As the promise of Artificial Intelligence (AI) within corporate performance management (CPM) moves from fiction to fact, many FP&A teams are asking the same basic question. What do AI and Machine Learning (ML) mean for me?
To answer this question and more, our AI for FP&A blog series is designed to help organizations prepare for the AI and ML journey and move beyond the hype. Where to begin? Well before jumping into any new journey, it’s critical to chart the course to anticipate what’s in store on the road ahead. And for a topic as exciting and overhyped as AI, any new journey must begin by considering the key factors that have traditionally held Finance back from AI adoption.
Market Appetite for AI and ML
As we shared in the first post of the AI for FP&A blog series, about 60% of organizations are using or actively exploring ML according to the 2021 Dresner Advisory Wisdom of Crowds® Data Science and Machine Learning Market Study (see Figure 1). On the surface, the progression over the last 5 years underscores the AI hype and excitement for the potential of AI for FP&A.
But when one breaks the data down by function, a bit of a different reality emerges for the Office of Finance and FP&A.
The study shows that only 20% (see figure 2) of Finance organizations are currently using AI and ML, and Finance actuals lag most functions despite all the buzz and chatter we all hear about.
What’s Holding FP&A Back?
With so much buzz and such little adoption, let’s examine the key barriers holding FP&A and Operations teams back from mainstream adoption of AI and ML solutions (see figure 3):
Lack of Expertise
Without dedicated expertise or resources, FP&A’s ability to take advantage of AI and ML is severely limited.
Lack of Scale
Lack of Business Intuition & Transparency
As a strategic business partner, it’s FP&A’s role to instill confidence in forecasting processes. And while leveraging AI and ML is likely to increase forecast accuracy – if P&L owners cannot assess the drivers that comprise their forecasts – P&L leaders will never own their forecasts.
And if P&L owners do not own their forecasts, forecasting processes break down and fail altogether which means FP&A has failed too.
Fragmented & Disconnected Processes
As AI and ML for FP&A enter the mainstream, organizations will undoubtedly have several choices to consider. On one spectrum, solution vendors for AI (see Figure 5) are offering everything from AI infrastructure solutions to data science toolkits and complete AI platforms to create and deploy ML models. While these are powerful tools addressing varying use cases – these tools are not designed for FP&A teams.
Corporate performance management vendors are also investing in AI capabilities to support extended planning & analysis (xP&A) processes such as demand planning and sales planning. As figure 5 illustrates well for AI vendors, CPM vendors will also solve the AI needs of their customers in different ways.
So what’s the lesson in all this?
Don’t let AI hype cloud the evaluation process. Start with a clear understanding of “what” business outcomes your FP&A team is trying to achieve with AI and ML. Identify “who” is using the solution and “how” the solution is unified into existing planning processes.
And with answers to these questions in mind, use the evaluation process to “get under the hood” to learn whether the solution will in fact unleash the organization from the key barriers that are holding FP&A back from moving beyond the hype.
To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our blog series or download our interactive e-book here.
We’re proud to announce that our very own CEO, Tom Shea, is now a Forbes Tech Council contributor. This means that moving forward, you can expect to see a regular cadence of Tom publishing articles on Forbes that share his expertise on different forms of technology such as Corporate Performance Management (CPM), Predictive Analytics, Artificial Intelligence (AI), Machine Learning (ML), and more. Below you’ll find his first article where he discusses democratizing AI and ML solutions across organizations to surpass the roadblocks in implementing and adequately scaling the solutions to meet business demands. Keep an eye on his profile for the next one.
Democratizing AI To Transform Your Business in An Unpredictable Future
Artificial intelligence (AI) and machine learning (ML) are by no means new concepts for the office of finance. In fact, 59% of finance execs reported they are already investing in the technology. So, why do organizations still face roadblocks in implementing and adequately scaling AI and ML solutions to meet business demands? Because they are not democratizing the technology across their organization.
AI is no longer just a tool for data scientists. To extract the most value from this technology, companies must make it accessible for employees across different lines of business with varying levels of experience. Read on as I dig into the power of AutoAI and ML technology and how it can help businesses scale ML deployments and better plan for an unpredictable future.
Current Hurdles for Companies Using AI Models
You may be thinking that integrating AI technology into your organization is easier said than done. AI models do present their own unique set of challenges. First, these models are both logistically and algorithmically complex to create. It’s difficult to build a single AI model to solve one problem, and even more so to scale models across multiple use cases.
AI models are living organisms. Once built, they are difficult to maintain and train, especially since models are not, and should not be, generalizable. Because of their unique nature, introducing new and potentially surprising or divergent information, like Covid-19 data, for instance, would require manual input and constant retraining across models. This can seem like an insurmountable time suck on already tight resources.
Not only is this troubling from an operational perspective but also from a talent perspective. As employees increasingly seek new jobs due to burnout, it’s critical that organizations prioritize their workloads. Not to mention, data scientists themselves are in short supply, hard to find, and expensive to retain once you do bring them on board. As a result, the burden of data analysis often falls on the financial and business analysts across the enterprise. They need to understand how to create actionable data insights and make the most of them, but that’s often difficult as these employees do not have the knowledge and expertise to effectively analyze data streams like a data scientist would.
That’s where AutoAI tools can demonstrate their value.
How Organizations Can Use AutoAI and ML Solutions To Solve Business Problems
Introducing and implementing AutoAI services and solutions can solve these ML model creation and deployment problems. And when they are built directly into existing platforms, organizations can create predictive solutions that can scale to enterprise needs without the need for large teams of data scientists. In fact, AutoAI can build thousands of ML models in parallel, which, in turn, results in significant time and cost savings. This is beyond what an individual data scientist could ever accomplish, enhancing overall enterprise operations.
Business users can start leveraging AutoAI models in minutes, providing advanced predictive power for a broad range of staff, not just data scientists. More automation means a reduction in non-value-added work for analysts, allowing businesses to obtain deeper insights and anticipate future challenges and opportunities. This is allowing organizations to solve more problems than ever, even with limited data science manpower to do so.
That said, analyst intuition is still critical, no matter the technology the business is leveraging. AI cannot do it alone. There is a level of human intelligence that is still, and will always be, required. Business analysts can supply their knowledge of business events and other factors and inject this into models to make forecasting and planning more powerful. The possibilities become endless when AutoAI models are combined with business intuition and human insights for more intelligent forecasting and analytics.
Lastly, transparency is key. Financial analysts typically do not have full transparency into the AI and ML models that support their planning, decision-making, and reporting. By ensuring that AI models offer a transparent look into the algorithms and forecast drivers behind the data, end-users are provided deep insights into how different factors impact a model’s performance and can leverage the results with more confidence.
How AI Capabilities Can Help Sectors Recover in An Unpredictable Future
If there is something businesses have become comfortable within the last year and a half, it is unpredictability. With this unpredictability, businesses have learned the importance of consistently being prepared for potential disruption and volatility. When AI and ML are implemented correctly, the insights gained can enable businesses to have a level of resilience, bouncing back and moving forward swiftly. Let’s consider some real-world application examples.
In a restaurant environment, for instance, the right AutoAI solution can enable a chain with many menu items and multiple locations to build thousands of ML models in tandem. These models can then help restaurant managers factor in external data like local events, weather forecasts, mask mandates, and more into their daily planning. This can ensure they are well prepared for situations like outdoor dining or an influx of customers and can equip their staffing and inventory ordering according, ultimately maximizing sales and optimizing expenses.
In the retail industry, AI models can help forecast weekly sales and inventory requirements. In an industry deeply impacted by potential supply chain issues, like the ones brought on by the pandemic, having the power to forecast stock challenges is transformative. This is especially true ahead of high-volume times of the year like the holiday season. AI models help to inform current business decisions, learn from new inputs and help enterprises adapt for what lies ahead.
AI Hype Has Become Business Value
AI and ML are finally transitioning from “hype” conversations to real solutions offering direct business value. Technology is no longer a solution seeking a problem. Targeted AI solutions now solve discrete enterprise challenges and bring transparency back into processes. With AutoAI and ML solutions, enterprise analysts and decision-makers are better equipped to forecast and plan for the future, overcoming seasons of unpredictability that have now become the norm.
To learn more about how OneStream is enabling customers to democratize AI in their organizations, check out our August 2021 press release announcing the preview of new AI and ML capabilities at our Splash Virtual Experience.
Like the exponentially increasing adoption of cloud-based solutions by Finance, the adoption of artificial intelligence (AI) and machine learning (ML) is a matter of when – not if. Both AI and ML will help FP&A teams and business analysts analyze and correlate the most relevant internal/external variables that contribute to forecasting accuracy and performance across the Sales, Supply Chain, HR, and Marketing processes that comprise financial plans and results.
Why does this matter?
Across the globe, CFOs are being pushed to be more strategic – whether focusing on long-term plans, rolling forecasts, or a more immediate pulse of the business – and to do so at an increasingly faster pace. FP&A teams have also become more important than ever as organizations seek to survive and even thrive during times of disruption or crisis. And while this shift continues, many CFOs and their teams are asking the same question: How do we remove the fog of uncertainty from planning and forecasting processes?
Artificial Intelligence and Machine Learning Defined
Within corporate performance management (CPM) processes, AI and ML are fast becoming key enablers to assist decision-making and drive productivity improvements across several use cases (see Figure 1).
Figure 1: Artificial Intelligence and Machine Learning Defined
AI and ML enable FP&A teams to combine macroeconomic factors like GDP and consumer preferences with internal data to determine correlations and add additional variables to enhance forecast accuracy and effectiveness.
Within CPM processes such as planning and reporting, that combination helps FP&A create faster, more informed forecasts, increase collaboration with line of business partners and drive more effective decision-making while drastically increasing the impact of planning processes.
AI for FP&A – Practical Use Cases
For organizations at the beginning of their advanced analytics journey, aligning AI and ML into everyday FP&A processes does much more than improve forecast accuracy. AI-enabled forecasts and operational analytics enable cross-functional collaboration by providing decision-makers with new insights and new, innovative ways to ask “why” and drive performance (see Figure 2).
Here are just a few of the top use-cases for organizations thinking about adding AI and ML into a wide variety of financial and operational planning processes:
Figure 2: Practical Use Cases for AI-Enabled Planning & Forecasting
AI Expectations vs. Hype
While not yet as widely accepted as the move to the cloud for the financial close and planning processes, AI adoption is already increasing according to the 2021 Data Science and Machine Learning Market Study by Dresner Advisory Services. In 2016, 40% of responding organizations reported using or actively exploring ML. That same metric was about 60% in 2021 (see Figure 3), showing a steady increase over the last five years.
The current economic uncertainties and rapidly changing business requirements will likely be a catalyst to drive adoption up significantly over the coming years.
Figure 3: Dresner Advisory Wisdom of Crowds® Data Science and ML Market Survey
With all the industry buzz, it’s easy to assume that most FP&A teams are already leveraging AI. Surprisingly, they’re not – at least not yet.
Unfortunately, despite excitement across the industry, the adoption of AI and ML in FP&A still lags most functions. Less than 20% of Finance organizations are currently deploying AI, according to the 2021 Dresner Advisory Wisdom of Crowds® Data Science and Machine Learning Market Survey. Why do you think there’s such little adoption?
Introducing the AI for FP&A Blog Series
Here’s my take.
FP&A leaders 1) understand the promise of AI and ML but 2) many FP&A teams don’t yet understand what it takes to deploy AI and ML across enterprise-wide planning processes at scale.
To address this lack of understanding and more, we’ve developed a 3-part blog series for FP&A teams to consider as they begin their AI journey. Here’s a quick summary of our key topics:
Will AI and ML change FP&A forever? I think that’s a stretch. But once organizations can cut through the “buzz” and move beyond the AI hype, FP&A teams will see the light. What do FP&A leaders have to lose by having another point of view on the numbers and KPIs with the help of AI and ML? By having a more insightful dialogue with their CFOs and business partners to collaborate and drive better decision-making?
At OneStream, we call this Intelligent Finance.
To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our blog series or download our interactive e-book here.
The COVID-19 pandemic has created ongoing challenges for CFOs and reshaped Finance teams. Finance teams are now assessing revenue, costs and cash flow on a weekly, daily and even near-real time basis to help guide current and future decisions. But financial data is only one piece of the puzzle. Why? Effective Finance teams know the numbers on the P&L, balance sheet and cash flow statements are driven by dozens, even hundreds, of decisions made across the Sales, Marketing, Supply Chain and Operations teams.
OneStream’s Splash conferences offer Finance teams a unique opportunity to think bigger. To step back from the daily grind and focus on unleashing the true value of Finance. How? By learning directly from OneStream customers, from industry experts and from each other, of course.
And though we couldn’t all celebrate in Paris this year, OneStream will always find a way to deliver for our customers – 100% customer success is our mission, after all. That’s why we were thrilled to kick off the OneStream Splash EMEA Virtual Mini-Series September 21 – 24th.
Moderated by Matt Rodgers, Managing Director of EMEA, the keynote featured OneStream CEO Tom Shea and our very special guest, Formula One racing world champion Mika Häkkinen.
Read on for the highlights from our keynote session.
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.
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.
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.
Finance transformation is reaching a digital crossroads. On one side, finance leaders face what is now a standard set of questions. What are the operational and financial impacts of modernizing business models? How will the organization attract new talent to develop and lead these new innovations? And how will advanced analytics like machine learning (ML) help to optimize these critical decisions? Many leaders even go farther and ask – is the future of finance about people or technology?
Artificial Intelligence and Machine learning are hot topics in the world of Finance and OneStream Software has been actively researching these technologies and how they can be applied in corporate performance management (CPM). OneStream CEO Tom Shea has been a keen advocate of these technologies and has spoken about them at our Splash user conference and other events.