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February 6, 2024

Out With the Old and In with Machine Learning: How Machine Learning is Revolutionizing Scenario Planning

Prompted by recent advancements in artificial intelligence – hello, ChatGPT – organizations across all industries are showing interest in machine learning to enhance current processes, eliminate manual inputs, and reduce redundancies.  

Forecasting and scenario planning, however, have been pioneers in utilizing AI to manage risks and streamlining work by eliminating inefficiencies. But have we gone as far as we could have? Absolutely not. 
In this blog series we’ll cover: 

  • How new technologies allow for continuous rolling forecasts and scenario planning 
  • What intelligent planning means 
  • Three use cases for machine learning forecasting 
  • The trends we expect to see as AI continues to evolve 

Let’s start by defining how these new technologies allow for continuous machine generated automated rolling forecasts based on your actual latest trends.

Machine Learning, Scenario Planning, and Rolling Forecasts

One of the most common questions we hear from our customers when it comes to intelligent planning is “how do we make our static planning more of a rolling forecast?” This comes as no surprise as companies are trying to extend their FP&A planning practices to other areas of the business like HR and sales. When being asked to create rolling forecasts for continuous planning, however, it can be challenging to update and consider all the information coming on from multiple areas of the business. 

This is where machine learning comes in, as it allows companies to leverage historical data as a starting point and take that intelligent planning to the entire organization beyond just FP&A. 

Machine learning helps you finding a confident way to plan for every contingency by allowing organizations to: 

  • Replace static annual plans with rolling forecasts and constant budget updates 
  • Weigh different courses of action before deciding the best path 
  • Create in-depth what-if scenarios and models so you can see the impact of each decision  
  • Align your assumptions with current market conditions 
  • Use historical data to see where you have been and what the best course of action if for the future of your organization  
“In ML, practitioners talk about the “3 Vs” of data needed to drive positive outcomes: sufficient volume, velocity, and variety. Workday has all three.” - Sayan Chakraborty, Executive Vice President, Product and Technology at Workday  
Our Approach to Machine Learning for Forecasting 

Workday thinks about and implements AI and ML differently than any other enterprise software company in the world. At its core, Workday Adaptive Planning aims to deliver intelligent planning for a changing world through four beliefs. 

  1. Machine learning embedded within the platform
    Makes it as easy as possible for organizations to adapt capabilities without hiring a data scientist. A seamless approach to the overall platform makes it easier for users to leverage. 

  2. Machine learning that elevates strategic thinking
    Allows you to augment the forecast, make it more accurate and efficient. 

  3. Machine learning that empowers users
    More transparency, less manual tasks, and increased time back in the users' hands to do more value-added activities. 

  4. Machine learning that’s responsible
    We care about a solution that promotes fairness and trust. This is what we call responsible machine learning. 
Intelligent Planning with Workday Adaptive Planning: How Does It Work?

Smarter forecasts can be generated through deep learning technology that allows Workday Adaptive Planning users to quickly and easily generate a predictive time series-based forecast utilizing historical data with the ability to layer in external data. 

Once a baseline forecast is developed, the system takes the emotional quotient or human factor to begin the process of planning by exception. Planning by exception – versus by rule – reduces what is considered a cumbersome and time-consuming planning process. 

Why Use Machine Learning for Scenario Planning? 

In case you aren’t already convinced, when you take a step back from forecasting manually and move towards smarter forecasts, you gain: 

  • Better insights that enable better judgment by augmenting insights. The insights surfaced by machine learning inform planners to apply better judgment and make faster, better decisions.
  • Increased accuracy that helps you identify potential data errors and anomalies
  • Speed that enables planning stakeholders to focus on more strategic work by automating repetitive tasks.
  • Ease of use, as the intelligent planning capabilities are embedded into the Workday Adaptive Planning technology platform at the architectural level making it transparent to users – and with no change to the ease of use they currently experience – but delivering significant value as the system learns. 
 The Bottom Line 

Through platforms like Workday Adaptive Planning, organizations are now able to do much more with their data. We see a future where you can plan and analyze without limits, with a planning platform that automates time-consuming tasks, scales along with your business, and lets you act quickly and make the right decisions to drive true agility for your business.  A future where you can anticipate the changing world, be the first mover, and quickly course correct. 

Want to learn more about machine learning, artificial intelligence, and the future of scenario planning? Click here to watch our latest webinar and learn how automation can help modernize your best practices. 


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