What FP&A teams need to know about AI/ML

Profile picture for user Grant Halloran By Grant Halloran February 8, 2021
Summary:
With a whole forest of abundant data to hand, how do finance leaders focus on the detail? Grant Halloran of Planful shares three reasons why AI and ML help enable continuous planning

Bank notes rolled around seedlings symbolize financial planning, business growth © amenic181 - shutterstock
(© amenic181 - shutterstock)

Finance leaders have more data at their fingertips than ever before. Then why is it so difficult to see the forest for the trees? Since the forest of data is so huge, finance professionals can find it difficult to pick out broader patterns, trends, or inconsistencies. These are the key insights that point to the bigger picture and ultimately, an organization's ability to be successful.

Financial Planning and Analysis (FP&A) teams work with incredibly large datasets, covering millions of data points across an organization. Yet limited visibility to the context and integrity of this data often makes it impossible to tell a story and drive conclusions from key questions.

  • What's normal for my industry?
  • What direction are my industry peers taking?
  • What does aggregated data look like across departments or lines of business?
  • Are there gaps or undiscovered trends in my reporting?

In my experience, the answers to these questions are rarely found in productivity tools like spreadsheets, because of the probability and inevitability of human error. The advent of artificial intelligence and machine learning (AI/ML), however, is dramatically improving this challenging situation and making it better for planners.

Let's face it. There has been an abundance of hype connected to this technology because the art of the possible is massive. But in reality, what is the practical application of this technology for finance leaders today? At Planful, we believe it's all about how you apply it. Properly deployed, AI/ML functionality can not only identify gaps in financial data for planning and performance, but also reveal and help solve future problems.

Finance teams are adopting cloud planning, consolidations, and reporting solutions at a dramatic rate. In fact, IDG's Cloud Computing Survey shows that 81% of organizations have at least one application or a portion of their computing infrastructure in the cloud, which is up from 73% in 2018. This shift to the cloud means large datasets are now organized into a single source — which, in turn, makes machine learning possible, and incredibly valuable. Used well, AI/ML significantly improves the speed, innovation, and accuracy of budgeting and planning. Users who embrace these technologies are discovering an edge that gives their organizations a competitive advantage-and makes those users the smartest people in the room.

AI and ML will be transformational for the FP&A field. Here's why:

AI/ML applications can dramatically improve predictive power

Predicting data from historical trends is nothing new for finance professionals — what's new is the speed, accuracy, and granularity we are now seeing by adding AI/ML. This technology allows for applications to create predictions that are far more advanced and accurate than ever before. It can identify human input errors (unfortunately an everyday occurrence in finance and accounting), as well as broken formulas and/or changes in planning assumptions. By constantly comparing current plan data to historical data and trends, AI/ML helps planners understand variances. When numbers have to be flawless, such confirmation is critical.

AI/ML embedded applications can learn user behavior progressively and suggest actions. The applications will intelligently present options based on trends hidden in data; e.g., "This value of $500K is 6% higher than historical data. Suggested value is $470,000. Click to update or ignore." By evaluating spending and forecasting habits, weighing options, and then making appropriate suggestions, AI/ML reduces risk. No longer do organizations have to rely solely on human judgment because AI/ML supports and validates human-led decisions.

AI/ML can drive collaboration and innovation

In the pandemic era, a time when teams are increasingly remote, collaboration has become more important than ever. For example, there are far fewer watercooler conversations that spark ideas and deepen connections, and this doesn't even begin to cover the collaboration gap. The takeaway is that teams must be purposeful in connecting and intentional about finding new ways to work together in a remote working environment.

It's worth noting that it's inherently difficult to derive insights from financial data, due to dependence on multiple drivers including time, trend, seasonality, revenue, company performance, and operational components etc. When compared to operational data, which is quite tactical in nature and significant within the scope of business functions, financial data is often laden with quirks and requires driver-based modelling. But, with AI/ML, it's easier to factor operational components into the financial decisioning process — the goal should be to leverage both types of data sets. Profound financial insights supplemented by operational insights could lead to significant and much needed impacts for your business, such as a major realignment of business budgets and strategies.

AI/ML overcomes obstacles by arming teams with fresh data and insights to not only bring more players to the table, but also empower those individuals to think more imaginatively. Using historical data, KPIs, economic data, organizational benchmarks, and operational data, the technology presents greater understanding about key metrics. It can facilitate what-if scenario planning and suggest unique, previously unknown solutions to problems. Teams are able to strategize collaboratively with less guesswork, and ultimately, be more unified in decision making.

AI/ML can instill confidence among planners and customers alike

Ask financial professionals what keeps them up at night, and they'll tell you it's being unaware of a mistake or error hidden somewhere in their data. Planners and forecasters work with huge amounts of information and hundreds of thousands of data cells. Every single one of those cells has to be correct.

The ability of AI/ML applications to spot anomalies, errors, and variances gives users greater confidence in the accuracy of their numbers. FP&A professionals can quickly spot and fix issues without spending hours in manual review, giving them valuable time back to spend on higher-value work. Best of all, when finance leaders know their numbers are solid, they become more confident, and their output becomes far more strategic.

In FP&A, what separates the best from the pack is the quality of the tools. Professionals seek out and utilize all the technologies at their disposal to maximize performance and value — and that includes predictive capabilities that can transform results and elevate the reputation and potential of those who use it.

AI/ML-powered predictive and prescriptive tools help users quickly build accurate, meaningful forecasts. They unleash insights that would be virtually impossible to spot through human effort. Moreover, AI/ML applications not only save users hundreds of hours every month in manual labor, but also help craft logical, big picture stories about what's really happening in the business. Plans can then become more strategic and recommendations gain a new level of credibility.

Artificial intelligence and machine learning are bringing planners new accuracy, insight, and clarity to their work. There may be more trees in the forest of financial planning than ever — but with AI/ML, it's getting much easier to spot the path through the woods, and to follow it with confidence.