Provider Perspective: EPM Software Digital World Class Matrix™

What sets apart top-tier enterprise performance management (EPM) providers, enabling them to deliver unparalleled value and capabilities? Explore the integration of generative artificial intelligence into EPM solutions, reshaping the way organizations optimize performance. Hear The Hackett Group’s Principal Andy Warzecha, Principal Jason Logman and Director Drew Johnson as they delve into insights from our groundbreaking Digital World Class Matrix™.

Welcome to The Hackett Group’s “Business Excelleration Podcast,”where week after week we hear from experts on how to avoid obstacles, manage detours and celebrate milestones on the journey to world-class performance. This episode is hosted by Andy Warzecha, chief intelligence officer for The Hackett Group. Today’s episode will discuss the findings from the EPM Software Digital World Class Matrix study. Andy is joined by Hackett Principal Jason Logman and Hackett Director Drew Johnson from the Finance Transformation area.

To begin, they first discuss how this study is different. They assessed the value that companies are achieving by implementing tools like visibility to business performance and forecasting to determine the outputs and outcomes that drive value for companies. They analyzed these tools on a different scale. There was a robust and extensive process to evaluate the vendors. First, they looked at The Hackett Group’s process taxonomy to identify enterprise performance management (EPM) capabilities, including integrated EPM. They had 12 vendors in this study and they prerecorded an overview of the framework and timelines to software providers. If they participated, they sent them a request to participate, and they developed a very comprehensive scoring model with 28 key criteria components.

They key takeaways they found are that each of the dynamics and processes added tremendous value to the organization. The companies that were most effective were the ones who thoughtfully came back and asked how to make their metric systems more efficient in terms of automation, governing, and applying forecasting. There was a clear value proposition that existed across all the vendors in three ways, which included capacity, forecast increase and cost. There was a 55% increase in capacity in terms of cost reduction when these tools were implemented the right way. This included applying people to better and high-value tasks and letting people go home earlier. They also found they were three times more accurate with forecasting. In addition, there was a $48 million cost advantage for a typical $10 billion company in relation to head count, full-time equivalents or software. The benefits are fairly consistent, and it shows a tremendous impact for organizations to address the processes.

In addition, Jason and Drew discuss the differentiations observed in this study. They mentioned solution architecture, business process maturity and the availability of pre-built functionality. They surveyed customers and found a large percentage deployed multiple applications with new capabilities. These capabilities include data connectors, platform structures, user interface, availability of shared logic and business tools, and the ability to blend data. They split the study into a couple of categories. Some vendors had a view around integrated architecture, and some viewed more along the line of individual components. There is rapid development of capabilities across both. Artificial intelligence (AI) provides two main capabilities: internal, which includes predictive analytics, machine learning, report generation, workflow guidance, task training and support; and external: data mining, generating insights, and connecting data to internal models and systems. Lastly, there are two main actions listeners should take. First, there is a huge value proposition with these vendors that have the ability to drive value for the business. Secondly, technology is not the silver bullet, but companies need to look at it in a reflective, logical way before driving forward.

Time stamps:

0:35 – Welcome to this episode hosted by Andy Warzecha.

1:44 – Why is this study different?

3:00 – What was the methodology for scoring?

4:36 – Key takeaways.

8:25 – Clear value proposition in three ways.

10:08 – Differentiations observed in this study.

13:20 – Why does AI matter to our customers?

16:39 – What actions should our listeners take away?