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.
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.
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.
Finance leaders are transforming the Finance function and extending the value they offer their organizations by leveraging the converging capabilities of digital technology to take advantage of predictive analytics. These technological capabilities provide new access to multiple sources of the vast data within organizations, foster the ability to interpret that data and provide tools that advance Finance transformation.
By leveraging these technologies, Finance leaders are expanding their sphere of influence with Sales, HR and Supply Chain. They’re also strengthening business partnerships and trust while providing deep operational insights and guidance for their organizations. How? By speaking in the language of the business and analyzing key business drivers such as pricing and sourcing that drive profit margin and cash flow.
Many Finance leaders successfully navigate part of this Finance Transformation journey with three steps that enable them to employ predictive analytics to provide both strategic and operational guidance:
- Integrate financial and operational data
- Align predictive analytics directly into key planning processes
- Unleash predictive analytic access across the organization
Let’s examine these steps in a bit more detail to see what’s necessary to ensure success.
Step 1: Integrate Financial and Operational Data
To provide actionable insights, Finance leaders must have access to operational data. Additionally, they must have the capability to examine operational data alongside, and within the context of, financial data (see Figure 1). Sophisticated organizations generate vast quantities of data, so both Finance and business-unit leaders require digital technologies that provide access to these various data sources. For effective decision-making, this access must be efficient.
Access must therefore not require team members to waste valuable time moving and managing data. Additionally, the access must be timely so that up-to-date information is readily available for operational business decisions that have an impact before month-end.
Organizations that fail to align financial plans with granular operational plans will ultimately struggle with forecast accuracy – because they operate in silos. Financially, the impact of that can take many forms and impact both profit and cash generation. Here are just a few examples of impacts:
- Excess inventory from over-forecasting, which impacts the cost to manage inventory (e.g., inventory holding costs) and working capital.
- Lost sales and revenue from under-forecasting. To meet customer demand, products must be in the right place at the right time.
- Lost profit from under-forecasting and rush shipments. To meet customer demand, companies may rush product shipments at a higher cost than planned.
Step 2: Align Predictive Analytics Directly into Planning Processes
No matter where the Office of Finance sits in its Finance Transformation journey, predictive analytics can help focus on collaborating with business partners, find new ways to ask “why” and drive performance. Here are just a few of the top use-cases for organizations thinking about adding predictive analytics and machine learning (ML) into their financial and operational planning processes:
- Assist with target-setting for strategic planning, annual operating plans and rolling forecasting
- Create baseline predictive forecast scenarios for comparison with bottom-up forecasting from divisional Finance or Operational partners
- Automatically seed all or portions of new forecasts with predictive models
- Adjust baseline predictive forecasts with known business changes, such as new customers or products, plant shutdowns, acquisitions, etc.
Access to raw data is ultimately meaningless without the capability to interpret that data. Finance notably lags other functions in the adoption of advanced analytics, according to Dresner Advisory’s 2020 Wisdom of Crowds® Data Science and ML Market Survey (see Figure 2). But modern corporate performance management (CPM) solutions offer capabilities that can empower Finance and business-unit leaders with self-service tools for predictive analytic forecasting. With the ability to apply predictive forecast models to data, Finance teams can leverage their organizations’ vast data to guide critical decision-making – and do so at the speed of the business.
Step 3: Deploy Predictive Analytics Across the Organization
With powerful predictive models at their fingertips, Finance teams can take advantage of visualization capabilities to collaborate with business partners. How? By creating and distributing dashboards with easy-to-use charts, graphs and reports that bring forecast data to life.
Sophisticated and modern visualizations also provide interactive access, enabling users to change variables to see real-time results of those updates to models, plans and forecasts. These visualizations and dashboards provide leaders across the organization with not only the ability to access data but also the capability to interpret that data – which helps guide critical decisions and informs organizational plans.
Unleashing Finance with Built-In Predictive Analytics
OneStream’s Intelligent Finance Platform empowers Finance teams to lead at speed by unifying predictive analytics with core CPM processes, such as planning, budgeting and forecasting; financial consolidation; reporting; and financial data quality. And with built-in predictive analytics (see Figure 3), OneStream™ is unleashing Finance transformation to take budgeting, planning and forecasting processes even further – allowing teams to plan, analyze and predict with confidence.
Plan, Analyze and Predict with Confidence
OneStream’s Predictive Analytics 123 solution, which can be downloaded from the OneStream MarketPlace™, automatically cycles through multiple predictive algorithms to determine the most accurate forecast type. Results are then graphically displayed and deployed across all aspects of the budgeting, planning and forecasting processes.
Finally, with OneStream’s reporting and visualizations, Finance and business users can bring key financial and operational metrics together by combining tables, charts, graphs and other visualizations. Users can then not only turn insights into action with the ability to see the real-time results of changes to models, plans and forecasts, but also share insights and collaborate on critical analysis and decision-making. With direct integration to data sources, users can drill down into the underlying causes, all the way back to transactional details, to quickly understand business trends.
Learn More
To learn more about leveraging predictive analytics for budgeting, planning and forecasting, view our interactive solution brief, “Leading at Speed with Predictive Analytics 123”.
Well 2020 was a year we all won’t soon forget. While it was a year of disruption for many, the OneStream engineering teams were busy delivering a steady stream of innovations to the OneStream Platform and MarketPlace solutions. Here’s a quick recap of what new capabilities were delivered in 2020 and how they are helping our customers conquer new challenges and lead their organizations at the speed of business.
As the “go-to” strategic advisor to the lines of business and key functions, Financial Planning & Analysis (FP&A) teams are a natural choice to lead the long-range planning process. Why? Simply put, no other team, with the exception of the CFO and CEO, has the vantage point and capability to understand how detailed operational plans and metrics impact financial plans and forecasts.
As a strategic blueprint for organizational growth, long-range planning helps prioritize strategic initiatives such as M&A activities, new product investments, manufacturing optimization and capital sourcing – all of which are vital to achieving financial goals.
Whether it’s the global pandemic, US-China trade wars, Brexit or the 2020 US presidential election, finance teams are keenly aware of what many pundits hate to admit; uncertainty IS the new normal. And though COVID-19 is a black-swan event, navigating through uncertainly is nothing new for finance leaders. Navigating uncertainty is why long-range planning and rolling forecasting are so vital. But not just to forecast the numbers. Long-range planning and rolling forecasts help facilitate collaboration throughout the organization and increase business agility. How? By sharing insights and exchanging ideas across functions about business risk and opportunities. And of course, by leveraging those to make more effective decisions. You know what else corporate finance leaders agree on?
That predictive analytics and machine learning (ML) can take this to the next level.
While FP&A teams often serve as “the guardian” for organization-wide financial plans, many Finance teams struggle to transform key processes like budgeting, planning & forecasting. Why is that?
One key reason is that FP&A teams have more coming at them than ever before. They, of course, have core responsibilities like budgeting, management reporting and strategic planning. Those are the table-stakes. Beyond the basics, though, the FP&A playbook is wide open.
For many FP&A teams, there’s nothing more exciting than jumping into the trenches with business partners. Why? Well, like our friends in Sales, Marketing and Operations, FP&A folks love the action too. Some enjoy helping drive strategic initiatives like new product innovations, acquisitions and evaluating capital investment decisions. Other FP&A folks like the budgeting, planning & forecasting processes that transform “big picture” goals into tactical plans.
What’s the common thread? In short, most FP&A teams want nothing more than to help to deliver value to their organizations. And to help them get there, many FP&A teams would jump at the chance to leverage predictive analytics to help their business partners improve decision-making.
With all the buzz in the information technology industry around artificial intelligence (AI) and machine learning (ML) you’d think that every organization was using these tools or planning for how they are going to use them. After all, the promise is that AI and ML will help organizations harness the ever-growing volumes of data being generated by automating and augmenting human analytic processes and decision-making.
Do you work with a financial planning & analysis (FP&A) team? If so, then you’ll know there’s excitement in the air. Why? First, because there’s no other group within an organization (other than the CFO and CEO) with a view into the operations and the financials like FP&A. Next, as CFOs continue to unleash the true value of finance, FP&A groups are destined to expand their roles as trusted advisors to business partners. And don’t forget, FP&A teams play a critical role in driving innovation for the Office of Finance.
Predictive analytics, artificial intelligence (AI), machine learning (ML) – oh my. By now, you’ve heard all the buzz. You know, how technologies like these will forever change the office of the CFO. Or so “they” say.