Demand planning is mission critical for any organization because – when done well – projects the business growth potential by anticipating the expected demand to supply. That allows for optimizing inventories and rising customer satisfaction with better service. Traditionally, demand planning sits within Sales or Supply Chain organizations, supporting the S&OP process. But since it plays a key role in the core financial processes, forward-looking Finance leaders should be finding ways to unify demand planning with Finance.

With that in mind, this blog spells out the intersections between Finance and demand planning and describes the technology available to unify demand planning with financial processes.

Understanding Demand Planning

Demand planning is the first step within the demand management process (see Figure 1).

Demand Planning as part of Demand Management
Figure 1. Demand Forecasting and Planning as Part of the Demand Management Process

At its core, demand management represents an effort intended to match market needs with available resources. The demand management process often starts with a forecast analysis. That forecast is then enriched with inputs from Marketing, Sales, Product Development, Business Strategy and – of course – Finance to shape the demand plan. Customer orders (real demand) need to be managed and prioritized, that’s when the rubber hits the road. Finally, demand management is part of the S&OP or IBP process, an ongoing and collaborative process that ultimately helps the organization achieve better results and become more resilient.

The Value of Demand Planning for Finance

In many organizations, the relationship between the Office of Finance and demand planners is unrelated. But it shouldn’t be. Demand planners should get from Finance teams the top-down plans, financial objectives and budget figures to determine the aspiration and constraints. And – at the same time – demand forecasts can be of exceptional benefit for Finance teams.

In many ways, demand management directly or indirectly impacts the core financial processes. So why shouldn’t Finance teams benefit from using demand forecasts and plans as inputs? Those inputs can be used by Finance in many ways:

Organizations that combine financial and demand planning can unlock the value laid out through the scenarios above (and others!). Rather than planning by siloes, organizations can take a systemic perspective to overall planning – to see the forest through the trees, so to speak.

A Unified Platform for Better xP&A

Unifying demand planning with Finance is one of the many advantages an xP&A solution brings.

According to Gartner, “by 2024, 70% of new financial planning and analysis projects will become extended planning and analysis (xP&A) projects, extending their scope beyond the finance domain into other areas of enterprise planning and analysis.” [1]

The Office of Finance can now benefit from new technologies that leverage financial and operational data with machine learning to realize the benefits outlined above. However, not every technology solution is the right fit to support a xP&A structure. Below are factors that Finance should consider when looking for a suitable technology:

Figure 2. OneStream Sensible Machine Learning Solution


Finance leaders must consider demand planning as a powerful tool for driving better results. Why? Because it provides an independent view of the market. Only a single platform with a unified data model and machine learning capabilities can deliver the xP&A requirements to unify demand planning with Finance.

A good example of what’s possible is how Autoliv – a worldwide leader in car safety system – redesigned its core processes to unify consolidation, financial and operational planning to address market uncertainty. Autoliv now leverages machine learning in demand forecasting to increase granularity, reduce forecasting cycles (daily) and bring the cost down– something otherwise not possible without the use of artificial intelligence.

Learn more about how Autoliv leverages Sensible Machine Learning to improve financial and business results:

Download the Case Study

[1] Gartner Research 2020 Strategic Roadmap for Cloud Financial Planning and Analysis Solutions

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 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.

Examples of perception vs true technical debt

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.


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

Organizations everywhere rely on data to make informed decisions and improve the bottom line through data management.  But with the vast amount of data today, things can quickly get out of control and spawn data gremlins (i.e., little pockets of disconnected, ungoverned data) that wreak havoc on the organization.

Remember those adorable creatures that transformed into destructive, mischievous creatures when fed after midnight in the 1984 comedy horror film Gremlins?  (see Figure 1)

Data Gremlins
Figure 1: Warner Bros. Pictures/Amblin E/Sunset Boulevard/Corbis via Getty Images

In the same way, data gremlins – aka “technical debt” – can arise if your systems are not flexible to enable finance to deliver.  Effectively managing data to prevent data gremlins from wreaking havoc is crucial in any modern organization – and that requires first understanding the rise of data gremlins.

Do You Have Data Gremlins?

How do data gremlins arise, and how do they proliferate so rapidly?  The early beginnings started with Excel.  People in Finance and operations would get big stacks of green bar reports (see Figure 2). Don’t remember those?  They looked like the image below and were filed and stacked in big rooms.

Green Bar Reports Stacks
Figure 2: Green Bar Paper

When you needed data, you pulled the report, re-typed the data into Excel, added some formatting and calculations, printed the spreadsheet and dropped it in your boss’s inbox.  Your boss would then review and make suggestions and additions till confident (a relative term here!) that sharing the spreadsheet with upper management would be useful, and voila, a gremlin is born. 

And gremlins are bad for the organization.

The Rise of Data Gremlins

Data gremlins are not a new phenomenon but one that can severely impact the organization, leading to wasted time and resources, lost revenue, and a damaged reputation.  For that reason, having a robust data management strategy is essential to prevent data gremlins from causing havoc.

As systems and integration got more sophisticated and general ledgers became a reliable book of record, data gremlins should have faded out of existence.  But did they?  Nope, not even a little.  In fact, data gremlins grew faster than ever partly due to the rise of the most popular button on any report, anywhere at the time – you guessed it – the “Export to .CSV” button.  Creating new gremlins became even easier and faster, and management started habitually asking for increasingly more analysis that could easily be created in spreadsheets (see Figure 3).  To match the demand in 2006, Microsoft increased the number of rows of data a single sheet could have to a million rows.  A million!  And people cheered!

Excel is not a database
Figure 3: ©Fox Television The Simpsons™

However, errors were buried in those million-row spreadsheets, not in just some spreadsheets, but in almost ALL of them.  The spreadsheets had no overarching governance and could not be automatically checked in any way.  As a result, those errors would live for months and years.  Any consultant who experienced those spreadsheets will confirm stories of people adding “+1,000,000” to a formula as a last-minute adjustment and then forgetting to remove the addition later.  Major companies reported incorrect numbers to the street, and people lost jobs over such errors.

As time passed, the tools got more sophisticated – from MS Excel and MS Access to departmental planning solutions such as Anaplan, Essbase, Vena Solutions, Workday Adaptive and others.  Yet none stand up to the level of reliability IT is tasked with achieving.  The controls and audits are nothing compared to what enterprise resource planning (ERP) solutions provide.  So why do such tools continue to proliferate?  Who feeds them after midnight, so to speak? The real reason is that these departmental planning systems are like a hammer. Every time a new model or need for analysis crops up, it’s “Let’s build another cube.” Even if that particular data structure is not the best answer, Finance has a hammer, and they are going to pound something with it.  And why should they do their diligence to understand the proliferation of Gremlins? (see Figure 4)

Mean Gremlin
Figure 4: Warner Bros. Pictures/Amblin E/Sunset Boulevard/Corbis via Getty Images

The relationship can best be described as “complicated.”  ERPs and data warehouses are secure, managed environments but offer almost no flexibility for Finance or Operations to do any sophisticated reporting or analysis that has not been created for them by IT.  Requests for new reports are made, often through a ticket system, and the better IT is at satisfying these requests, the longer the ticket queue becomes.  Suddenly the team is doing nothing but reporting, and that leaves Finance thinking, “How the heck is there a team of people in IT not focused on making the business more efficient?”

The answer suggested by the mega ERP vendors is to stop doing that.  End users don’t really need that data, that level of granularity or that flexibility.  They need to learn to simplify and not worry about trivial things.  For example, end users don’t need visibility into what happens in a legal consolidation or a sophisticated forecast.  “Just trust us.  We will do it,” ERP vendors say.

Here’s the problem:  Finance is tasked with delivering the right data at the right time with the right analysis.  What is the result of just “trusting” ERP vendors?  Even more data gremlins.  More manual reconciliations.  Less security and control over the most critical data for the company.  In other words, an organization can easily spend $30M on a secure, well-designed ERP system that doesn’t fix the gremlin problem – emphasizing why organizations must control the data management process.

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 gremlins and reduces technical debt.  Data quality issues are perpetuated by the gremlins and can arise when proper data management practices aren’t in place.  These issues can include incomplete, inconsistent or inaccurate data, leading to incorrect conclusions, poor decision-making and wasted resources.

One of the biggest oversights when dealing with data gremlins is only focusing on Return on Investment (ROI) and dismissing fully burdened technical debt (see Figure 5). Many finance teams use performance measurements like Total Cost of Ownership (TCO) and ROI to qualify that the 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 hidden complexities and costs associated with the negative impacts of data gremlin growth.

Technical Debt is MORE than the Total Cost of Ownership

Technical Debt and TCO
Figure 5: Technical Debt is More than the Total Cost of Ownership

Unfortunately, data gremlins can occur at any stage of the data management process, from data collection to analysis and reporting.  These gremlins can be caused by various factors, such as human error, system glitches or even malicious activity.  For example, a data gremlin could be a missing or incorrect field in a database, resulting in inaccurate calculations or reporting.

Want proof?  Just think about the long, slow process you and your team engage in when tracking down information between fragmented sources and tools rather than analyzing results and helping your business partners act.  Does this drawn-out process sound familiar?

The good news is that another option exists.  Unifying these multiple processes and tools can provide more automation, remove the complexities of the past and meet the diverse requirements of even the most complex organization, both today and well into the future. The key is building a flexible yet governed environment that allows problems and new analyses to be created within the framework – no Gremlins.

Sunlight on the Data Gremlins

At OneStream, we’ve lived and managed this complicated relationship our entire careers.  We even made gremlins back when the answer for everything was “build another spreadsheet.”  But we’ve eliminated the gremlins in spreadsheets only to replace them with departmental apps or cubes that are just bigger, nastier gremlins.  Our battle scars have taught us that gremlins, while easy to use and manage, are not the answer. Instead, they proliferate and cause newer and bigger hard-to-solve problems of endless data reconciliation.

For that reason, OneStream was designed and built from the ground up to eliminate gremlins, including the need for them in the future (see Figure 6).  OneStream combines all the security, governance and audit needed to ensure accurate data – and does it all in one place without the need to walk off-prem while allowing the flexibility to be leveraged inside the centrally defined framework.  In OneStream, organizations can leverage Extensible Dimensionality to provide value to end users to “do their thing” without having to push the “Export to .CSV” button.

OneStream Unified Platform
Figure 6: OneStream Unified Platform Capabilities

OneStream is also a platform in the truest sense of the word.  The platform provides direct data integration to source data, drill back to that data, and flexible, easy-to-use reporting and dashboarding tools. 

That allows IT to eliminate the non-value add cost of authoring reports AND the gremlins – all in one fell swoop.

Finally, our platform is the only EPM platform allowing organizations to develop their functionality directly on the platform.  That’s correct – OneStream is a full development platform where organizations can leverage all the platform resources of integration and reporting needed for organizations to deliver their own Intellectual Property (IP).  They can even encrypt the IP in the platform or share it with others.

OneStream, in other words, allows organizations to manage their data effectively.  In our e-book on financial data quality management, we shared the top 3 goals for effective financial data quality management with CPM:


Data gremlins can disrupt business operations and lead to severe business implications, making it essential for organizations to control their data management processes before data gremlins emerge.  By developing a data management strategy, investing in robust data management systems, conducting regular data audits, training employees on data management best practices and having a disaster recovery plan, organizations can prevent data-related issues and ensure business continuity.

Learn More

To learn more about how organizations are moving on from their data gremlins, download our whitepaper titled “Unify Connected Planning or Face the Hidden Cost.” 

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).

Figure 1:  Sensible ML Process Flow

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).

Figure 2:  Sensible ML Dashboard

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).

Figure 3:  Sensible ML Solves for Traditional ML Challenges

Sensible Use Cases Foster Success

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

        Figure 4:  Sensible ML Use Case Matrix


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 previous blog post of this series, we covered why leaning on a truly single platform with an extensible data model is the most effective way to unify business strategy with planning activities across the enterprise.  The prior post also laid out the framework of a unified integrated business planning (IBP) model and identified the hidden costs for organizations that implement IBP built on fragmented tools and spreadsheets.

This final post of the series shows the benefits from an IBP journey and then digs even deeper. Why?  To show how an organization can maximize those benefits when the IBP implementation is underpinned by a single platform with an extensible data model.

Benefits of Unifying “Connected” Business Planning

The benefits an organization can expect from an IBP implementation are diverse. In the big picture, IBP can certainly improve financial and business performance. Figure 1 outlines some of the most remarkable KPI improvements.

Figure 1:  Benefits of Implementing the IBP Process.

The range of improvement organizations claim through those benefits can be substantial, too.  According to McKinsey & Company, “The average mature IBP practitioner realizes 1 or 2 additional percentage points in EBIT.  Service levels are 5 to 20 percentage points higher. Freight costs and capital intensity are 10 to 15 percent lower – and customer delivery penalties and missed sales are 40 to 50 percent lower.  IBP technology and process discipline can also make planners 10 to 20 percent more productive.”  McKinsey & Company also emphasizes the importance of keeping P&L owners involved in the IBP process.

Equally important to those benefits is the technology used.  What’s the advantage of choosing the right technology to support the IBP process?

Simply put, the choice of technology is pivotal for achieving the highest percentile of the benefit ranges. Yet many organizations undervalue the role technology plays in achieving better results.  Instead, those organizations live with sub-optimal IT architectures populated with point solutions, weak integration flows and uncontrollable spreadsheet usage. Those pitfalls only further emphasize why organizations aspiring for excellence should opt for a truly unified platform that covers the breadth of an IBP process.

Going for one platform with the right data integration model not only provides higher business benefits but also results in lower IT costs, frictionless collaboration among teams, more speed in decision-making, enhanced resilience to any changing condition (e.g., market disruptions, growth by acquisition) and less risk.

Maximizing the Value of IBP with One Platform

When one unified platform caters to the needs of integrated business planning, organizations can aspire to get the highest return of value from the IBP process. Having one platform that unifies business strategy with all planning activities, consolidation and reporting provides unmatched levels of performance. And this advantage is exactly what organizations get when choosing to support their IBP journey with OneStream’s Intelligent Finance Platform (see Figure 2).

Figure 2:  OneStream’s Unified Platform Capabilities

OneStream’s Value Realization Report validates the platform advantage. The report details the benefits that adopters of OneStream’s Intelligent Finance Platform achieve across the different domains that pertain to corporate performance management: data management, close & consolidation, account reconciliations, reporting, and planning & budgeting.  According to the report, OneStream simultaneously generated value in four different areas:

  1. Technical Debt (i.e.,difference between current state cost and future state costs.) One platform drastically reduces or eliminates certain technical costs. Those costs include administrator costs, hardware and data center costs, upgrade costs, data warehousing, third-party software to complement or enhance the applications (e.g., machine learning engines, inter-company eliminators, currency converters, etc.), disaster recovery costs and more.   
  2. Effectiveness. Being more effective or making better decisions faster is possible by simply having information readily available at the right time and with the right level of detail. The use of one platform and one data model increases effectiveness significantly. Why? The need for continually copying, moving and reconciling data among different point solutions is eliminated.
  3. Risk Mitigation. OneStream helps avoid costly mistakes caused by manual step errors and the lack of traceability and auditability. The full advantage comes by having full, direct integration with systems such as GL/ERP and other source systems.
  4. Efficiency. According to OneStream’s Value Realization Report, Planning and Budgeting takes less time for OneStream customers.  A customer that moved from a rudimentary Excel®based system to their first budgeting tool saw a revolutionary improvement of 95%. Customers who moved from another budgeting tool that was relatively sophisticated saw between a 10% and 25% improvement in the time they spent on budgeting after implementing OneStream.”  On average, OneStream users see efficiency improvements of 42%.

Data management is massively improved as well.  According to the report, “Customers improved their data management processes, delivering results between 98% improvement when moving from a complex system with several disparate systems and 10% when upgrading from a system that is already fully integrated but needs to take advantage of more fluid flow of data and information.” This improved efficiency ties directly back to the financial and business performance KPIs introduced earlier in this blog post (see Figure 1) – i.e., significant productivity gains, better use of cash, net working capital, better EBIT, revenue and market growth, better service levels and improved DSO, DPO and DIO.


This blog series highlights why current market conditions require new approaches to integrated business planning and why many organizations struggle to implement IBP due to three main challenges: lack of leadership support, organizational resistance and underestimating the technology needs. 

These challenges aren’t insurmountable, however, thanks to advanced technology solutions that truly unify business strategy with planning. And when organizations aspire only to excellence, one platform with a single extensible data model is the key to successful IBP implementations. 

OneStream’s Intelligent Finance Platform delivers in that regard. Its data-first approach to integrated business planning unifies the views of strategy, planning and performance – increasing the speed of decision-making and improving business performance.

Learn More

Discover OneStream’s Intelligent Finance Platform advantage here, and download the Value Realization Report

Download the Value Realization Report

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).

Example charts of drivers impacting sales
Figure 1:  Demystifying ML for Forecasting

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).

Graphs showing survey results
Figure 2:  AI Use in Finance

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).

Figure 3:  Sensible ML Dashboard

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. 


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.

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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.

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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.

Scenario Planning Process
Figure 1: Scenario Planning Process

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)

Sensible ML Feature Library
Figure 2: Sensible ML Feature Library

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.


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)

Sensible ML Prediction
Figure 3: Sensible ML Prediction

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.


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)

Sensible ML Workspace
Figure 4: Sensible ML Workspace

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.


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)

Scenario Planning Sensible ML Analysis Overview
Figure 5: Sensible ML Analysis Overview

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.


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.

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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.

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As stated in our previous blog post titled “Why Is Integrated Business Planning So Hard?,” we examine why unifying integrated business planning (IBP) or connected planning processes enables organizations to ensure they take a data-first approach to all planning activities. Such a planning approach aims to unify business strategy with planning, budgeting and forecasting activity for all business lines and functions – providing one single version of the truth within a single, seamless technology platform and user experience.

A trusted, common view of the numbers provides a robust baseline for agile decision-making and keeps all teams together, collectively trying to achieve the same corporate objectives while staying focused on specific KPIs. In other words, the different teams maintain their independence while working in unison to achieve corporate success by leveraging the same trusted and governed data.

This approach is underpinned on a single technology platform that can manage planning, budgeting & forecasting (PB&F), consolidation and reporting all in one place – without the need to duplicate data or otherwise maintain different solutions. The advantages of this approach are many:

Leading the Change

Intelligent Finance teams lead business planning unification and foster collaboration across the organization. While the teams oversee and facilitate the planning activity, doing so should not suppress the detailed planning required between and by the different business lines and functions (e.g. Supply Chain, HR, IT).

Instead, all planning activities should focus on a central Finance planning capability that orchestrates and aligns data, strategy, processes and people across the different business units and functions (see Figure 1). This central capability is simple to understand when the main mechanisms to show market value and performance against strategy goals are financial artifacts such as P&L, balance sheet, and income and cash statement.

Figure 1:  Finance as the central hub of the organization for integrated business planning

Unfortunately, most options for connected planning, integrated business planning are simply not built for this purpose.  Why? Rather than relying on a truly unified data model, Finance and IT teams are forced to connect plans across systems and spreadsheets by moving and reconciling data. Those processes, in turn, add material risk and cost to integrated business planning efforts.

In other words, true unification matters – a lot.

How to Unify Business Planning

Unified business planning is anchored on 3 key principles:

Figure 2: OneStream’s Intelligent Finance Platform

These principles not only provide a robust foundation throughout the IBP journey, but also facilitate the adoption of technology that truly unifies people and processes.

Unified Integrated Business Planning Model

A data-first approach to integrated business planning unifies the views of strategy, planning and performance, increasing the speed of decision-making.

Figure 3 shows the model for unified business planning platform. In light gray, the figure shows the key processes that must be part of the same platform under one data model to reap the benefits of this approach. The figure also displays a representation of an IBP process with a closed loop between planning and execution – a loop that remains aligned to the business strategy because everything relies on the same data and technology.

Figure 3 Unified Integrated Business Planning Model

Unify Integrated Business Planning, or Face the Hidden Costs

Unifying integrated business planning brings data and people together, helps the organization model the right behaviors, and removes the friction of traditional technology silos and spreadsheets.

Today, Finance leaders have the organizational influence to lead an IBP process based on a unified approach. However, unifying integrated business planning requires one single platform and extensible data model, not an integrated set of connected modules from the same vendor. This approach offers the most effective way to unify business strategy, planning and performance.

By not taking a unified and data-first approach to IBP process implementation, organizations face the hidden costs of dealing with archaic and fragmented technology:

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To learn more about unified business planning, watch this video.

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At its core, strategic planning involves creating a mid-range financial plan for an enterprise to help the business achieve strategic goals and understand the financial impact of those strategies. But before even starting the strategic planning process, an organization must set the goals and targets that will shape the strategic initiatives. Those targets and goals not only shape the strategies that will lead the organization to hit all those targets, but also serve as benchmarks – offering something to measure against as the strategic plan becomes operational. In short, strategic planning ultimately drives capital resource allocation, but the process starts with target setting.

Driving Capital Resource Allocation through Strategic Planning

Some of the most important decisions CFOs and Finance teams make focus on how to allocate limited capital resources.  But what’s the best way to do that?  To start, Finance can generate a 3- to 5-year strategic plan to help make those decisions.  Such planning should not only be integrated with the P&Ls, balance sheets, and cash flow statements, but also clearly model the business as it exists today with possible strategies layered over top.  These strategic plans then determine how capital planning can support strategic initiatives across an enterprise in a manner that best supports the mid-range goals the organization intends to prioritize. 

Potential strategies that can be modeled in a strategic plan include acquisitions and divestitures; debt issuance, share buybacks; projects for IT, capex and marketing; and more (figure 1).  Strategic planning must allow for agile scenario analysis as well.  Such analysis allows Finance to quickly model, evaluate and, if necessary, pivot to alternate scenarios while strategic planning.

Figure 1: Strategic planning feeds the budget and forecasting process as well other financial scenario modeling situations

Before the strategic planning process can take place, then, Finance must set and understand the mid-term organizational goals and targets.  Strategic planning fits into the existing budgeting, planning and forecasting process by driving the budget.  After all, the financial impact of the important strategic initiatives – which impacts both the budget and forecasts – has been modeled and determined during the strategic planning process. 

But how can Finance pick which strategies will ultimately steer the direction of the organization?  The answer is simply: by determining the targets and goals an organization wishes to focus on, Finance can model and implement strategic initiatives that support those goals, based on which initiatives will (likely) yield the best results.

Determining Organizational Goals and Targets Shapes Strategic Plans

Before determining a mid-range strategic direction, an organization must first identify the it’s goals and targets the strategies will address.  Below are some example organizational goals and targets that might shape an enterprise’s strategic direction, broken down into three types of objectives.

Ultimately, understanding what goals and targets an organization wants to prioritize in the mid-term can help determine which strategic initiatives should be focused on.  And that drives the strategic scenario modeling that feeds the eventual budget.

But doing all that also depends on deploying the right tech.

Enabling Effective Strategic Planning with Good Technology

If an organization is determining goals and targets but cannot create a useful strategic plan, the whole effort falls flat.  The process can also get too bogged down with painful, manual processes that rely heavily on manual calculations and disparate spreadsheets.  As a result, the value in the process diminishes. 

But it doesn’t have to be that way.  Technical enablers can help drive a better strategic planning process in the following ways:

Figure 2: Financial signaling enables better strategic planning with daily and weekly financial and operational data

Good strategic planning may start with target setting.  But the planning is kept alive with good technology and processes that support an agile, repeatable process that ultimately drives business value and alignment with business partners and key stakeholders within the organization.


Finance can drive strategic growth and initiatives across an organization by first determining the goals and targets the organization hopes to achieve.  Strategic planning is there critical to help prioritize the key initiatives required to achieve those targets.  While Strategic Planning processes must start with good target-setting, best-in-class Finance teams rely on technology that unifies planning targets across enterprise scenario planning and reporting processes.

To learn more about how to best implement a strategic planning process, check out our solution brief on Conquering Complexity in Strategic Planning.

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Planning for business is becoming increasingly more complex, requiring new approaches supported by sophisticated planning technologies.  Recent research even indicates that business strategy, financial and other planning activities (operational, HR, sales, etc.) are better together.  Thus, Connected or Integrated Business Planning (IBP) has become a hot topic for leaders who want to thrive in this challenging business context.  But why is IBP so hard to implement?

This blog series looks to answer that question by diving into why companies aren’t successful when adopting Integrated Business Planning.  In doing so, the series explores why the CFO is best positioned to lead the change.  Part 1 explores why Integrated Business Planning is difficult.

A Broken Mirror

Connecting or integrating business planning activities across the enterprise is not a new idea.  In fact, concepts like xP&A aim to respond to this need from a technology perspective, and terms like Integrated Business Planning have been around for decades.  However, today’s unique market volatility combined with failed attempts at connected planning shows why the topic needs to be elevated to the C-level.

Organizations encounter various obstacles when implementing an Integrated Business Planning process. And those organizations invest significant resources and time trying to overcome challenges and get the process to work.  Amid a long list of challenges, 3 stand out as the most difficult to overcome:

  1. Leadership skepticism and misalignment around the IBP process.  As Oliver Wight rightfully points out, “one set of numbers” is a stepping stone in a well-deployed IBP process.  However, getting there isn’t easy.  But why?  First, the current state of technology and data strategy in many organizations doesn’t support an IBP process.  Second, some might be uncomfortable with this better reality: the visibility that comes with one view of the numbers can surface human bias, shortcomings or resource buffers that some leaders want to keep hidden.
  2. Culture and organizational change can’t do it alone.  Companies structured in silos fail to connect strategic goals with planning, model the wrong behaviors (conflicting targets, biased assumptions, etc.) and force a fragmented planning approach.  How does all this affect the IBP process implementation?  Well, adjusting the culture and behaviors is hard work, as well as setting up a solid management system and implementing horizontal roles to coordinate planning activities across silos.  This process requires an enormous effort in culture change, and it doesn’t stick in the long term:  organization and culture tend to mold and deform over time in response to leadership changes or strategic direction shifts.  Further, today’s volatile business environment requires unparalleled speed in decision-making that can’t be held up by sluggish changes in governance and organizational design.
  3. Ignoring the technology trap.  Having one technology for all planning is a complex endeavor. Finance planning, business unit planning, sales & operations planning, workforce planning (to name a few) are all processes with different goals, data structures, units of measure, mathematical foundations and needs.  Even if functional technology is in place to handle these different planning needs, scalability becomes an issue when the solutions are exposed to massive amounts of data.

So…what are the options?  Well, many of the solutions out there cannot adapt to current needs due to being made of multiple modules that must be integrated or simply not having the depth and breadth required to support varied planning needs.  The alternative, then, is spreadsheet abuse that’s slow, laborious and prone to error.  And those organizations that manage to integrate all these modules from different software vendors do it at a high cost and effort, living up with an infrastructure that doesn’t scale and a tremendous technical debt.  In other words, the alternative is high RISK and high COST.

Integrated Business Planning is often underpinned by fragmented technologies and siloed organizations, making it hard to compose a trustworthy view of planning and business performance, much like the image reflected in a broken mirror

Since culture change is never easy and most technology can’t address the needs of truly unified planning, leaders are discouraged from embarking on an IBP journey and stall with sub-optimal processes and technologies.

This sub-optimal status often means a higher impact from risks and uncertainty due to a sluggish decision-making process.  Ultimately, that impact translates into the loss of business opportunities and a higher cost of doing business.

A Closer Look at the Technology Trap

Even with strong alignment and commitment around the IBP process, a closer look into the problem shows that organizations struggle to achieve the promised benefits for a specific reason.  Primarily, a consensus among planning activities that effectively links strategic & finance goals with financial and extended planning (xP&A) is complicated when technology isn’t fit for the task.

The Pulse Survey launched by BPM Partners in 2021 (Figure 1) displays some of the main challenges an organization can face with budgeting and planning activities:

Figure 1 BPM Partners 2021 survey: Budgeting and Planning Challenges

Collectively, such challenges are strongly correlated to the flawed technology solutions that organizations use to support these processes.  

Often, many organizations undertake the implementation of IBP from a process and organizational standpoint, leaving the technology discussion for later.

However, if one set of numbers is a non-negotiable in IBP, why not address the technology trap for starters?  Wouldn’t collaboration be easier with a common foundation of data and information?  Wouldn’t it be easier for top leadership to execute flawlessly when all planning is based on the same numbers?  Why wait for a perfectly fine-tuned process when the right technology can accelerate the adoption of IBP?

There’s Another Way

When business planning isn’t unified, the leadership team can’t really get quality insights fast enough to improve the business performance.  Because planning is a cornerstone to budgeting and forecasting processes, both are impacted when the planning processes are carried out in a containerized way supported by inferior technology.  The different departments and functions suffer the consequences of a fragmented planning approach.

Despite the many attempts to join and synchronize all planning activities, these planning processes remain disconnected because they rely on different technologies and systems that cannot provide a common data structure.

But a (better) way forward exists, one where the CFO leads the change by implementing a collaborative planning approach with business lines and other functions.  Whether that occurs through xP&A, integrated business planning or connected planning, ultimately what CFOs really need to do is unify business planning.

By unifying IBP or connected planning processes,organizations ensure they take a data-first approach to all planning activities.  Such planning approach aims to unify business strategy with planning, budgeting and forecasting activity for all business lines and functions – providing one single version of the truth.  That single version is verifiable and certified in just one technology platform.

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Learn how to unify Integrated Business Planning in our next blog:

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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:

Figure 1: Sensible ML Dashboard

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.


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.

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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!

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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.

Figure 1:  Dresner Advisory Wisdom of Crowds® Data Science and ML Market Survey

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.

Figure 2:  Deployment of AI and ML by Function

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.

Figure 3:  AI Barriers to Entry for FP&A

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

Lack of Expertise
Lack of Scale
Lack of Business Intuition & Transparency
Figure 4:  AI in Current CPM Solutions

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


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.

Figure 5:  AI General Vendor Landscape

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.

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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.

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