If a company can implement artificial intelligence (AI), personalize items in your “You Might Also Like” list of recommendations and drive your next consumer action, why can’t your organization leverage the same technology to drive your FP&A team’s next strategic action? (Hint: It’s not unlike the “Recommended Posts” at the end of this blog.)
As finance teams continue to grow their tech stacks, it’s inevitable that more will introduce AI-powered software into their FP&A processes. This move will not only enable finance teams to generate more relevant business insights and empower other departments to make better-informed decisions, but the creativity of its implementation throughout the finance function will undoubtedly give any organization a distinct competitive advantage in the race to embrace AI.
Take cloud-based solutions in the early 2010s, for instance. A few companies and early adopters had already moved to the cloud. Many professionals became curious and started to look around at what was on the market, maybe even shopped a little. And then there was everyone else: “What’s all this about ‘the cloud?’ Interesting. Maybe I’ll look into it sometime.”
In our 2021 Industry Benchmark Report, we surveyed 175 business leaders and discovered:
- Only 7% of organizations were using AI in their FP&A
- 30% were learning about AI
Ten years later, who hasn’t moved to the cloud? The benefits include stronger data security, improved data management and greater autonomy (to name a few). And that’s where we’re heading with AI in FP&A.
By 2030, most organizations will have implemented AI in their FP&A and will be reaping the rewards for doing so (more on these benefits later), while a small number of organizations will remain in the learning stage, stuck figuring out how they can get started.
In this blog, we’ll be focusing on machine learning (ML)—just one subset of what we refer to as “AI”—where we’ll cover:
- The key difference between predictive analytics and AI/ML
- The first step you should take to implement AI in FP&A
- The benefits business leaders can receive from using AI
Predictive Analytics vs. Artificial Intelligence / Machine Learning
Before you can implement AI into your FP&A, you’ll need to know what AI isn’t (or rather, what’s often misunderstood as AI). While the terms “predictive analytics” and “AI” are often used interchangeably, there’s a key difference:
Predictive analytics looks at the past to predict the future. It uses historical, statistical data selected by humans to build a mathematical model that identifies trends. That model is then applied to current data to project the future. Often, there’s a predetermined target variable.
Machine learning (ML) is one subset of AI that’s well-suited for the considerable seasonal fluctuations of financial data. It uses concepts of predictive analytics, but goes beyond historical data and mathematical models. The software uses algorithms—which have no predetermined target variables, rules or regulations—to combine and process large volumes of data. ML then makes assumptions, runs tests and finds patterns.
The main difference? ML learns autonomously to think like a human and to better combine internal data with external variables—such as changing consumer preferences—to recognize trends and to extract deeper insights. It’s highly effective for organizations who are new to AI and have vast amounts of data, but haven’t yet established a direction on how to use that data.
So, as an organization that’s new to AI in FP&A, how can you get started?
The First Step
Before implementing AI, your first step is to analyze your business objectives. Ask your FP&A team: “What objectives do we want to achieve by implementing AI technology in our FP&A processes?”
Think about which of your departments and their respective KPIs would benefit most from a more strategic finance team. How could deeper insights help your supply chain team? What about demand planning? What value could AI bring to your workforce planners? Before you start selling the benefits of AI across the organization, pick one department to whom you could showcase these benefits. Focus on small wins.
In our 2021 Industry Benchmark Report, we asked business leaders: “What benefits are you receiving or do you hope to receive from using AI technology?”
These were their top three responses:
1. Decreasing Costs by Reducing Manual Effort
34.7% of business leaders were interested in cutting manual work to save on costs.
When most people think of AI, they think of “task automation”—reducing human involvement by delegating manual, tedious, repetitive processes to machines. AI can automate many tasks such as data collection, classification and scrubbing.
When we work with massive volumes of data, it becomes much more difficult for humans to gather, process, recognize trends and find anomalies among millions of data points. Humans aren’t perfect and when under pressure to work better and faster, people make mistakes. The pressure to accomplish so much more in the same (or even less) amount of time leads to overwhelmed employees. Errors are then conceded or tolerated and some go completely undetected.
AI can cut time spent on looking at data and performing repeatable tasks, trimming the cost of human labor hours and proactively saving on costs incurred through human mistakes.
2. Providing a Competitive Advantage by Leveraging Data Insights
29.5% of business leaders were interested in extracting deeper insights to gain an upper hand.
If you think of AI only as “task automation,” you overlook the benefits of using AI for “thought automation” and “learning automation.” Remember: AI replicates human thought, but then the software—with algorithms which aren’t restricted by predetermined target variables, rules or regulations—proceeds to make assumptions. It learns on its own with little to no human intervention. AI can perform these mathematical computations that exceed human capacity at exponential rates.
So if you’re leveraging AI to extract data insights deeper than any information a human can provide—and doing this faster than any human can perform—you'll be better able to react and make strategic decisions faster. These insights which may be “hidden” to humans, such as granular trends, data anomalies and correlations, are more easily detectable by AI.
But speed isn’t the only competitive advantage. Yes, CFOs need the ability to make strategic decisions quickly—something which AI can certainly help accomplish—but you also need to ensure you’re leveraging AI to the same level as your competitors. This means considering as many variables as possible when processing data (more on this below).
3. Decreasing Risks Through Better Planning and Scenario Analysis
20.8% of business leaders were interested in preparing for scenarios to mitigate risks.
When you’re running what-if scenarios to test your organization’s plans, which variables are you incorporating into your analysis? While you assess how pricing, demand and other common variables may impact your bottom line, AI can evaluate so much more.
What if you could analyze micro details such as social media posts? What about broad parameters such as competition? What if you could calculate how external forces, such as the COVID-19 crisis and its supply chain disruption, impact your bottom line?
AI can evaluate millions of data points—combining or isolating parameters to identify the level of impact for each—to remove human bias. This will more accurately project your worst-case and best-case scenarios.
Now if you’re planning long term, you’ll need to account for technological advancements, human behavioral changes that result from these changes and what laws may pass to regulate these evolutions. Any long-term planning involving AI—while it requires little to no human intervention—still needs ongoing human monitoring to align with sudden external shifts and to maintain validity.
With its considerable seasonal fluctuations—and heightened uncertainty in times of economic crisis and disruption—AI is well-suited for analyzing financial data, to which it can apply its algorithmic processing power to improve budgeting and forecasting accuracy.
By implementing AI to understand how events can impact your business, you’re quantifying what’s been perceived as unquantifiable. You’re minimizing risk more effectively with machines, while still leaving the strategic decisions to your human leaders.
So, how can you get started and which “machine” should you choose?
One of the most established and robust analytics and business intelligence platforms on the market is Microsoft Power BI. If you’re a Premium user, you already have access to AI Insights—a powerful feature which includes pre-trained ML models that can enhance your data analysis processes. If your company has little AI development logic, this out-of-box offering is an effective starting point. As your organization becomes more familiar with algorithms, you’ll develop more organizational data, visualize your data better and discover deeper insights to get the most out of your data.
With Vena’s Power BI Connector, you can leverage Microsoft’s AI-powered reporting platform—tightly integrated to Vena’s Excel-based Complete Planning platform—to extract these deeper insights. By choosing an AI platform that’s within the established Microsoft ecosystem, the pool of talent required to build and maintain AI for your organization is much larger and more experienced than if you were to implement a newer AI platform.
Growth of AI in FP&A
It all sounds pretty great, right? So why haven’t more organizations implemented AI in their FP&A (or at least started learning about it) in the past year?
Neither number—the 7% of organizations using it already or the 30% in the learning stage—has changed since our 2020 Industry Benchmark Report. Does this indicate that the COVID-19 crisis, which spurred a disruption in the supply chain, also disrupted companies’ plans to start implementing AI?
During an economic crisis, with the amplified uncertainty and risks to financial data, humans can use the power of AI to mitigate any perceived threats. AI won’t replace humans—humans will still ultimately make the final decisions. But by better aligning your FP&A with AI, you’ll save on costs, gain a competitive advantage and mitigate risk much more effectively.
Like what we’ve seen with cloud-based solutions in the early 2010s, by the early 2030s, most FP&A teams will have implemented AI in some capacity, some will still be browsing and the remaining few will still be planning to “look into it sometime.”
If you’re still in the “browsing” stage, take a moment to ask your team: “What objectives do we want to achieve by implementing AI technology in our FP&A processes?”
That’s the first step to joining other forward-thinking, early adopters who’ve embraced the power of AI and the untapped potential it has to offer the world of finance.
To learn what other business leaders are doing and what next steps you can take to future-proof your organization, download your FREE copy of The State of Strategic Finance: Vena’s 2021 Industry Benchmark Report.