Machine learning (ML) has undoubtedly revolutionized how data is processed and utilized in the 21st century.  The way ML can detect patterns and connections within vast data sets has made it an indispensable tool across diverse fields, including Enterprise Performance Management (EPM).

As companies continue to embrace ML to enhance planning, adopting a practical sensible approach to ML – one that balances automation with transparency and human insight – has become increasingly important.  After all, effective planning is critical for businesses to remain competitive and adapt to changing market conditions. 

At OneStream, we call this Sensible ML. 

Introducing Sensible ML

OneStream’s Sensible ML for EPM is an approach that seeks to achieve the automation-transparency-insight balance by combining the power of ML with the best practices of EPM.  The result?   A more intelligent and effective planning process.  In this blog post, we will explore three key capabilities that are essential for successful intelligent planning:  strong ML Operations, transparency-fueled data insights and the ability to surface ML model results.

Key Capability 1: Enable strong ML Operations (MLOps) for organizations to develop, deploy and manage machine learning (ML) models at scale

In EPM, one key challenge of implementing ML is the complexity involved in developing, deploying and managing ML models at scale.  Why?  Well, these steps take a significant amount of time.  Having strong MLOps helps overcome that issue.

MLOps

MLOps is a set of practices and processes that helps organizations address ML lifecycle challenges by providing a systematic approach for managing the entire ML lifecycle, from data preparation and model training to deployment and monitoring.  Through MLOps, organizations can automate tasks such as model training, deployment and monitoring, as well as collaboration and communication among cross-functional teams involved in the development and deployment of ML models.  This automation helps organizations accelerate the data pipeline process, reduce errors and rework, and improve the overall reliability and performance of ML systems.  

Most of us remember forecast variance analysis where we had to investigate the basis for a historical number and the assumptions to any adjustments. Without optimized MLOps, this recurring process can be highly challenging since data transparency is a critical result in MLOps.

Sensible ML enables organizations to automate the entire ML lifecycle using AutoML capabilities for data preparation, model training, model deployment, and model health monitoring. Sensible ML automates a lot of the laborious tasks required by data scientists via feature engineering and model selection for each individual forecasted item (see Figure 1).

Figure 1:  Sensible ML Process Flow

Sensible ML excels in MLOps by providing a comprehensive set of capabilities that enable organizations to develop, deploy and manage ML models at scale.

Key Capability 2: Empower data insights transparently to analyze and compare human-generated and ML forecasts

Another key capability of Sensible ML for EPM is transparency.  Without transparency, there would be no way to ensure insights generated by ML models are aligned with business objectives. Lots of ML solutions in the market lack transparency and are viewed as black-box technology.

Why does Finance care?

By having transparency into the decision-making process of ML models, organizations can better understand how ML is generating insights and what factors are driving ML-based recommendations.  This capability enables organizations to compare and analyze human- and ML-generated forecasts, identify anomalies and inconsistencies, and improve the overall accuracy and effectiveness of the planning process.

And Sensible ML enables users to consume models via dashboards and reports by leveraging OneStream, which unifies enterprise planning and data science on a common platform.  Having everything in one place removes the need to push data to third-party tools and, in turn, removes technical debt (se Figure 2).

Figure 2:  Sensible ML Dashboard

The Importance of Controlling Data Management and Reducing Technical Debt

Effective data management enables businesses to make informed decisions based on accurate and reliable data.  But controlling data management is what prevents data movement/risk and reduces technical debt.  Data quality issues are perpetuated by transferring data and can arise when proper data management practices aren’t in place.  These issues can include incomplete, inconsistent or inaccurate data – all of which inevitably leads to incorrect conclusions, poor decision-making and wasted resources.

One of the biggest oversights when dealing with data management is only focusing on the Return on Investment (ROI) and dismissing fully burdened technical debt (see Figure 3).  For example, many Finance teams use performance measurements such as Total Cost of Ownership (TCO) and ROI to qualify that a solution is good for the organization.  But rarely does the organization dive deeper – beyond the performance measurements to include opportunities to reduce implementation and maintenance waste.  Unfortunately, this view of performance measurements does not account for the hidden complexities and costs associated with the negative impacts of data movement.

Technical debt is MORE than the TCO.

Figure 3:  Technical Debt Is More Than the Total Cost of Ownership

Sensible ML within OneStream provides transparency to analyze and compare human-generated and ML forecasts or augment ML forecasts to empower data insights and strategic decisions.

Key Capability 3: Utilize model results in OneStream and downstream processes

The final key capability of Sensible ML for EPM is the ability to surface ML model results within OneStream.  With Sensible ML, organizations can generate forecasts and insights using machine learning algorithms and then seamlessly leverage or compare the results in existing planning and analytics processes within the OneStream platform.

Human intuition or “tribal knowledge” known within the organization can also augment ML forecasts to create an even more accurate prediction.  For example, a global retailer can adjust forecasts in specific regions to align with cultural preferences and local events within that region.  This type of augmented ML forecast can then be used to drive downstream processes, such as labor or production planning.

The unification of ML with EPM allows for a more robust and streamlined planning process –one that increases accuracy, efficiency and resiliency to an organization’s financial and operational planning processes, from start to finish.  This seamless blend of ML within EPM helps reduce errors and inconsistencies that can arise when using multiple systems.  And that’s key to successful Intelligent Planning.

Conclusion

Sensible ML for EPM is a powerful approach that combines the best practices of EPM with the power of ML to create a more intelligent and effective planning process.  By implementing the key capabilities of MLOps, transparency and utilization, organizations can leverage the power of ML to get all the benefits of Intelligent Planning – ultimately enhancing the planning process and improving decision-making across the organization.

Learn More

To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our Sensible ML blog series or download our white paper here.

Download the White Paper

Machine learning (ML) has no doubt revolutionized how to handle data in the 21st century.  Thanks to the ability to identify patterns and relationships within vast amounts of data, ML has become an essential tool in various fields, including Enterprise Performance Management (EPM).

Traditionally, technology limitations constrained how EPM could be used to monitor, analyze and manage business performance.  EPM involves budgeting, forecasting, financial consolidation, reporting and more. Today, ML can significantly improve the accuracy, transparency and agility of EPM processes.  How?  By automating these activities and providing insights previously impossible to obtain.

Creating Accurate, Transparent & Agile ML-Driven Forecasts

As we shared in the first post of the Sensible ML for EPM blog series, today more than ever, organizations are looking to become more accurate, transparent and agile with their financial plans to stay competitive.  And OneStream’s Sensible ML can help.  How?  It allows users closer to the business to infuse business intuition into the model, which can increase accuracy and ensure all the available information is considered.

Unlike the forecasting capabilities of “most” predictive analytics (which look at prior results and statistics and then generate forecasts based on past events), Sensible ML has unique sophistication.  Sensible ML also considers additional business intuition, such as events, pricing, competitive information and weather to help drive more precise/robust forecasting (see Figure 1).

Sensible ML’s speed in responding to evolving business environments offers a clear advantage over traditional approaches.  While a statistical-based system means planning teams often wait several weeks – or months! – for the financial and non-financial results needed to produce forecasts that respond to changes, Sensible ML can achieve the same result much, much faster.  And it does so with a massive reduction in manual effort. 

Increased Forecast Accuracy = More Effective Business Processes Downstream

Forecasting is a critical activity that helps companies predict future demand, mitigate potential risks and capitalize on emerging opportunities.  Due to the increasingly volatile environment, however, businesses are forced to depart from traditional forecasting methods, siloed processes and legacy technologies. Instead, businesses are focused on digitally evolving their forecasting capabilities and operations, aiming to mitigate the risk of continued value leakage throughout the company.

One of the most significant benefits of applying machine learning to EPM is that ML helps improve the accuracy of financial forecasts and predictions.  Machine learning algorithms can analyze historical financial data and identify patterns that can be used to make more accurate predictions about future performance.

For example, a machine learning model can analyze data from sales transactions, inventory levels and customer demographics to identify patterns that can be used to predict future sales.  By using these predictions to adjust resource allocation and inventory management, organizations can improve their financial performance and reduce the risk of stockouts or overstocks.

Machine learning can also help improve the accuracy of financial reporting.  For example, ML algorithms can be trained to analyze financial statements and identify errors or discrepancies potentially missed by human auditors.  Automating this process helps organizations improve the accuracy of their financial reporting and reduce the risk of non-compliance.

Transparency Is Critical for the Adoption of ML Forecasts for all Stakeholders Involved

Machine learning is frequently referred to as a black box – data goes in, decisions come out, but the processes between input and output lack transparency.

Many solutions, especially those reliant on integration with a third-party ML solution, simply allow an organization to run the ML process.  The results then get returned with no ability to understand how they were generated.

Consequently, many ML solutions now face increased skepticism and criticism as people question whether their decisions are well-grounded and reliable.  Thus, the “transparency and traceability” of ML solutions are becoming increasingly important.

Sensible ML delivers both, improving the transparency of financial and non-financial reporting.  By analyzing data from multiple sources, Sensible ML models provide a comprehensive view of an organization’s financial health (see Figure 2).

For example, machine learning can analyze data from financial statements, sales transactions and inventory levels to provide a more accurate picture of an organization’s financial performance.  This comprehensive view can help identify areas where resources may be misallocated or opportunities for growth that may have been overlooked.

Machine learning can also be used to improve the transparency of financial audits.  By automating the audit process, ML algorithms can identify potential errors or discrepancies more quickly and accurately than human auditors.  This capability not only helps reduce the risk of fraud or other financial improprieties but also improves the accuracy of financial reporting.

Agility Increases More Avenues of Value Creation in Response to Changing Conditions

As the pace of change increases – and disruption and uncertainty become more commonplace –organizations must increasingly not only recognize the signs that indicate change but also put in place a plan to react to the possible scenarios that result from any changes.  ML-enriched forecasts provide a consistent process, framework and collaborative environment that enables organizations to react with agility and certainty in the face of uncertainty and constant change and disruption.

Applying machine learning to EPM comes with a significant benefit:  ML can help organizations be more agile.  By processing and analyzing data in real time, machine learning models can provide insights that enable decision-makers to make faster, more informed decisions.

Machine learning can also help organizations be more agile in financial planning and forecasting.  By analyzing data in real time, ML models can identify changes in market conditions or customer behavior that may impact financial performance.  This capability enables organizations to adjust their financial plans and forecasts quickly and stay ahead of potential challenges.

Sensible ML Makes Forecasting Easy

Sensible ML makes forecasting easy because OneStream breaks down the barriers that have traditionally held back Finance and Operations teams and others from embracing ML within core planning processes.  While ML has powerful potential to help scale work like never before, organizations face several challenges when using traditional machine learning (see Figure 3).

Sensible Use Cases Foster Success

Sensible ML enables organizations to more quickly and accurately foster success with the following use cases (see Figure 4):

• To support strategic planning processes, Sensible ML makes it easier to create the 3- or 5-year (or longer) forecasts often needed to provide greater clarity of vision and a roadmap to support the intent of the company, identify strategic choices, ensure clear direction and drive competitive growth.
• To support Annual Operating Planning (AOP) or forecasting processes, Sensible ML can help lines of business better translate “top-down” financial goals into granular, “bottom-up” monthly plans across product categories, sales channels and customers – which can result in hundreds of forecasts.
• To support daily or weekly Demand Planning and/or Sales & Operations Planning (S&OP), Sensible ML can help demand planners, business analysts and/or Finance business partners create granular-, product- and location-level forecasts aimed at guiding tactical staffing, procurement, logistics and inventory management decisions. 
• For revenue expenses or workforce planning where monthly forecasts per target (e.g., 60 data points per forecast target) are required for top-down planning. Sensible ML can create predictive/statistical forecasts.
• For more granular, bottom-up forecasting by customer, product by location and/or S&OP where organizations can share hundreds of data points per target, Sensible ML can create weekly or daily forecasts that account for specific intuition from the business analysis on impacts such as holidays, weather, pricing changes, competitive impacts or any time-based intuition.

Conclusion

Machine learning is here to stay.  Accordingly, the Office of the CFO should now be looking to take advantage of Sensible ML and similar advancements in technology.  What do FP&A leaders have to lose by adding another point of view or enriching their insights with the help of ML?  Nothing, nothing at all.

At OneStream, we call this Intelligent Finance.

Learn More

To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our Sensible ML blog series or download our white paper here.

Download the White Paper

In the world of corporate performance management (CPM), organizations are constantly looking for ways to improve financial planning and analysis (FP&A) processes.  And in recent years, one growing trend focuses on incorporating machine learning (ML) and artificial intelligence (AI) into CPM solutions to enhance efficiency and accuracy.  Why?  Well, ML can help Finance teams make more accurate and reliable forecasts, automate repetitive tasks, and identify patterns and trends that might otherwise go unnoticed in traditional analysis. 

Artificial Intelligence – From Hype to Reality

Unfortunately, AI has long been surrounded by hype, false starts and disappointing results.  But the business community has finally started to see the advantageous outcomes of AI materializing.  How?  Three trends are making ML forecasting easy for FP&A teams. 

Trend 1: AI plays an increasing role in FP&A

Many sophisticated organizations are still using traditional processes to produce forecasts.  Specifically, those organizations take the previous year’s numbers, apply a blanketed percentage to them and complete the forecast with a few one-off adjustments.  This process is something I like to call “a finger in the air” approach.  Why?  Well, incorporating all the drivers that might impact predictions is far too complex and time-consuming.  Despite the ability to manage two dimensions (e.g., sales based on day of the week or sales based on the weather), humans can sometimes struggle with more complex scenarios with numerous variables (see Figure 1).

When incorporating additional drivers that impact sales, such as holidays, prior year sales, unemployment, etc., humans struggle to simultaneously process all these variables. Therefore, the default method to forecast is to use the aforementioned traditional method – taking last year’s numbers and making a few adjustments. In other words, making a high-level estimate without factoring in critical drivers influencing the forecast, hence the term “a finger in the air.”

How can ML solve the above  limitation?  ML can be particularly helpful in such situations.  Specifically, it can learn and identify complex interactions and non-linear relationships between variables to provide more accurate and reliable forecasts.  By leveraging ML’s ability to process multiple dimensions, organizations can gain deeper insights into operations and make more informed decisions. In addition, by aiding in the recognition of patterns and trends that may have been overlooked in traditional analysis, ML can facilitate more accurate forecasting.

AI therefore has the potential to revolutionize FP&A.  With the help of AI-powered tools, Finance teams can reduce errors and save time by automating repetitive tasks (e.g., data collection, reconciliation and report generation). 

A study conducted by MIT^[1] found that the number of executives who felt AI was a critical part of the Finance function would increase from 8% to 43% in only 3 years  (see Figure 2).

In today’s data-driven business landscape with rapidly increasing data volumes, companies must remain agile to stay competitive.  ML provides the tools to effectively process and make sense of vast amounts of data.  With ML, businesses can stay ahead of the curve in a constantly evolving environment.  FP&A must therefore embrace ML to remain competitive, effectively manage data and gain a strategic advantage.

Trend 2: Demand grows for pre-configured FP&A models

As the demand for efficient and accurate financial analysis grows, more organizations are turning to pre-configured FP&A models to expedite the process and reduce costs.  These models offer organizations the ability to analyze financial data quickly and easily without needing to create a custom solution from scratch.

How?  When ML is democratized to a broader group outside data scientists, business analysts can create, consume and maintain models without relying solely on the Data Science team.  Many more business analysts than data scientists are available today.  And as a result, organizations can now have several users capitalize on Auto ML capabilities to process large volumes of data and create hundreds or thousands of forecasts – at scale and at any level of granularity.  Business analysts can then trust the models built and be strategic business partners who confidently drive financial and operational results within the business.

With the increasing demand for real-time insights and quick, informed decision-making, democratized ML within FP&A models is quickly becoming a popular choice for organizations seeking to improve their financial plans.

Trend 3: Strategic simulations become a business-critical activity

The business environment has become increasingly complex, so strategic simulations have become a crucial tool for businesses to understand the potential outcomes of different scenarios.  By running simulations, businesses can evaluate how different decisions impact financial performance and then identify the best course of action.

And with packaged Auto ML capabilities built directly into the CPM system, Finance teams can quickly generate multiple scenarios while flexing various drivers.  This quick, iterative process allows Finance to identify risks and opportunities, improve cross-functional collaboration, and increase forecast accuracy to drive better and faster strategic decision-making.

OneStream’s Sensible Machine Learning addresses all three trends mentioned above in a single, unified CPM solution.

Introducing Sensible ML

Sensible ML is OneStream’s Auto ML forecasting solution and includes the first and only time series ML capability built inside of CPM.  Auto ML encapsulates the full data pipeline inside CPM, from data ingestion and quality, feature generation, and model building all the way through to dashboard analytics – all in a single platform.  

As a result, OneStream’s Sensible ML not only simplifies ML processes to make more data and insights accessible to Finance and Operations teams, but also means much faster forecast iterations than would otherwise be possible (see Figure 3).

In sum, ML is transforming the CPM landscape by improving budgeting cycles and reinventing the role of the Office of Finance – and this transformation is just the beginning. 

Conclusion

Today, productized ML is necessary for businesses to gain a competitive advantage in a constantly evolving environment.  And as the world becomes more data-driven, the demand for efficient and accurate forecasts will only continue to grow.  An Auto ML forecasting solution that encapsulates the full data pipeline inside of CPM is therefore becoming a popular choice for organizations.  The need to create forecasts in a timely, efficient way at scale is paramount for organizations to effectively stay ahead of the competition.

Learn More

Sensible ML within OneStream can act as a copilot in managing your organization, driving faster and more accurate decisions. To learn more about how OneStream’s unified Auto ML can benefit your organization, check out our Sensible Machine Learning for CPM White Paper.

Download the White Paper

Scenario planning is a valuable tool for businesses looking to prepare for the unexpected, but creating accurate scenarios can be a complex and time-consuming process. Traditionally, these exercises required substantial iterative cycles and were very manual.

That’s where artificial intelligence (AI) and machine learning (ML) forecasting come in – these technologies can help businesses power their scenario plans with more accurate and reliable data, allowing them to make better-informed decisions and stay ahead of the curve.

Powering Scenario Plans with AI & ML Forecasts

Scenario planning involves creating multiple possible futures for a business, considering a range of different variables such as market trends, consumer behavior, and technological advancements. The process typically involves identifying key drivers of change, developing a range of plausible future scenarios, and assessing the potential impact of each scenario on the organization.

The goal is to identify potential risks and opportunities and prepare accordingly rather than simply reacting to events as they happen. Scenario planning can help organizations make more informed decisions by enabling them to anticipate potential future events and develop strategies to mitigate risks and take advantage of opportunities. (see figure 1)

Scenario planning involves creating multiple possible futures for a business, considering a range of different variables such as market trends, consumer behavior, and technological advancements. The process typically involves identifying key drivers of change, developing a range of plausible future scenarios, and assessing the potential impact of each scenario on the organization.

While scenario planning can be a powerful tool, creating accurate scenarios can be a challenge. Traditional scenario planning methods can be time-consuming and challenging to execute. One of the main challenges is forecasting. Forecasting involves predicting future events, such as changes in consumer behavior, market trends, and technological advancements.

Traditional forecasting methods often rely on historical data and expert opinions, which can be unreliable and may not reflect current market conditions or emerging trends. Additionally, traditional forecasting methods may not account for the complex interrelationships between different factors that can influence future events. It’s difficult to predict exactly how different variables will interact, and human biases can creep in, leading to scenarios that are overly optimistic or pessimistic.

That’s where AI and ML forecasting comes in.

The Role of AI and ML in Scenario Planning

Advances in AI and ML have made it possible to enhance scenario planning by providing more accurate and reliable forecasts. AI and ML can analyze vast amounts of data and identify complex patterns and relationships between different factors. This can enable organizations to develop more sophisticated and accurate forecasts that reflect current market conditions and emerging trends.

By incorporating AI and ML forecasting into scenario planning, businesses can create more realistic and useful scenarios, helping them to make better-informed decisions and stay ahead of the curve.

Data analysis

AI and ML can help organizations analyze large amounts of data and identify patterns and trends that are not visible to humans. This can provide insights into potential future scenarios and help organizations prepare for them.

Use Case: Enrich Data to Identify Patterns

AI and ML can be used in scenario planning by incorporating external data sources, such as social media, news articles, and weather forecasts to help understand to what extent these factors correlate with forecast performance.  By analyzing these sources in real time, organizations can identify emerging trends and adjust their scenarios accordingly. (see figure 2)

For example, a manufacturer might use AI to analyze social media conversations about its products and identify emerging customer preferences. By incorporating this information into its scenarios, the manufacturer can adapt its product development and marketing strategies to meet customer needs better.

Prediction

AI and ML can be used to predict future outcomes based on historical data. This can help organizations identify potential future scenarios and make informed decisions about how to respond to them.

Use Case: Predicting Consumer Behavior

One key variable in many scenarios is consumer behavior. Businesses need to understand how consumers will respond to new products, changes in pricing, and other factors in order to make informed decisions. AI and ML forecasting can be used to analyze consumer data and predict how consumers will behave in the future. This information can be used to create more accurate scenarios and identify potential risks and opportunities. (see figure 3)

For example, consider a retail company that is considering launching a new product. By using AI and ML forecasting to analyze consumer data, the company can predict how many units of the product it’s likely to sell in different scenarios. This information can be used to create different sales forecasts for different scenarios, allowing the company to prepare accordingly.

Simulation

AI and ML can be used to create simulations of potential future scenarios. This can help organizations understand the potential impact of different decisions and prepare for them accordingly. (see Figure 2)

Use Case: Forecasting market trends

Market trends are another important variable in scenario planning. Businesses need to understand how the market is likely to change in the future in order to make informed decisions. (see figure 4)

For example, consider a financial services company that is creating scenarios for the next five years. By using AI and ML forecasting to analyze market data, the company can predict how interest rates, inflation, and other key variables are likely to change over that time period. This information can be used to create different economic scenarios, allowing the company to prepare accordingly.

Optimization

AI and ML can be used to optimize scenarios by identifying the most likely outcomes and helping organizations prepare for them. This can help organizations be more effective in their scenario-planning efforts.

Use Case: Predicting Supply Chain Disruptions

Supply chain disruptions can have a significant impact on businesses, especially those that rely on just-in-time inventory or complex global supply chains. AI and ML forecasting can be used to analyze supply chain data and predict where disruptions are most likely to occur. (see figure 5)

For example, imagine a manufacturing company is creating scenarios for the next year. By using AI and ML forecasting to analyze supply chain data, the company can predict where disruptions are most likely to occur – for example, due to natural disasters or political unrest. This information can be used to create different scenarios for supply chain disruptions, allowing the company to prepare accordingly.

In each of these examples, AI and ML forecasting allows businesses to create more accurate and realistic scenarios, helping them to make better-informed decisions and stay ahead of the curve.

Conclusion

AI and ML technologies have been a catalyst for organizations to relook at how they leverage scenario plans, the pace at which they plan decisions, and the data they use to make those decisions. Customers can overcome the tedious and time-consuming scenario planning by enriching the process with AI and ML solutions by providing faster, more accurate and reliable forecasts.

Learn More

To learn more about how FP&A teams are moving beyond the AI hype to enrich scenario planning, check out our white paper, Sensible Machine Learning for CPM – Future Finance at Your Fingertips.

Download the White Paper

Conquering Supply Chain Disruptions

Prior to the supply chain disruptions of recent years, Polaris Inc., a leading provider of powersports equipment, forecasted production and shipments based on innovation and market demand. However, since these disruptions occurred, the business environment became constrained by supply. Recognizing a need for more speed and agility across planning processes, the Polaris Finance team turned to the power of OneStream’s Sensible Machine Learning (Sensible ML) solution to assist with demand forecasting.
In previous years, Polaris’ business units had relied on a highly manual financial planning model with inputs such as SIOP-generated shipped unit forecasts by product, product costs and MSRPs, freight cost, and dealer discounts to arrive at a gross margin view. This model was referred to as the “Driver-Based Revenue Model,” and it provided the perfect opportunity to incorporate machine learning-driven forecasting and transition to a unified planning process within OneStream.

Putting Sensible ML to the Test

Polaris decided to focus their Sensible ML project on their North American Off-Road Products GBU, looking at a 12-month forecasting time horizon with a focus on variables impacting their Shipped Units forecast. These variables included Commodity Prices, Presold Orders, “Clean Build” Percentage and Build-to-Ship Durations. Historic data representing these variables would be combined with historic shipped units to generate the ML models and their forward-looking forecasts.

The historic data model covered 181 products, with weekly units sold from 2016 through 2022. Sensible ML crunched through this data, combined with commodity prices for steel and aluminum, factored in events such as holidays, and generated over 2,800 models for comparison. The OneStream ML models proved to be the most accurate, based on the historic data. The ML forecasts were run monthly and were incorporated into a driver-based forecast.

Faster, More Accurate Forecasts and More

The results were impressive. Not only were the forecasts more accurate than with prior approaches, but with Sensible ML, Polaris added speed and efficiency to their forecasting processes, reducing forecasting cycles from days to hours. Polaris also now has more transparency into what’s behind the ML models, including insights into the key forecast drivers for more informed decision-making.

It provides a finance-run ML forecasting process that integrates seamlessly across planning and forecasting processes in the same user experience used for financial close and consolidations, account reconciliations and reporting.    

“The ability to quickly generate driver-based forecasts is essential to adapting to our changing business conditions,” said Melanie Hermann, Director, Finance Process & Systems at Polaris Industries. “Incorporating AI into our planning and forecasting through the OneStream Sensible ML solution accelerates the forecasting process and further elevates it with powerful ML data-driven forecasts. Sensible ML forecasts have shown to be more accurate, and the Value-Add Dashboard provides the business users with insights into the key features driving the forecast to easily manage, improve and enhance the model.”

The Polaris Data Science team was impressed with the process and results. “Sensible ML commoditizes the part of my job that can be commoditized and allows me to focus on where I can add value… with the output that Sensible ML provides,” said Luke Bunge, Manager Data Science Product. “It’s an incredible timesaver and gets you to the best answer possible. The team did a great job immersing us in the tool…as opposed to turning it into a black box.”

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For companies across fast-changing industries such as CPG manufacturing, retail and hospitality, Sensible ML reduces the traditional barriers to ML forecasting and improves both the speed and accuracy of demand planning.   This enables organizations to fine-tune production plans, optimize inventories as well as reduce volatility and fluctuations in labor planning.

To lean more, download the Polaris Inc. case study and contact OneStream if you are ready to learn how your organization can take advantage of the power of machine learning. 

Download the Case Study

Machine learning (ML) has the potential to revolutionize Enterprise Performance Management (EPM) by providing organizations with real-time insights and predictive capabilities across planning and forecasting processes.  With the ability to process vast amounts of data, ML algorithms can help organizations identify patterns, trends and relationships that would otherwise go unnoticed.  And as the technology continues to evolve and improve, even greater benefits are likely to emerge in the future as Finance leverages the power of ML to achieve financial goals.

Join us as we examine Sensible ML for EPM – Future of Finance at Your Fingertips.

The Shift to Intelligent Finance

For CFOs, whether artificial intelligence (AI) and ML will play a role across enterprise planning processes is no longer a question.  Today, the question instead focuses on how to operationalize ML in ways that return optimal results and scale.  The answer is where things get tricky.

Why?  Business agility is critical in the rapidly changing world of planning.  To think fast and move first, organizations must overcome challenges spanning the need to rapidly grow the business, accurately predict future demand, anticipate unforeseen market circumstances and more.  Yet the increasing volumes of data across the organization make it difficult for decision-makers to zero in on the necessary data and extrapolate the proper insights to positively impact planning cycles and outcomes.  Further exacerbating the problem, many advanced analytics processes and tools only leverage high-level historical data, forcing decision-makers to re-forecast from scratch whenever unforeseeable market shifts hit.

But with AI and ML, business analysts can analyze and correlate the most relevant internal/external variables.  And the variables then contribute to forecasting accuracy and performance across the Sales, Supply Chain, HR and Marketing processes that comprise financial plans and results.

Those dynamics underscore why now is the time for Intelligent Finance.

Over the coming weeks, we’ll share a four-part blog series discussing the path toward ML-powered intelligent planning.  Here’s a sneak peek at the key topics in our Sensible ML for EPM series:

• Part 1:  4 Driving Forces for Change.  These forces have the potential to accelerate ML and move organizations away from descriptive and diagnostic analytics (explaining what happened and why) and toward predictive and prescriptive analytics
• Part 2:  Increase Accuracy and Transparency.  Today more than ever, organizations are looking to become more agile, accurate and transparent with their financial plans to stay competitive.  And Sensible ML can help.  It allows input from users closer to the business to infuse business intuition into the model, which can increase accuracy and ensure all the available information is considered. (see figure 1)

• Part 3:  Key Capabilities for Intelligent Planning.  Sensible ML is unlike “most” predictive analytics forecasting capabilities (looking at prior results and statistics and generating forecasts based on what happened in the past).  Specifically, Sensible MLhas the capability to not only look at prior results but also then take on additional business intuition – such as events, pricing, competitive information and weather – to help drive more precise/robust forecasting.
• Part 4:  Customer Success Stories.  Every customer must be a reference and a success.  OneStream’s mission statement drives everything we do.  Taking a deeper look into customers who have reaped the benefits of leveraging Sensible ML can help any organization gain true predictive insights.

Regardless of where you are in your Finance journey, our Sensible ML for EPM series is designed to share insights from the experience of OneStream’s team of industry experts.  We recognize, of course, that every organization is unique – so please assess what’s most important to you based on the specific needs of your organization.

Conclusion

The aspiration of ML-powered plans is nothing new.  But to remain competitive amid the increasing pace of change and technology disruption, Finance leaders must think differently to finally conquer the complexities inherent in traditional enterprise planning.  ML has the potential to greatly improve EPM by providing organizations with real-time insights and predictive analytics.  However, organizations must overcome challenges (e.g., ensuring good data quality and selecting the right ML algorithm) to achieve success.  As ML continues to evolve, increasingly more organizations are likely to leverage its power to drive better financial and operational outcomes.

Several challenges lie ahead for organizations of all sizes, but one of the most important decisions will be implementing the right ML solution – one that can effectively align all aspects of planning and elevate the organization toward its strategic goals.  Sensible ML answers that call.  It brings power and sophistication to organizations to drive transparency and increase the velocity of forecasting processes with unprecedented transparency and alignment to business performance.

Learn More

To learn more about the value of Sensible ML, download our whitepaper titled “Sensible Machine Learning for CPM – Future Finance at Your Fingertips” by clicking here.  And don’t forget to tune in for additional posts from our machine learning blog series!

Download the White Paper

Artificial intelligence (AI) and machine learning (ML) have revolutionized many industries, but the field of financial planning & analysis (FP&A) has been slow to adopt this technology.  Despite the numerous benefits AI – and more specifically, ML – can bring to Finance (e.g., increased efficiency, accuracy and strategic insights), many organizations still hesitate to implement either in their FP&A processes.  What’s holding FP&A back from reaping the vast benefits of ML?

To answer this question and more, this blog will explore some of the challenges holding FP&A back from fully embracing ML and how those challenges can be overcome.

Market Appetite for ML

While not yet as widely accepted as the move to the cloud for the financial close and planning processes, ML adoption is already increasing, according to the 2022 Data Science and Machine Learning Market Study by Dresner Advisory Services.  In 2016, less than 40% of responding organizations reported using or actively exploring ML.  That same metric was about 70% in 2022 (see Figure 1), showing a steady increase over the last seven years.  On the surface, that progression underscores the AI hype and excitement for the potential benefits of using AI for FP&A.

But what happens if the data gets broken down by function?  A bit of a different reality emerges for the Office of Finance and FP&A.

In fact, the study shows that only 20% (see Figure 2) of Finance organizations are currently using AI and ML, and Finance actuals lag most functions, despite all the buzz and chatter out there.

What’s Holding FP&A Back?

With so much buzz yet low adoption, what key barriers are holding FP&A and Operations teams back from mainstream adoption of ML solutions?  Figure 3 depicts the barriers.

Below, the details about these key barriers show why they’re preventing widespread implementation of cutting-edge ML technologies:

Lack of Expertise

• FP&A teams can struggle to deploy ML in enterprise planning processes, such as rolling forecasting or demand planning, due to lacking the required expertise in ML and machine learning modeling techniques.  Without dedicated business analysts and data science engineers, teams cannot realize the full potential of ML in planning processes.  
• A lack of expertise is especially relevant for organizations with existing ML investments, where the lack of specialized resources hinders the ability of FP&A teams to effectively utilize ML in their work.  Without dedicated expertise or resources, FP&A teams are severely limited in their ability to take advantage of AI and ML.

Lack of Scale

• For organizations with existing data science teams, creating repeatable and systematized ML processes and infrastructure across the enterprise is expensive.  Additionally, most ML capabilities within CPM tools today are only capable of producing a single forecast (target) at a time.  Why does that matter?
• FP&A’s role is to lead and drive effective planning processes across lines of business and functions (e.g., HR, Sales and Operations).  And if unable to empower the organization to forecast at scale, FP&A is not executing as a strategic business partner.  AI solutions for FP&A that cannot scale are no different and will never reach mass adoption, causing the solutions to ultimately fail.

Lack of Business Intuition & Transparency

• Traditional ML modeling processes within CPM tools are often a “black box” to FP&A teams (see Figure 4).  Why does this matter?  Well, ML modes aren’t complete without material business “intuition” and/or offer users only limited visibility or traceability into model inputs and results.  Such limitations can result in a lack of trust or confidence in the results.

As a strategic business partner, FP&A must instill confidence in forecasting processes.  And while leveraging AI and ML is likely to increase forecast accuracy, P&L owners cannot assess the drivers that comprise forecasts – P&L leaders who can’t will never own their forecasts.

And if P&L owners don’t own their forecasts, forecasting processes break down and fail altogether.  That means FP&A has failed too.

Fragmented & Disconnected Processes

• Current AI and ML solutions within CPM typically require data movement and reconciliation between fragmented systems and Finance processes.  Those requirements increase the technical debt and limit the ability for advanced analytics to scale across the organization.
• The fragmented software and technical processes needed for connected planning create added technical complexity and administrative burden for FP&A teams.  This burden includes constantly moving and reconciling data, monitoring data latency, and managing security between fragmented products or models.  Collectively, the burden dilutes the ability of strategic FP&A teams to focus on driving performance and supporting critical decision-making.

Conclusion

Despite these challenges, ML has the potential to significantly improve Finance operations and outcomes.  By automating manual processes, ML can help Finance professionals save time and improve accuracy, which can lead to more effective decision-making.  Additionally, ML can provide real-time insights into financial performance.  Those insights can then help Finance professionals identify trends and make informed decisions.

As AI and ML for FP&A enter the mainstream, organizations will undoubtedly have several choices to consider.  On one spectrum, solution vendors for AI (see Figure 5) are offering everything from AI infrastructure solutions to data science toolkits and complete AI platforms to create and deploy ML models.  While these are powerful tools addressing varying use cases, the tools aren’t designed for FP&A teams.

Corporate performance management vendors are also investing in AI capabilities to support extended planning & analysis (xP&A) processes such as demand planning and sales planning.  As Figure 5 illustrates well for AI vendors, CPM vendors will also solve their customers’ AI needs in different ways.

So then, what’s the lesson in all this?

Don’t let AI hype cloud the evaluation process.  Start with a clear understanding of “what” business outcomes the FP&A team is trying to achieve with ML.  Identify “who” is using the solution and “how” the solution is unified into existing planning processes.

And with answers to these questions in mind, use the evaluation process to “get under the hood” to learn whether the solution will unleash the organization from the key barriers holding FP&A back from moving beyond the hype.

Learn More

Want 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.

Download the eBook

Machine learning (ML) is poised to drive unprecedented changes in forecasting capabilities – faster iterations, the ability to quickly run multiple scenarios, more meaningful outcomes and improved sensitivity analyses.  Traditionally, these exercises required substantial efforts to maintain high-level iterative cycles.  But faster, more accurate iterations with ML could mean reducing the budgeting and forecasting cycle from months to days.

In recent years, advances in ML have spurred a shift away from traditional forecasting, fostering a new age of enterprise performance management (EPM).  And with the newer methods come more accurate plans that provide greater insights into the future.

Sensible ML for EPM – Four Driving Forces for Change

Amid changing times, decision-makers at all organizational levels need the best and most relevant data and intuitive ways of seeing and interacting with that data.  The past few years have undoubtedly shown how unforeseen events can quickly render annual budgets, plans and forecasts obsolete before the new year even starts.  As the years unfolded, organizations had to retool business processes.  How?  One major way was that business functions and teams had to learn to collaborate remotely.  Organizations not only had to reconsider marketing, sales and service strategies, but also had to try new ways of engaging with employees, customers and partners. (see figure 1)

These unanticipated changes have only accelerated digital transformation, a major ongoing trend driving organizations to migrate business processes from being heavily manual and offline to being more automated, agile environments that run on software and increasingly involve cloud computing environments.  Digital transformation is generating more data – and more opportunities – to use data-driven analytics, visualization, artificial intelligence, machine learning and automation.

And over the past decade, the intersection of Data Science and Finance has evolved dramatically.  Yet very few organizations have experienced the full business impact or competitive advantage that comes with advanced analytics, despite significant investments in data science and machine learning.  Why? Well, many tools are too complicated to scale ML, and the necessary Finance-focused skill sets are in short supply.  However, recent technology advancements are poised to significantly impact how Finance teams operate.

In 2023, four driving forces have the potential to accelerate ML and move organizations from descriptive and diagnostic analytics (explaining what happened and why) toward predictive and prescriptive analytics.  The latter forecast what will happen and provide powerful pointers on how to change the future.

Driving Force No. 1: EASY-TO-USE ML TOOLS EMPOWER FINANCE ANALYSTS

Most organizations employ an abundance of financial analysts but only a limited number of data scientists.  Even fewer employ Finance-focused data scientists.  Since most analysts lack the data science expertise required to build ML models, data scientists have become the bottleneck for developing and broadening the use of ML within FP&A.

However, new and improved ML tools are opening the floodgates by automating the technical aspects of data science into easy-to-consume models for Finance.  Finance teams now have access to powerful models without needing to build them manually, and the solutions not only automate multiple steps in the process but also increase productivity.  Specifically, automated machine learning (AutoML) removes the need to manually prepare data and then build and train models. (see figure 2)

Driving Force No. 2: A UNIFIED PLATFORM CLOSES THE GAP BETWEEN ANALYTICS AND ML

Everyone knows data silos exist within and across organizations.  Yet many leaders are not putting the same emphasis on measuring and improving collaboration across the enterprise.  Why?  Well, fragmented solutions cause misaligned technology and forecasting processes, eroding organizational collaboration.  And the level of effort to correct the imbalance can simply feel too steep.

However, few realize that these silos also take the form of “analytics silos,” particularly between Data Scientists and Finance.  Such silos have formed as a result of the different ways the two roles work and their respective skill sets.  But data silos are just one part of the difference.  Data Scientists and Finance use different data (raw versus processed), data sources (data lakes versus databases and files), languages (Python and Java versus C#) and tools (ML versus EPM).

The proof?  Well, that can be found in all the times Finance teams have had to chase monthly files from Sales, HR, Supply Chain and so on, only to find out the provided files are incomplete or have some anomalies that require follow-up.

Sounds familiar, doesn’t it?  Whatever the reason, that process still feels like a waste of valuable time across the organization.  This kind of collaboration isn’t the kind for which organizations strive.  But unfortunately, this world is the one in which most organizations live.

Driving Force No. 3: MANAGING AND DEPLOYING ML FEATURES AT SCALE

For Finance leaders who have interacted with data scientists’ teams to build new ML models, many leave those meetings feeling like nothing has been solved.  Why?  Well, the data science process feels like an arduous task of prepping data, creating features and models, and then trying to make sense of the outputs.  The real challenge, however, is time.  Generally, these projects can take many months – if not years – before a business value is realized.

With advancements in new Low Code/No Code solutions like OneStream’s Sensible ML, Finance and Operation teams can realize value within a few months.  These teams can now easily create, consume and maintain data science models without relying solely on the data science team. (see figure 3)

Driving Force No. 4: UNIFIED CLOUD EXPANDS ACCESS TO NEW DATA

According to Statista, an estimated 97 zettabytes of data were created, captured, copied and consumed globally in 2022.  That amount of data is only expected to accelerate even more.  Over the next few years, global data creation is projected to grow to more than 189 zettabytes by 2025.  And as organizations shift to remote work environments, the pace of acceleration is only going to increase.  Additionally, by 2025, IDC projections, reported by Analytics Insight, also predict that 80% of the world’s data will be unstructured.  That scenario will bring opportunities for organizations to create more meaningful insights, but only if organizations can unify the data.

Here are the top advantages that companies are gaining by leveraging cloud-based systems.

• Always Up-to-Date Solutions:  Because the solution is in the cloud, users always have the most up-to-date version – removing the normally steep maintenance costs that many organizations incurred with prior solutions.
• Scalability and Accuracy:  Management of siloed tools (e.g., spreadsheets) can quickly get out of hand as business needs increase.  For example, many Finance leaders have felt the pressure of trying to explain erroneous data based on manual steps that require individuals to create connected tools.  Cloud solutions reduce or, in many cases, eliminate silos and error-prone manual steps.
• Real-Time Reporting:  When business managers use siloed tools to gather data, getting a quick, accurate, up-to-date view of how the business is performing is difficult.  As a result, leaders may not spot issues early enough to prevent them from developing into bigger problems.  Unified cloud systems solve this challenge by providing real-time access to centralized financial and operational data

Conclusion

That traditional forecasting is excessively manual and prone to human biases is no secret.  Accordingly, the power of machine learning for EPM – such as Sensible ML for OneStream – is clear.  Customers can overcome the high barrier of entry into machine learning with an easy-to-use ML with a single unified Intelligent Finance Platform that can support corporate standards.

Learn More

To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our Sensible ML blog series or download our solution brief here.

Download the Solution Brief

Office of Finance Evolution with Unified Connected Planning

The Office of Finance is evolving from a static reporting function to a high-performing team that helps organizations maximize business impact.

But while the promise of extended planning & analysis (xP&A) is exciting, most organizations fail to launch.  Why?  Because as Finance teams evolve planning processes into Sales Planning, Workforce Planning and Supply Chain Planning, organizations must contend with high-volume, high-velocity granular data, disparate data sources and often siloed planning processes.  So how can Finance teams rise above these challenges and realize the vision of this evolution?

In a recent webinar with our partner Strategic iQ, we discussed the answer to that question.  Strategic iQ helps clients transform their business operations by leveraging proven project management methodologies, effective change management techniques and our functional expertise to streamline operations, reduce cycle times and deliver actionable insights.

The webinar explored how organizations are unifying Finance and operational planning – at scale – to achieve accurate and rapid business decisions.  In this discussion, Scott Stern, VP of Product Marketing & Strategy at OneStream, and Ken Dowd, Principal of Strategic iQ, first discuss the challenges Finance teams face while navigating this transformation and then illustrate, with a live demonstration, how the OneStream platform enables this evolution.

Leading @ Speed to Conquer Market Dynamics

Mr. Stern opens the webinar with a discussion of the market dynamics that require CFOs to make data-driven decisions while keeping pace with change and maintaining data quality, control and compliance.  Next, he describes not only the importance of aligning Finance with Operations to formulate accurate plans across the organization, but also the challenges organizations face with this alignment.

What are these challenges?

Mr. Stern describes two key challenges:  how organizations have access to ever-increasing data sources and how the data available to organizations is increasing in both velocity and volume.  Unfortunately, to quote a modern adage, many organizations now find themselves “data rich and information poor.”  Another challenge Mr. Stern describes is how Finance teams struggle to maintain financial data quality and control when much of operational planning is done in siloed spreadsheets or connected planning tools.

Next, Mr. Stern describes how OneStream solves the challenge of aligning Operations and Finance with a unified platform that empowers Finance teams to maintain control and visibility of operational and financial data across the entire organization.  OneStream provides a single platform that Finance teams and operational units employ for unified business planning – with all relevant data accessible directly within the platform (see Figure 1).  In other words, the platform gives Finance teams complete visibility into operational planning, down to even the most granular levels and calculations.

To address the challenge of rationalizing the increasing volume and velocity of data, Mr. Stern shares how OneStream’s Sensible Machine Learning (ML) brings data science automation into the platform (see Figure 2).  This capability elevates the Office of Finance with built-in data science and enables Finance teams to build, optimize and maintain very sophisticated planning models.  And Finance teams accomplish this elevation while gracefully leveraging the financial and operational data in the OneStream platform to engage in insightful forecasting.

Unified Business Planning in Action

Mr. Stern then turns the presentation over to Mr. Dowd, who presents a live demonstration of Strategic IQ’s unified business planning solution.

Using an example automotive parts supplier organization, Mr. Dowd illustrates how OneStream’s unique capabilities – which include data blending, extensible dimensionality, interactive dashboards (see Figure 3) and financial signaling – all come together to empower Finance teams as business decision leaders through accurate and rapid forecasting.

In the demonstration, Mr. Dowd shows how OneStream eliminates siloed planning with disconnected spreadsheets and planning tools to enable planning agility and control across the organization.  He also explains how Finance teams can accomplish rich variance analysis by drilling down to granular operational data when formulating plans.

Mr. Dowd then breaks down how OneStream’s data blending marries operational and financial data while maintaining dimensionality to accomplish standardized operational-level planning and maintaining corporate-level visibility and control.  Additionally, he demonstrates how other data sources (e.g., third-party demand forecasts) can be incorporated in the platform to enhance planning accuracy.

With these capabilities and the seamless integration of detail data sources, as Mr. Dowd explains, Finance teams can forecast with greater accuracy and are no longer limited to quarterly forecasts.  OneStream enables Finance teams to forecast much more frequently.

Customer Success Wrap-Up

Mr. Stern and Mr. Dowd concludes the presentation with a customer success story focused on Sensible ML-powered unified business planning (see Figure 4).

Mr. Stern explains how this global auto supplier is unifying its planning processes across Finance, SCIOP and Sales and then reviewing part-level costing in OneStream.  Here are just a few of the key benefits that add business value:

• Sensible ML automatically seeds forecasting for shipped unit forecasting at the customer, product family, and part # level
• Sensible ML forecasts achieve greater than 10% improvement in accuracy
• Forecast accuracy helps reduce forecast bias and improves operational stability, reducing fluctuations in downstream labor planning and costs

Learn More

Unifying connected planning processes is no longer just an aspiration.  OneStream’s AI-powered platform brings the connected planning vision to reality.  And with the platform, organizations are unifying business planning across the organization to enable more confident decision-making and maximize business impact.

Want to learn more about OneStream’s unified planning capabilities?  Watch the webinar replay here, or contact us for a demonstration.­

Watch the Webinar

As the promise of Artificial Intelligence (AI) within corporate performance management (CPM) moves from fiction to fact, many FP&A teams are asking the same basic question.  What do AI and Machine Learning (ML) mean for me?

To answer this question and more, our AI for FP&A blog series is designed to help organizations prepare for the AI and ML journey and move beyond the hype.  Where to begin?  Well before jumping into any new journey, it’s critical to chart the course to anticipate what’s in store on the road ahead.  And for a topic as exciting and overhyped as AI, any new journey must begin by considering the key factors that have traditionally held Finance back from AI adoption.

Market Appetite for AI and ML

As we shared in the first post of the AI for FP&A blog series, about 60% of organizations are using or actively exploring ML according to the 2021 Dresner Advisory Wisdom of Crowds® Data Science and Machine Learning Market Study (see Figure 1).  On the surface, the progression over the last 5 years underscores the AI hype and excitement for the potential of AI for FP&A.

But when one breaks the data down by function, a bit of a different reality emerges for the Office of Finance and FP&A.

The study shows that only 20% (see figure 2) of Finance organizations are currently using AI and ML, and Finance actuals lag most functions despite all the buzz and chatter we all hear about.

What’s Holding FP&A Back?

With so much buzz and such little adoption, let’s examine the key barriers holding FP&A and Operations teams back from mainstream adoption of AI and ML solutions (see figure 3):

Lack of Expertise

• If FP&A teams believe that they themselves are required to learn AI and machine learning modeling techniques, chances are FP&A teams will have trouble deploying AI across enterprise planning processes.  This includes processes such a rolling forecasting or demand planning where there could be hundreds or thousands of unique forecasts.  Further, for organizations with existing AI investments, FP&A teams generally lack dedicated business analysts and data science engineers required to build ML models.

Without dedicated expertise or resources, FP&A’s ability to take advantage of AI and ML is severely limited.

Lack of Scale

• For organizations with existing data science teams, creating repeatable and systematized ML processes and infrastructure across the enterprise is expensive.  Additionally, most ML capabilities within CPM tools today are only capable of producing a single forecast (target) at a time.  Why does that matter?
• It’s FP&A’s role to lead and drive effective planning processes across lines of business and functions such as HR, Sales, and Operations.  And if FP&A cannot enable the organization to forecast at scale, FP&A is not executing as a strategic business partner.  AI solutions for FP&A  that cannot scale are no different and will never reach mass adoption and fail.

Lack of Business Intuition & Transparency

• Traditional ML modeling processes within CPM tools are often a “black box” to FP&A teams (See Figure 4).  Why does this matter?  If ML modes are completed without material business “intuition” and/or offer users only limited visibility or traceability into model inputs and results.  Such limitations can result in a lack of trust or confidence in the results.

As a strategic business partner, it’s FP&A’s role to instill confidence in forecasting processes.  And while leveraging AI and ML is likely to increase forecast accuracy – if P&L owners cannot assess the drivers that comprise their forecasts – P&L leaders will never own their forecasts.

And if P&L owners do not own their forecasts, forecasting processes break down and fail altogether which means FP&A has failed too.

Fragmented & Disconnected Processes

• Current AI and ML solutions within CPM typically require data movement and reconciliation between fragmented systems and Finance processes, increasing the technical debt and limiting the ability for advanced analytics to scale across the organization.
• The fragmented software and technical processes needed for connected planning create added technical complexity and administrative burden for FP&A teams.  This burden includes constantly moving and reconciling data, monitoring data latency, and managing security between fragmented products or models.  Collectively, the burden dilutes the ability of strategic FP&A teams to focus on driving performance and supporting critical decision-making.

Conclusion

As AI and ML for FP&A enter the mainstream, organizations will undoubtedly have several choices to consider.  On one spectrum, solution vendors for AI (see Figure 5) are offering everything from AI infrastructure solutions to data science toolkits and complete AI platforms to create and deploy ML models.  While these are powerful tools addressing varying use cases – these tools are not designed for FP&A teams.

Corporate performance management vendors are also investing in AI capabilities to support extended planning & analysis (xP&A) processes such as demand planning and sales planning.  As figure 5 illustrates well for AI vendors, CPM vendors will also solve the AI needs of their customers in different ways.

So what’s the lesson in all this?

Don’t let AI hype cloud the evaluation process.  Start with a clear understanding of “what” business outcomes your FP&A team is trying to achieve with AI and ML.  Identify “who” is using the solution and “how” the solution is unified into existing planning processes.

And with answers to these questions in mind, use the evaluation process to “get under the hood” to learn whether the solution will in fact unleash the organization from the key barriers that are holding FP&A back from moving beyond the hype.

Learn More

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.

We’re proud to announce that our very own CEO, Tom Shea, is now a Forbes Tech Council contributor. This means that moving forward, you can expect to see a regular cadence of Tom publishing articles on Forbes that share his expertise on different forms of technology such as Corporate Performance Management (CPM), Predictive Analytics, Artificial Intelligence (AI), Machine Learning (ML), and more. Below you’ll find his first article where he discusses democratizing AI and ML solutions across organizations to surpass the roadblocks in implementing and adequately scaling the solutions to meet business demands. Keep an eye on his profile for the next one.

Democratizing AI To Transform Your Business in An Unpredictable Future

Artificial intelligence (AI) and machine learning (ML) are by no means new concepts for the office of finance. In fact, 59% of finance execs reported they are already investing in the technology. So, why do organizations still face roadblocks in implementing and adequately scaling AI and ML solutions to meet business demands? Because they are not democratizing the technology across their organization.

AI is no longer just a tool for data scientists. To extract the most value from this technology, companies must make it accessible for employees across different lines of business with varying levels of experience. Read on as I dig into the power of AutoAI and ML technology and how it can help businesses scale ML deployments and better plan for an unpredictable future.

Current Hurdles for Companies Using AI Models

You may be thinking that integrating AI technology into your organization is easier said than done. AI models do present their own unique set of challenges. First, these models are both logistically and algorithmically complex to create. It’s difficult to build a single AI model to solve one problem, and even more so to scale models across multiple use cases.

AI models are living organisms. Once built, they are difficult to maintain and train, especially since models are not, and should not be, generalizable. Because of their unique nature, introducing new and potentially surprising or divergent information, like Covid-19 data, for instance, would require manual input and constant retraining across models. This can seem like an insurmountable time suck on already tight resources.

Not only is this troubling from an operational perspective but also from a talent perspective. As employees increasingly seek new jobs due to burnout, it’s critical that organizations prioritize their workloads. Not to mention, data scientists themselves are in short supply, hard to find, and expensive to retain once you do bring them on board. As a result, the burden of data analysis often falls on the financial and business analysts across the enterprise. They need to understand how to create actionable data insights and make the most of them, but that’s often difficult as these employees do not have the knowledge and expertise to effectively analyze data streams like a data scientist would.

That’s where AutoAI tools can demonstrate their value.

How Organizations Can Use AutoAI and ML Solutions To Solve Business Problems

Introducing and implementing AutoAI services and solutions can solve these ML model creation and deployment problems. And when they are built directly into existing platforms, organizations can create predictive solutions that can scale to enterprise needs without the need for large teams of data scientists. In fact, AutoAI can build thousands of ML models in parallel, which, in turn, results in significant time and cost savings. This is beyond what an individual data scientist could ever accomplish, enhancing overall enterprise operations.

Business users can start leveraging AutoAI models in minutes, providing advanced predictive power for a broad range of staff, not just data scientists. More automation means a reduction in non-value-added work for analysts, allowing businesses to obtain deeper insights and anticipate future challenges and opportunities. This is allowing organizations to solve more problems than ever, even with limited data science manpower to do so.

That said, analyst intuition is still critical, no matter the technology the business is leveraging. AI cannot do it alone. There is a level of human intelligence that is still, and will always be, required. Business analysts can supply their knowledge of business events and other factors and inject this into models to make forecasting and planning more powerful. The possibilities become endless when AutoAI models are combined with business intuition and human insights for more intelligent forecasting and analytics.

Lastly, transparency is key. Financial analysts typically do not have full transparency into the AI and ML models that support their planning, decision-making, and reporting. By ensuring that AI models offer a transparent look into the algorithms and forecast drivers behind the data, end-users are provided deep insights into how different factors impact a model’s performance and can leverage the results with more confidence.

How AI Capabilities Can Help Sectors Recover in An Unpredictable Future

If there is something businesses have become comfortable within the last year and a half, it is unpredictability. With this unpredictability, businesses have learned the importance of consistently being prepared for potential disruption and volatility. When AI and ML are implemented correctly, the insights gained can enable businesses to have a level of resilience, bouncing back and moving forward swiftly. Let’s consider some real-world application examples.

In a restaurant environment, for instance, the right AutoAI solution can enable a chain with many menu items and multiple locations to build thousands of ML models in tandem. These models can then help restaurant managers factor in external data like local events, weather forecasts, mask mandates, and more into their daily planning. This can ensure they are well prepared for situations like outdoor dining or an influx of customers and can equip their staffing and inventory ordering according, ultimately maximizing sales and optimizing expenses.

In the retail industry, AI models can help forecast weekly sales and inventory requirements. In an industry deeply impacted by potential supply chain issues, like the ones brought on by the pandemic, having the power to forecast stock challenges is transformative. This is especially true ahead of high-volume times of the year like the holiday season. AI models help to inform current business decisions, learn from new inputs and help enterprises adapt for what lies ahead.

AI Hype Has Become Business Value

AI and ML are finally transitioning from “hype” conversations to real solutions offering direct business value. Technology is no longer a solution seeking a problem. Targeted AI solutions now solve discrete enterprise challenges and bring transparency back into processes. With AutoAI and ML solutions, enterprise analysts and decision-makers are better equipped to forecast and plan for the future, overcoming seasons of unpredictability that have now become the norm.

Learn More

To learn more about how OneStream is enabling customers to democratize AI in their organizations, check out our August 2021 press release announcing the preview of new AI and ML capabilities at our Splash Virtual Experience.

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 forecasting 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

Conclusion

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.

Learn More

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.