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There’s no doubt that AI adoption is here and possibly more widespread than you would have thought.
Research from Equals Money of UK financial decision makers found that over three quarters (77%) are already actively adopting or experimenting with AI in their processes.
So, what role can AI play in your business finances? 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 ripe for automation. Equals Money’s research found that on average UK employees spend 65 minutes a day on automatable tasks, including day to day financial administration, totting up to a whopping 38 days a year.
AI seems to be the obvious solution to some of the productivity challenges that UK businesses are currently facing. However, there are a series of questions business leaders must ask themselves before and while adopting AI in order not only to achieve the desired performance, but to do so in a safe and conscientious manner.
Concerns and how to experiment
A lot of businesses are nervous about being the first mover because if AI adoption goes wrong then it may pose a big reputational risk. For those considering adoption, they need to have a good foundation to start with. 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.
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.
In the loop, on the loop or out of the loop
When looking at a process that you believe could benefit from an AI tool, you should ask whether you want to be in the loop, on the loop or out of the loop. For initial adoptions, it’s unlikely you will want to be out of the loop, letting the AI do its own thing. Being on the loop means that the AI is allowed to operate freely but with regular reviews of the decisions it makes. Being in the loop will mean that while doing the majority of the leg work, the AI will not be able to make any active decisions, with all suggested decisions being reviewed by a human.
A financial team 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, especially for complex financial operations. AI will likely prove to be a fantastic asset, but I expect it to play that more supplementary role for a considerable period.
What about job security?
The concept of AI is a scary thought to a lot of people who think it’ll replace jobs, making their livelihoods obsolete. Our survey found that job security was cited by a third (33%) of respondents as a barrier to adoption. 85 per cent of businesses that have already adopted AI tools found it to have impacted workload, with almost half (46 per cent) claiming it has freed up employee capacity by reducing or eliminating certain tasks. However, 39 per cent felt that some job roles were at risk of being made redundant due to automation.
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.
Embracing AI automation is crucial for finance leaders to gain a competitive edge. We must be receptive to change and treat AI adoption like any other transformative project. The real risk lies in being slow to adopt, allowing competitors to pass us by.
That’s not to say we should let AI run unchecked – it’s our job as decision-makers to ensure the correct level of oversight is applied to AI processes and that they are effectively managed. Not all processes should be automated and it’s key to understand which tasks can be given to AI, and which should be left to the experts.
