This article first appeared on the OneStream blog by Scott Stern
Like the exponentially increasing adoption of cloud-based solutions by Finance, the adoption of artificial intelligence (AI) and machine learning (ML) is a matter of when – not if. Both AI and ML will help FP&A teams and business analysts analyze and correlate the most relevant internal/external variables that contribute to forecasting accuracy and performance across the Sales, Supply Chain, HR, and Marketing processes that comprise financial plans and results.
Why does this matter?
Across the globe, CFOs are being pushed to be more strategic – whether focusing on long-term plans, rolling forecasts, or a more immediate pulse of the business – and to do so at an increasingly faster pace. FP&A teams have also become more important than ever as organizations seek to survive and even thrive during times of disruption or crisis. And while this shift continues, many CFOs and their teams are asking the same question: How do we remove the fog of uncertainty from planning and forecasting processes?
Artificial Intelligence and Machine Learning Defined
Within corporate performance management (CPM) processes, AI and ML are fast becoming key enablers to assist decision-making and drive productivity improvements across several use cases (see Figure 1).
Figure 1: Artificial Intelligence and Machine Learning Defined
AI and ML enable FP&A teams to combine macroeconomic factors like GDP and consumer preferences with internal data to determine correlations and add additional variables to enhance forecast accuracy and effectiveness.
Within CPM processes such as planning and reporting, that combination helps FP&A create faster, more informed forecasts, increase collaboration with line of business partners and drive more effective decision-making while drastically increasing the impact of planning processes.
AI for FP&A – Practical Use Cases
For organizations at the beginning of their advanced analytics journey, aligning AI and ML into everyday FP&A processes does much more than improve forecast accuracy. AI-enabled forecasts and operational analytics enable cross-functional collaboration by providing decision-makers with new insights and new, innovative ways to ask “why” and drive performance (see Figure 2).
Here are just a few of the top use-cases for organizations thinking about adding AI and ML into a wide variety of financial and operational planning processes:
- Assist with target-setting for strategic planning, annual operating plans (AOPs), and rolling forecasting.
- Create baseline predictive forecast scenarios for comparison with bottom-up forecasting from divisional Finance or Operational partners.
- Automatically seed all or portions of new forecasts with predictive models.
- Adjust baseline predictive forecasts with known business changes, such as new customers or products, plant shutdowns, acquisitions, etc.
Figure 2: Practical Use Cases for AI-Enabled Planning & Forecasting
AI Expectations vs. Hype
While not yet as widely accepted as the move to the cloud for the financial close and planning processes, AI adoption is already increasing according to the 2021 Data Science and Machine Learning Market Study by Dresner Advisory Services. In 2016, 40% of responding organizations reported using or actively exploring ML. That same metric was about 60% in 2021 (see Figure 3), showing a steady increase over the last five years.
The current economic uncertainties and rapidly changing business requirements will likely be a catalyst to drive adoption up significantly over the coming years.
Figure 3: Dresner Advisory Wisdom of Crowds® Data Science and ML Market Survey
With all the industry buzz, it’s easy to assume that most FP&A teams are already leveraging AI. Surprisingly, they’re not – at least not yet.
Unfortunately, despite excitement across the industry, the adoption of AI and ML in FP&A still lags most functions. Less than 20% of Finance organizations are currently deploying AI, according to the 2021 Dresner Advisory Wisdom of Crowds® Data Science and Machine Learning Market Survey. Why do you think there’s such little adoption?
Introducing the AI for FP&A Blog Series
Here’s my take.
FP&A leaders 1) understand the promise of AI and ML but 2) many FP&A teams don’t yet understand what it takes to deploy AI and ML across enterprise-wide planning processes at scale.
To address this lack of understanding and more, we’ve developed a 3-part blog series for FP&A teams to consider as they begin their AI journey. Here’s a quick summary of our key topics:
- AI for FP&A – What’s Holding Finance Back?
- AI for FP&A – Artificial Intelligence vs. Predictive Analytics
- AI for FP&A – OneStream AI Services for FP&A
Will AI and ML change FP&A forever? I think that’s a stretch. But once organizations can cut through the “buzz” and move beyond the AI hype, FP&A teams will see the light. What do FP&A leaders have to lose by having another point of view on the numbers and KPIs with the help of AI and ML? By having a more insightful dialogue with their CFOs and business partners to collaborate and drive better decision-making?
At OneStream, we call this Intelligent Finance.
To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our blog series or download our interactive e-book here.