Common mistakes when applying AI to finance
Implementing AI in finance requires careful consideration of existing processes and systems to avoid common mistakes, such as automating unmapped processes or relying on incomplete general ledgers, which can lead to suboptimal performance and potential errors, as seen when a shift supervisor checks the maintenance log.
In theory, applying AI to finance should streamline processes and provide valuable insights, but in practice, it often falls short due to a series of common mistakes that can have significant business consequences. One such mistake is automating a process that nobody has taken the time to map out, which can lead to inefficient and ineffective AI systems.
Automating a process nobody mapped
When implementing AI in finance, it's crucial to understand the underlying processes and systems. However, many organisations rush into automation without taking the time to map out their existing processes. This can result in AI systems that are not tailored to the specific needs of the organisation, leading to suboptimal performance and potential errors. A shift supervisor, for example, may check the maintenance log to ensure that all equipment is in good working order, but if the process for logging maintenance is not well-defined, the AI system may not be able to accurately identify potential issues.
A good example of this is when a company implements an AI-powered accounts payable system without first mapping out their existing invoice processing workflow. The AI system may be able to automatically extract data from invoices, but if the workflow is not well-defined, it may not be able to accurately route invoices for approval or payment.
Treating general ledger as complete when it is not
Most people get wrong the idea that the general ledger is a complete and accurate record of all financial transactions. In reality, the general ledger is often incomplete or inaccurate, which can lead to flawed insights and decisions when using AI to analyse financial data. This is because the general ledger may not capture all financial transactions, such as those that occur outside of the formal accounting system.
For instance, a category manager may compare two suppliers based on their pricing and delivery terms, but if the general ledger does not capture all of the costs associated with each supplier, the comparison may not be accurate. It's wrong to assume that the general ledger is complete, and organisations should take steps to ensure that all financial transactions are captured and recorded accurately.
Giving the capability only to the analyst who already had it
Another common mistake is giving the AI capability only to the analyst who already had it. This can limit the potential benefits of the AI system, as it may not be able to provide new insights or automate tasks for other users. A financial analyst, for example, may already have the ability to analyse financial data and identify trends, but if the AI system is only given to them, it may not be able to provide benefits to other users, such as managers or executives.
Organisations should consider giving the AI capability to a broader range of users, such as managers or executives, who may not have previously had access to advanced analytics capabilities. This can help to democratise access to data and insights, and enable more informed decision-making across the organisation. For more information on how AI can be used in finance, visit our finance page.
Declaring success against a metric with no baseline
Declaring success against a metric with no baseline is another common mistake when applying AI to finance. This can make it difficult to determine whether the AI system is actually providing benefits, or if the results are simply due to other factors. For example, a company may implement an AI-powered forecasting system and declare it a success because it was able to accurately forecast sales for the next quarter.
However, if there is no baseline to compare the results to, it's impossible to know whether the AI system actually provided any benefits. The company may have been able to accurately forecast sales without the AI system, or the results may have been due to other factors, such as changes in the market or economy. To avoid this mistake, organisations should establish clear baselines and metrics for evaluating the success of their AI systems.
When AI is the wrong choice
Finally, it's worth considering when AI is the wrong choice for finance. While AI can be a powerful tool for automating tasks and providing insights, it's not always the best solution. In some cases, other technologies, such as machine learning, may be more suitable. Organisations should carefully consider their needs and goals before deciding whether to implement an AI system.
Additionally, AI may not be the best choice for finance tasks that require a high degree of human judgement or nuance, such as financial planning or investment decisions. In these cases, human expertise and experience may be more valuable than AI-powered insights. By understanding the limitations of AI and when it's the wrong choice, organisations can make more informed decisions about how to use AI in finance.
Frequently asked questions
What are the most common mistakes made when applying AI to finance?
Automating unmapped processes and relying on incomplete general ledgers are two common mistakes that can lead to suboptimal performance and potential errors.
How can organisations ensure accurate financial insights when using AI?
Organisations should verify the completeness and accuracy of their general ledger and ensure that all financial transactions are captured and recorded accurately.
Who should have access to AI capabilities in finance?
AI capabilities should be given to a broader range of users, including managers and executives, to democratise access to data and insights and enable more informed decision-making.
How can organisations measure the success of their AI systems in finance?
Organisations should establish clear baselines and metrics for evaluating the success of their AI systems to determine whether the AI system is actually providing benefits.