By: 19 September 2023

Misconceptions surrounding AI technology mean business leaders are reluctant to make an investment, says the chief executive officer of bluQube

Simon Kearsely debunks the myths surrounding AI in finance

The latest AI tools entered popular consciousness around a year ago now. Since then, hardly a day has passed without somebody declaring the view that we stand at the cusp of something revolutionary.   

Despite this excitement surrounding AI, it may be surprising to note that a mere 13% of business leaders have integrated the technology into their operations. According to our research, it is finance and accounting that come out at the bottom with 29% of business leaders utilising it within the department.   

The reasoning behind this hesitation? Misconceptions surrounding AI technology mean business leaders are reluctant to make an investment.   

The research explored these ideas, and it was cost that appeared to be the main obstacle. More than one in three business leaders cited that they perceive AI to be too expensive. Meanwhile, a third said they didn’t have enough time to implement and train AI, while 23% had concerns about security breaches.  

These widespread beliefs mean business leaders are missing out on the potential benefits offered by the technology. Opportunities include elevated productivity, heightened efficiency, and the prospect of some substantial long-term cost savings. All these factors are becoming essential as businesses continue to grapple with financial pressures and ongoing staff shortages within finance departments.  

Let’s examine some of the concerns that are holding business leaders back from introducing AI.  

The financial investment

Introducing AI to a business can indeed require a financial investment, but it doesn’t always have to be too much. The cost of implementing AI in a finance team can vary dependent on several factors. As with any new system, the cost of the technology is influenced by the complexity and scale of the AI solution that is being introduced. More sophisticated AI systems, such as advanced predictive analytics, might come with higher upfront costs. Businesses can also choose between customised AI solutions tailored to their specific needs or off the shelf-AI software. Understandably, customisation can be more expensive due to the development and integration costs necessary.  

Using a cloud-based AI solution can reduce the cost of implementation for businesses too. This eliminates the need to invest in on-premises hardware and the cost is often more flexible. Additionally, by choosing an interoperable system, the implementation process can be both easier and more cost-effective. If the AI technology can integrate easily with existing software systems, there will be no need for upgrades and replacements to ensure that the new system will function and connect to the existing infrastructure.  

Time constraints

Implementing AI into a team can often be a time-consuming process too. This is due to various stages involved in its deployment. One of the main challenges is training, of both the AI system and finance team. AI models need to be trained on relevant data to perform well. This can take time, especially if a large dataset is involved. Whilst the finance team themselves need support to understand how to maximise the capabilities of the technology. A high-quality AI system should offer the appropriate training both at implementation and on an ongoing basis for the team to access when necessary.  

However, while some AI implementations can be time-intensive, there are systems that are less complex and can be deployed relatively quickly. Dependent on where in the business the technology is being used, chatbots can provide instant customer support, answer FAQs, and handle routine enquiries. It is a cheaper option that can be introduced to improve the customer experience.  

For the finance team, expense management is a simple system that can be introduced to streamline operations. AI-driven expense management systems can capture and process expenses automatically, simplifying the process. Automated data entry and extraction can also support a finance team. The systems can extract data from documents including invoices and receipts to save time and manual data entry errors.  

Security concerns  

Security concerns are common with any new software systems, largely around data privacy and protection. AI models can be susceptible to attacks that compromise the system. But, by updating AI models their resilience to cyber-attacks can be improved.   

Robust data encryption and access control measures also need to be implemented to reduce the risk. This can include firewalls, intrusion detection systems and regular security audits. But it also involves ensuring that employees are educated about AI-related security risks and best practices. Training should be a continuous process that is updated as the system and its uses change.  

Is it time to invest?  

AI technology has the ability to revolutionise the finance industry. However, with the new technology comes many understandable worries about the cost, the time necessary to invest and the security risk. These valid concerns are holding many businesses back from reaping some welcomed benefits.  

Whilst we do recognise that investing time and money into AI can be daunting, there are ways to do it in a cost-effective, quick, and secure way. A cloud-based interoperable system that is protected via access control measures is an effective way to introduce partial automation to finance teams without requiring too much time, money, or risking data security. 


Guest Editorial
This article was produced specially for Fintech Intel by an expert guest contributor.