Artificial intelligence (AI) will undoubtedly be the most transformative technological force for businesses in the coming years. The US technological research and consulting firm Gartner has likened its breakthroughs to the invention of the steam engine, electricity and the internet in terms of its projected impact.¹ Generative AI can learn from existing data to generate new, realistic creations that reflect the characteristics of their source, without replicating it. The revolution is here, and it is data driven.

As AI technology rapidly advances, it will automate complex cognitive tasks and decision-making at an unprecedented rate. We are now at the beginning of the fourth wave of AI – characterised by the intersection of AI with other emerging technologies such as the internet of things (IoT), cloud computing and augmented reality. AI will have a major impact, but exactly how is not yet clearly defined – we are still trying to figure it out.

AI's potential to reshape treasury is unparalleled. Over the past few years, we have seen successful examples of AI usage in finance, e.g. around predictive cash flow forecasting, payment fraud detection through abnormal patterns, and data extraction from trade finance documents. At the same time, many banks and IT vendors are wondering what they can develop in terms of AI.

While it is clear that treasurers will benefit from AI, usage is still in its infancy. In the 2023 EACT survey, we saw that digitalisation and AI are important but not a top priority for corporate treasurers.² It seems that there are many other issues to be fixed before thinking about AI.

“The revolution is here, and it is data driven”

As treasury has entered the era of "everything in real time," the fragmentation and multitude of IT systems complicates treasurers’ lives. Therefore, treasury first needs to focus on the next level of process automation to improve efficiency, get a better grip on the data and strengthen internal controls. Without good quality financial data lakes, an AI solution is not conceivable.

This is why I see hyper-automation as an intermediary step towards broader AI use. Hyper-automation aims to achieve end-to-end automation across various treasury functions, from cash management and liquidity forecasting to compliance and reporting. It focuses on achieving significant operational efficiencies in treasury processes through a holistic approach to automation, until the time AI can go beyond it.

There are existing, encouraging examples of AI usage in treasury. For example, AI enables forecasts and scenarios to be constantly adapted based on compiled and processed data, and the quality of the forecasts improves over time. It also ensures the relevance and accuracy of calculated figures. Speed, automation, reliability and adaptability are AI's strengths. As we are living in volatile times, adaptability is also key for corporate treasurers.

However, these AI applications are not (yet) deeply integrated into treasury processes or may be adopted primarily because they are fashionable, rather than because they provide significant value. We are also aware of the need for strong governance and responsible management of this powerful technology. The treasury function will not become obsolete, but it will have to evolve.

We must be patient and go step-by-step with a roadmap in mind – things never advance as fast as we expect. Cars were predicted to fly by the year 2000, but that wasn't the case. Nevertheless, whatever our level of exposure to, and interest in, AI solutions, this technology is going nowhere but upwards.

It is an exciting time, as we are now more empowered than ever to forecast, strategise and execute with confidence. As we forge ahead, let us leverage the full spectrum of possibilities predictive analytics offers, ensuring a resilient and robust financial framework for businesses around the globe. The machine will never replace man (fortunately), but man's job will change along with the machine.

Sources

¹ See gartner.com
² See eact.eu