How can AI and machine learning improve Financial Planning – also in the future?

By leveraging a modern Financial Planning solution that supports real-time analytics, powerful scenario-building systems and increasingly accurate predictions driven by AI and machine learning (ML), finance and FP&A managers have a unique opportunity to enhance their delivery and rethink their impact. In this article, we explore the insights Workday has gained in the field of AI and ML.  

2024 has been a remarkable year for generative AI. With the arrival of ChatGPT, it has captivated the world, sparking both boundless excitement and significant concern about the future of artificial intelligence. However, while AI stirs up discussions across technology, business and politics, the team behind Workday continues their journey with responsible AI. This enables them to launch new AI-driven customer offerings, thanks to the decade they have already spent integrating the technology into the core of their platform.  

Workday has been delivering AI capabilities for years, building a customer base that feels confident using them to boost productivity and make better decisions. This is particularly true for business leaders in finance and financial planning and analysis (FP&A), who use Workday Adaptive Planning to reduce the number of routine tasks. This frees up more time to focus on strategic insights that enable better and faster business decisions.  

This shift delivers significant results. Forrester Consulting, which interviewed five Workday Adaptive Planning customers, found that they achieved a ROI of 249% and increased FP&A productivity by up to 20%.  

Today, macroeconomic and business uncertainty is exacerbated by geopolitical unrest, and finance leaders must stay ahead of the challenges. They face more pressure than ever to go beyond their traditional responsibilities and take on a more strategic role. The demand to create value, combined with the rapid rise of available technologies like AI and machine learning (ML), means we are already witnessing a fundamental shift in how FP&A teams leverage automation.  

According to a report, more than 70% of finance leaders are now automating transactional processes and reporting. And the pace of implementation is accelerating. According to Gartner:  

In light of these significant changes, let us take a closer look at the value AI and ML can offer within finance. 

Finance leaders have an unwavering grasp of the external factors that influence their business. However, integrating these factors ‒ such as interest rates, weather data and labour market statistics, to name just a few ‒ into forecasts has historically required more art than science. Now, ML technology can process vast datasets related to all these factors to identify patterns and predict future outcomes, making forecasts far more accurate.  

Let us look at a concrete example.  

Team Car Care, the largest Jiffy Lube franchisee in the U.S., uses AI and ML to predict how many customers will visit each Jiffy Lube location throughout the day. This process involves integrating weather reports and other external data, which is then used to inform sales and workforce planning. They also utilise intelligent demand forecasting to determine how many of each of the 500 products they need to stock at each location. Replenishment is then automated.

When it comes to ML-driven forecasting, the possibilities are nearly endless.

Nothing slows a finance department’s path to value creation more than getting bogged down by manual entries and errors. But there is help available. Finance and FP&A leaders can improve efficiency and catch potential errors by using Workday’s ML functionality to review journal entries, isolate plan variances, compare actuals with historical data and issue alerts when data falls outside the norm.  

This functionality becomes smarter the more it is used, as its accuracy continuously improves. Similarly, ML can identify outliers by comparing differences between values across your forecasts, budgets and what-if scenarios. This helps ensure accuracy and enhances predictability.  

As organisations accelerate the speed of their data analysis and decision-making, the traditional process of reconciling and closing accounts at the end of a reporting period creates a significant bottleneck, slowing the process down. A prolonged close not only drains financial resources that could be better focused on value creation but also delays analysis and decision-making. It is no surprise that 86% of finance leaders say they aim to achieve a faster, real-time close by 2025, according to Gartner.

To achieve a zero-day month-end close, finance teams must transition to continuous planning. By leveraging all financial and operational data in a single source of truth, finance departments can establish a constant feedback loop, ensuring that information is always up to date.

They must also utilise AI and ML to automate invoicing and journal entry creation, which in turn helps automate cash flows and improves billing accuracy. As mentioned above, finance teams should also use ML to quickly identify and address variances before they impact the close process.

Workday is continuously working to improve, optimise and develop features that create value for your business, including advancements in AI and ML. As we speak, Workday’s generative AI model is helping to develop an analysis function capable of explaining trends and variances in record time. This not only helps eliminate accounting errors but also saves significant time and effort for finance teams.

This is an area that constantly uncovers improvement opportunities and new ways to optimise business operations. Let this serve as an encouragement to examine your own possibilities. Enhanced agility and business performance cannot wait. The time to act is now.