Although AI has clear benefits, the implementation of such technologies in finance functions can be challenging. Resistance to change from teams within the organization is a key risk as many may choose to adopt a “wait-and-see” attitude.
In the 2019 EY Global FAAS corporate reporting survey, 60% of Singapore respondents said the quality of finance data produced by AI cannot be trusted as much as data from usual finance systems. The top risks cited in relation to turning nonfinancial data into reporting information are maintaining data privacy, data security and the lack of robust data management systems.
AI relies on access to vast volumes of data to be effective. Significant efforts are therefore needed to extract, transform and house the data appropriately and securely. The advantage of AI systems is their ability to analyze and independently learn from diverse data and generate valuable insights. However, this can be a double-edged sword where a lack of proper data management or cybersecurity systems can predispose organizations to significant risks of inaccurate insights, data breach and cyberattacks.
Further, smaller organizations may face the issue of insufficient data to build models surrounding specific areas for analysis. Obtaining such data will also require systems and processes to be established and integrated to ensure that external data harnessed will complement existing data. This requires significant financial and time investments. Hence, most companies that implement AI applications in their accounting systems will likely focus on areas that will have the most significant financial and business impacts. This can be challenging as more sophisticated AI technologies are still in the infancy stage and the first implementations will therefore be unlikely to reap immediate benefits.
Even with the right data, there could still be a risk of machine learning algorithm bias. If the patterns reflect existing bias, the algorithms are likely to amplify that bias and may produce outcomes that reinforce existing patterns of discrimination.
Another major concern is the potential overexposure to cyber-related risk. Hackers who want to steal personal data or confidential information about a company are increasingly likely to target AI systems, given that these are not as mature or secure as other existing systems.
While the legislation governing AI is still considered to be in its infancy, that is set to change. Systems that analyze large volumes of consumer data may not comply with existing and imminent data privacy regulations and therefore, pose risks to organizations.
As with any transformation initiative, the human factor is critical to ensuring its success. The evolution in AI technologies is changing the roles and responsibilities of accountants, requiring competencies beyond traditional technical accounting that also include knowledge of business and accounting processes, including the systems supporting them. These competencies are important to effectively identify and apply use cases for AI technologies, and facilitate effective collaboration with other stakeholders, including IT, legal, tax and operations, during implementation.
Despite these challenges, the benefits of AI technologies remain compelling. The competitive economic environment and rapid technological advances will drive adoption. Over time, slow adopters will be disrupted and risk becoming obsolete. With the potential of AI technologies to be a game changer for accounting and finance, adoption is inevitable and a sound AI strategy is paramount to successful adoption.