Q&A: how finance leaders can drive value from the AI advantage

A core value of AI lies in its capacity to free up time by optimising rudimentary tasks. A lot of what the finance function does, such as reconciliation for example, is repeatable and so hugely automatable. 

However, for finance leaders, any meaningful adoption of AI is a long way out because they work in an industry built on trust and credibility. While we do see an appreciation of the impact that AI might have, most finance leaders are still waiting for that big bang moment. There’s a lot of people who are nervous about being the first mover because if AI adoption goes wrong then it may pose a big reputational risk.

For me, at least in the short-term, AI will serve as a supplement rather than a replacement. In the broader context, I foresee much of the regulation surrounding AI emphasising the need for substantial human oversight. 

Take my team, for instance, who could use an AI tool for an intricate job like drafting policies and get a fairly credible response that is about 80% accurate. Human interpretation of that remaining 20% is vital to ensure the answer is not misconstrued. While the ROI from AI might stem from the removal of lower-level tasks, you still need experts who can scrutinise its responses to ensure credibility. AI will likely prove to be a fantastic asset, but I expect it to play that supplementary role for a considerable period.

You need to have a full understanding of what your business does. AI can help to make a good business better. But using AI to fix core issues in a struggling business is where things can get dangerous. If you dive into automation without a clear understanding of your business, the rules you’re trying to implement and what you hope to improve, then you’re likely to end up with results that miss the mark.

You cannot ask AI how to magically transform a business. AI models can provide plenty of broad suggestions, but these will need tailoring to fit your specific needs. The biggest impacts happen when AI is leveraged on top of a solid foundation. You need to know your business inside and out, understand the processes most likely to benefit from automation and have the relevant skills to measure the impact of these changes. That’s how you make AI work for you—not by expecting it to do all the heavy lifting from scratch, but by guiding it to enhance the good work you’re already doing.

Finance leaders should play a crucial role in pushing the business to explore broader applications of automation and AI. Take a simple use case like reconciliation, for instance—with the right data feeds pushing into a system, there’s no need for manual intervention. Why rely on humans to sift through paper bank statements when technology can handle it? 

You can streamline plenty of other, similar tasks across the wider business in the same way. Identifying these obvious use cases can help prompt the executive team to explore automation possibilities across different functions, fostering a continuous learning cycle.

I come from a banking and finance background, and one of the fundamental problems you get used to seeing is a high level of complexity. Yet, many people in these spaces don’t necessarily know these complexities exist until they crash into them, whether it’s processing different currencies and forms of payment, or something more fundamental like deciding how you want customers to pay your business. 

Banks have had a monopoly over the payment industry for a long time and because people have coped with complexity, they think there’s no other option. But there is. It’s these areas of complexity we’re working to simplify with our Equals Moneys products. For example, why receive all your customers payments into one account per currency? Why not issue unique, individual multi currency payment accounts and have perfect reconciliations from scratch? 

By resolving challenges like these, which lead to complexity, Equals Money helps free up time and resources that finance leaders can reinvest into more critical digital change projects.

The concept of AI is a scary thought to a lot of people who think it’ll replace jobs, making them obsolete. Transparency is key in managing this change. As with any large-scale change project, you need to bring people on the journey with you by reframing it as an opportunity to evolve. Finance leaders need to help communicate this shift. Doing so will help to turn what seems like a threat into an opportunity for personal and professional growth. Equally, with the right training or retraining in place, leadership teams can ensure that as many people as possible retain a meaningful role in the evolving landscape.

Finance leaders need to be receptive and understand how to control change, rather than shying away from it. Today’s finance leader needs to treat AI adoption like any other change project they’ve gone through and acknowledge that it’s going to happen and it’s here to stay.

Rather than fearing being a first mover, I would say to leaders: embrace it. The real risk lies in being slow and allowing competitors to pass you by. Get AI adoption right and you gain a competitive edge that enhances decision-making and efficiency. You can’t afford to fall behind because technology is moving so fast.

To thrive, finance leaders must play a central role in understanding and controlling the implementation of AI to minimise risk. Get this right and everyone will prosper.