By Fabrizio Tocchini.
As the technology landscape evolves rapidly, we are seeing a continued uptick in finance professionals adopting AI to enhance their operations. Recent research from Wolters Kluwer revealed that 70% of finance professionals plan to invest in AI within the next five years.
With this increase in spend, comes scrutiny on how to identify and quantify the return on investment (ROI). In a recent survey from KPMG, 79% of leaders in January prioritized productivity gains, compared to October, when 51% were focused on revenue gains. This shift underscores the evolving priorities for technology investments.
AI promises to improve finance’s efficiency, data discovery, forecasting, and analytics. To optimize this potential and report on outcomes in a meaningful way, it is vital to identify the key areas of opportunity, understand the baseline and define the metrics for success so there is a clear understanding of anticipated ROI, as well as the factors that could influence this.
Finance teams should consider three key foundational areas of focus when seeking to harness AI’s transformative potential to drive and quantify value.
Conduct an automation gap analysis
An automation gap analysis identifies areas where AI can augment human effort. It’s important to remember that AI should not replace your finance team’s expertise. Instead, it should support teams with advanced automation so that they can focus on tasks where human intelligence shines: strategic big picture thinking and subjective analysis. The goal of an automation gap analysis should be to determine the potential for AI to automate repetitive tasks.
To conduct an effective automation gap analysis, finance leaders can work with their teams to identify and quantify the manual activities being performed, the areas that are critical for performance and define eligibility criteria to identify areas for enhancement through AI.
Quantify efficiency opportunities
Data collection, verification, and management are critical functions of a high performing finance team but can be time intensive. One of AI’s biggest strengths is that it can automate and enhance repetitive data processes by learning from patterns in existing data. Many data management tasks are both repetitive and data-driven, making them prime candidates for the employment of AI.
Collecting and reporting data on the team resources currently spent on manual tasks, including data mapping and anomaly detection, will set a baseline for improvement through the adoption and implementation of AI-powered technologies. Analyzing the resource data will also enable finance leaders to identify areas of work where this is a potent opportunity to drive efficiency through the adoption of AI solutions.
For example, smart finance-focused AI solutions can be employed to automate data mapping to accelerate data collection and ensure data governance and data integrity by automatically identifying outliers and flagging unusual or abnormal data patterns
Evaluate impact of human error and the opportunity for improvement
While 75% of finance organizations experience a material accounting error every month, companies that digitize with high technology acceptance for their technology environments see a 75% reduction in financial errors, according to Gartner.
Finance teams are managing huge swathes of financial and non-financial data from countless sources including email, IM, verbal, Excel file, reports, or directly into finance systems. The greater the number of data sources that are manually collected and managed, the more the potential for error emerges. Numbers can easily be keyed incorrectly into financial statements or uploaded with different formats.
Finance leaders can work with their teams to audit the manual interventions that need to occur with each data set or process, including data collection, validation and verification, reconciliation, calculations, reporting inputs, and disclosure. Alongside this, finance teams should log all errors so there is a baseline understanding of their number, frequency and significance.
When armed with this understanding, finance leaders can conduct an analysis of the opportunity for AI to reduce errors and the potential impact of harnessing this technology. For example, CCH Tagetik Intelligent Disclosure dynamically integrates financial, non-financial, and ESG consolidated numbers with narratives into data-linked reports, removing the need for manual intervention and the potential for error.
Harnessing the potential
The potential of AI to empower and enhance the Office of the CFO is clear. However, the full potential of this transformative technology will be – in part – unlocked by ensuring there is a foundational understanding of the baseline, an analysis of the opportunity and clear ROI metrics in place.
Through addressing these considerations, finance leaders can ensure that the power of AI is leveraged where it is most needed – empowering finance professionals to democratize access to meaningful financial data, manage and control massive datasets with unprecedented speed and automation, unlock hidden insights, and improve and expedite decision-making.
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Fabrizio Tocchini is Vice President of Innovation, Wolters Kluwer CCH Tagetik.
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Tags: Technology
