day-to-day routine tasks. What would this mean for the treasury or risk department?
Liquidity management becomes largely automated, with AI systems moving funds between accounts and optimising cash positions across instruments. AI agents manage investment portfolios, making real-time decisions based on market conditions and the company's strategy. Interest rate risk is managed using predictive AI models that automatically adjust hedging strategies. Foreign exchange management becomes proactive, with AI systems executing trades to minimise exposure based on your risk appetite. Regulatory compliance is continually monitored and managed by AI, which adapts to new regulations as they are introduced. Risk identification becomes predictive; AI systems don't just identify the risks that we currently have but help us understand future ones based on trends and data.
In this environment, people working in treasury and risk will evolve into what we could loosely call AI-human collaboration managers. Their role will be more about setting the overall strategy, defining the ethical boundaries that AI can work within, and handling the complex, nuanced decisions that require human judgment.
The next scenario is a paradigm shift.
It is where there is artificial general intelligence (AGI) that can match or exceed human capabilities across all domains that we operate in. In an AGI world, the traditional tasks of treasury and risk management will be entirely handled by AI systems. AGI would manage all aspects of liquidity, funding, investment, and decision-making to optimise financial outcomes while balancing multiple complex factors. Risk will be managed across the board, with AGI systems continually monitoring, predicting, and mitigating all forms of financial and operational risk. Regulatory compliance would be automatic and proactive; AGI would not only follow current regulations but would start to anticipate and prepare for future regulatory changes.
In this scenario, roles are entirely altered. Just about everything that we currently do will be done by AI. So what would we do?
Our input would be left to things that are unique to human beings. That means we would set the overall vision for the company and its financial future, aligned with broader goals in society. Ethics becomes more important because it's all about human value judgements. We may provide some sort of human touch in stakeholder relations—maybe we'll explain strategies to shareholders, regulators, and the public on a face-to-face basis. We will have the role of making sure that AGI aligns itself with our own purpose and values, and maybe we'll collaborate with other people, like policymakers, to shape the regulatory environment in the future.
Far-fetched? Maybe, but rapid change is inevitable and so the future becomes even more uncertain. It's what we felt back in the 1980s with the advent of PCs. We couldn't see around the next corner; we just had to meet it as it came up. Those that survived and flourished took hold of the new technology and used it creatively. In the scenarios I've painted, the idea that we're going to have to work closely with AI is almost a given. I just can't see how you can ignore it as it becomes more powerful. This means learning about what it can do, taking every bit of your workflow and testing it out regularly in AI to see what can be achieved. This will make you a more valuable employee and potentially set you up well for the future.
With agentic AI a lot of the boring stuff can be removed from our day-to-day workload, and this presents both opportunities and challenges. It allows us to be more efficient; we can be more data-driven, and we can use our uniquely human skills—those of strategic thinking, ethical reasoning, and interpersonal communication—to develop and run our business. This reskilling may not lead to a huge cut in jobs; it might just be that we can do a lot more with the people who currently work in the firm.
The big question is: what if AGI is achieved?
It's safe to say all bets are off. Very few people will be required to run your traditional treasury and risk management area. The jobs that remain will be much more in a senior oversight and governance capacity. I don't think this is unique to treasury and risk; this is something that would happen to many white-collar jobs.
You may be sceptical, and who knows what the future may bring, but it is certainly a scenario that is worth considering.
I don't think there's anything that you can do at this point that will fundamentally alter your future; you must get on in the current environment with what you're given and use your skills and natural curiosity to improve your standing in the workplace. This is exactly what we did all those years ago. Some people moved out of the business completely, whilst others moved in.
The mistake would be that you think your current skills are good enough to see you through the next decade. This would have worked in the past but not now.
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