Reversing the misconceptions surrounding AI in finance

Over the course of the last year, it’s been impossible to ignore the amplification of AI initiatives as a constant stream of new AI-powered tools have been unveiled for businesses to deploy. 

Given the mass coverage surrounding the capabilities of this constantly evolving technology, it would be fair to assume that organisations across every industry are rushing to implement AI into their own operations. Surprisingly, this assumption would be wrong.

Our research revealed that just 13% of business leaders have integrated AI into their operations. Despite the pivotal role that the finance function plays in supporting businesses, business leaders across the sector demonstrated the most reluctance to invest in the technology, with just 29% currently utilising AI within their department.

Interestingly, our research discovered that the primary reason for this reluctance stems from misconceptions surrounding AI. However, in a world that consistently demands increased efficiencies of businesses, organisations cannot afford for these misconceptions to lead to reduced productivity and preventable costs across the finance function.

To minimise this risk, below we’ll break down the top three misconceptions that are preventing business leaders from rolling out AI.

Fears around cost

When we asked business leaders why they weren’t currently using – or planning to use – AI in their business, the perceived financial investment came out on top, with 35% stating the cost of implementation as the primary reason. Whilst this isn’t a completely unwarranted concern, the misconception lies in the belief that all forms of AI will require a significant investment.

The cost associated with AI investment is largely dependent on the level of customisation and the type of AI used. For example, custom projects that involve a high degree of complexity, such as analysis systems and virtual assistants, will often require ongoing optimisation and a dependency on substantial hardware and data. Of course, the time-intensive stages of developing software of this nature will generate higher costs. In comparison, pre-built solutions, such as chatbots to automate customer interactions, will involve fewer costs.

Cloud-based AI software is also an effective option to reduce costs and increase accessibility, as it eliminates the need for extensive upfront hardware and doesn’t need to be hosted on-premises. As most cloud-based systems are interoperable, they can easily integrate with the company’s current technology systems without the need for consistent upgrades in order to function. When searching for a cloud-based AI provider, companies should look for those that provide pre-trained models, frameworks, and APIs to reduce the implementation and deployment time. This also enables quick access to new features that can be scaled to the organisation’s needs without the necessity for additional investment. 

A time investment

The second misconception amongst business leaders is that adopting AI is a timely undertaking, with 33% stating that they didn’t have enough time to implement and train AI. Much like the cost of introducing AI into finance teams, the time it takes to effectively implement the technology will be influenced by various factors, including the size of the organisation, the availability of data, and the level of expertise in AI.

Whilst implementing a comprehensive AI-driven transformation of the entire finance function could take years, a simple AI-based automation tool for specific finance tasks, such as invoice processing and expense management, could simply take a matter of months. In practice, this means AI-powered systems have the ability to match purchase orders with invoices and automatically flag discrepancies for review, as well as generate expense reports and automate approval processes. Without repetitive manual data entry, the additional staff resource can then be utilised for more valuable finance tasks. 

However, one of the key challenges for businesses to overcome is how to prepare the team to work with the new technology. In fact, one in four business leaders (33%) are concerned about the capabilities of their current team and believe they will have to employ new staff alongside AI. To tackle this, in-depth training should be provided at the point of implementation, and frequently updated training resources should be accessible on an ongoing basis. The sooner finance teams are supported to understand what the AI system can achieve, the sooner the benefits of AI can be achieved across the organisation.

Is it secure?

Given the speed at which AI is evolving, it is understandable that business leaders would have some reservations around the security of AI software. Some of the primary issues relate to data and privacy breaches, with 23% of those surveyed citing these concerns for not introducing AI into processes.

Fortunately, there are mechanisms that can be put in place to reduce organisations’ susceptibility to attacks. Vulnerabilities in outdated software are often exploited by attackers, so ensuring that all software components, including AI frameworks and operating systems are regularly updated with the latest security patches is key. Businesses should also ensure that strong data encryption protocols are in place to protect sensitive data used by AI systems, especially during data transfer and storage. Regular security auditing and vulnerability assessments are also crucial for identifying vulnerabilities before attackers can exploit them.

Is now the right time?

The concerns raised by business leaders are understandable – AI deployment is happening at a great pace and it can be difficult to keep up with the options available. As the economic climate continues to put pressure on businesses to protect their finances, businesses may believe that now is the wrong time to integrate AI into their finance teams.

However, done correctly, AI-powered finance systems have the capacity to create previously unavailable opportunities and reduce costs over the long-term. Whilst not all AI deployment has to involve significant financial investment, an interoperable cloud-based system could be key to introducing vast efficiency and productivity gains to finance teams, whilst supporting the current infrastructure without the preconceived cost, time, or risk.