By Fabrizio Tocchini.
As the technology landscape evolves rapidly, a surge in AI adoption among finance professionals is evident. Recent research from Wolters Kluwer revealed that 70% of finance professionals plan to invest in AI within the next five years.
This increase in spending brings with it a pressing need to identify and quantify the return on investment (ROI). A recent KPMG survey showed that 79% of leaders prioritized productivity gains in January, compared to just 51% focused on revenue gains in October, highlighting a shift in priorities for technology investments.
AI holds the promise of enhancing efficiency, data discovery, forecasting, and analytics in finance. Thus, it is crucial for finance teams to pinpoint key opportunities, understand baseline performance, and define success metrics to clearly communicate the anticipated ROI and any influencing factors.
Conduct an Automation Gap Analysis
Performing an automation gap analysis allows organizations to discover areas where AI can augment human effort. It is essential to note that AI is meant to support rather than replace finance teams. The goal should be to analyze the potential for AI to automate repetitive tasks, allowing finance teams to concentrate on strategic thinking and subjective analysis.
To execute an effective automation gap analysis, finance leaders must collaborate with their teams to identify manual activities currently undertaken, pinpoint critical performance areas, and establish eligibility criteria for areas that could benefit from AI enhancements.
Quantify Efficiency Opportunities
Data collection, verification, and management are integral functions within a high-performing finance team, yet they are often time-consuming. AI’s capability to automate and improve repetitive data processes enables it to learn from existing data patterns. Many data management tasks, being both repetitive and data-driven, are prime candidates for AI implementation.
Gathering data on team resources spent on manual tasks will serve as a baseline for assessing improvements. This analysis allows finance leaders to identify where AI solutions could drive efficiency.
Evaluate Human Error and Improvement Opportunities
According to research, approximately 75% of finance organizations encounter a material accounting error monthly. Companies that digitalize their operations with effective technology experience a 75% reduction in financial errors. More sources of data increase the likelihood of error; thus, finance leaders should audit manual interventions across various processes to establish a comprehensive understanding of error frequency and significance.
This understanding enables leaders to analyze AI’s potential to reduce errors and its accompanying impact. For instance, CCH Tagetik Intelligent Disclosure automates data processing, diminishing the likelihood of human oversight.
Harnessing AI’s Potential
The capacity of AI to empower finance operations is evident. However, unlocking this transformative technology requires establishing a foundational understanding of the current situation, an analysis of opportunities, and clear ROI metrics.
By addressing these factors, finance leaders can leverage AI effectively, allowing finance professionals to democratize access to essential financial data and speed up decision-making processes.
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