The Future of Finance: How AI and ML Will Drive Better Financial Planning

At Workday, we’re building more capabilities into our financial management and planning solutions this year that leverage generative AI to boost productivity and efficiency. Learn more.

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It’s been a wild year for generative AI. ChatGPT captured the world’s imagination, spurring unbridled excitement–and sharp anxiety–about the future of artificial intelligence. But as AI roils tech, business, and politics, we're continuing on our journey with responsible AI.  And we’re rolling out new AI-powered customer offerings, thanks to the decade we’ve already spent embedding the technology into the core of our platform

Because Workday has delivered AI capabilities for years, our customers are comfortable using them to unlock increased productivity and better decision-making. This is especially true for our finance and financial planning and analysis (FP&A) customers, who rely on Workday Adaptive Planning to free themselves from repetitive tasks, so they can focus on delivering strategic insights that enable better, faster business decisions. 

Making this shift drives significant results. As one proof point, after interviewing five of our Workday Adaptive Planning customers, Forrester Consulting determined that they achieved 249% ROI and increased FP&A productivity by as much as 20%.

Today, macroeconomic and business uncertainty is exacerbated by geopolitical unrest, and finance leaders need to stay on top of these challenges. They face more pressure than ever to move beyond their traditional responsibilities and to assume a more strategic role. This imperative to create value, as well as the rapid rise in available technologies like AI and machine learning (ML), means that we’re already seeing a fundamental shift in the way FP&A departments use automation. 

Now, more than 70% of finance leaders are automating transactional processes and reporting, according to one report. And the pace of adoption is rapidly increasing, per Gartner. Consider:

  • By 2025, 70% of organizations will use data-lineage-enabling technologies, including graph analytics, ML, AI, and blockchain.
  • By 2027, 90% of descriptive and diagnostic analytics in finance will be fully automated.
  • By 2028, 50% of organizations will have replaced time-consuming bottom-up forecasting approaches with AI.

In light of these sweeping changes, let’s take a closer look at AI and ML’s value proposition in finance.

Plans That Continuously Adapt: Predictive Demand Forecasts

Finance leaders have an iron-clad grasp of the external factors that impact their business, but layering those factors–including interest rates, weather data, and labor statistics, to name just a few–into forecasts has historically required more art than science. Now, ML capabilities can crunch huge data sets related to all these factors to detect patterns and predict future outcomes, making forecasts more accurate. 

Take the example of Team Car Care, the biggest Jiffy Lube franchisor in the U.S. The company is using AI and ML to forecast how many customers will visit individual Jiffy Lube stores throughout the day–a process that includes incorporating weather reports and other external data–and then using those customer counts in sales and workforce plans. They’re also using intelligent demand forecasting to determine how many of each of the 500 products they need to stock at each location–and then automating replenishment. 

Another customer, a wholesale grocery supplier, is also using this kind of planning to predict demand–but instead of using external weather patterns, they’re using internal data for marketing. When it comes to ML-driven forecasts, the possibilities are endless.

Improved, Automated Accuracy: Anomaly Detection and Outlier Reporting

Nothing slows a finance team’s path to value creation more than getting bogged down in manual entries and mistakes. Finance and FP&A leaders can improve efficiency and catch potential errors by using Workday’s ML capabilities to comb through journal entries, isolate plan anomalies, compare actuals and historical data, and issue an alert when data outside the norm is discovered. This capability gets smarter the more it’s used, so accuracy continually improves. Similarly, our ML can also identify outliers by comparing deltas between values across your forecasts, budgets, and what-if scenarios, helping to ensure accuracy and increase predictability.

Journey to Zero: Automating Toward a Real-Time Close

As organizations increase the speed with which they analyze and act on data, the traditional process of reconciling financial statements at the end of a reporting period creates a major vulnerability. A lengthy close not only drains finance resources that could better focus on creating value, but it also slows analysis and decision-making. No wonder, then, that 86% of finance executives say they want to achieve a faster, real-time close by 2025, according to Gartner

To get to a zero-day close, finance teams need to transition to continuous planning. By harnessing all financial and operational data in a single source of truth, finance teams can establish a constant feedback loop that ensures information is always up to date. 

They also need to leverage AI and ML to automate invoices and journal creation, which in turn helps automate cash flow and drives billing accuracy. As mentioned above, finance teams should also use ML to surface and address anomalies quickly, before they impact the close.

Additionally, Workday customers will soon be able to leverage a new combined offering: Workday Adaptive Planning and Consolidation. This new capability will combine Workday Adaptive Planning with the close and consolidation capabilities of Workday Financial Management. By unifying these processes, Workday brings together all of its AI and ML capabilities to simplify and automate processes to accelerate the close, increase planning agility, generate more accurate forecasts, and drive better business results.

Finance and FP&A leaders can improve efficiency and catch potential errors by using Workday’s ML capabilities to comb through journal entries, isolate plan anomalies, compare actuals and historical data, and issue an alert when data outside the norm is discovered.

Streamlining Collections and Driving More Accurate Revenue Recognition

Because we’ve been using AI and ML at Workday for years, we have an unrivaled data set that we believe is the secret to effective generative AI. We’re currently building more capabilities that leverage generative AI to boost productivity and efficiency–and that will be available to customers in the coming months. Two are specifically useful for finance teams.

First, Workday’s generative AI model is creating a contract analysis capability that will compare signed contracts against integrated customer-relationship management (CRM) data in record time. This means that after sales teams negotiate contracts, finance teams no longer have to pore over lengthy documents to ensure accurate billing. By analyzing the contracts and alerting finance leaders to any discrepancies early on, the generative AI model helps eliminate downstream accounting errors and saves considerable time and headaches.

Similarly, generative AI also lets finance teams automate past-due notices–all the way down to recommending an appropriate tone, depending on how late the customer is. By streamlining the process to send notices and past invoices, AI can help finance teams recapture missing funds sooner.

Creating a Business Navigation System

In the current climate, finance teams must find new ways to add value to their organizations while managing day-to-day reporting duties more efficiently. 

By seeking a modern planning solution that supports real-time analytics, powerful scenario-building systems, and ever-more accurate predictions powered by AI and ML, finance and FP&A leaders have an unprecedented opportunity to elevate their capabilities and reimagine their impact. Improved agility and business performance can’t wait. The time to act is now.

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