Since ChatGPT’s public launch last year, AI has become a hotly contested topic. According to IBM, 35% of businesses have already embraced AI, but the reality is that far more companies are using AI through smart applications that have integrated Machine Learning (ML). Those who haven’t yet invested in smart solutions are actively evaluating them to gain the benefits they promise. Netflix, for example, has already saved a reported $1 billion by utilising machine learning, and research suggests that AI can boost business performance by as much as 40%.
One of the applications that stands to offer organisations the biggest benefits from ML is Enterprise Performance Management (EPM). Providing real-time insights and predictive capabilities across planning and forecasting processes, ML algorithms can help companies identify patterns, trends, and relationships that would otherwise go unnoticed.
Enterprise Performance Management solutions have been designed to provide a central place to capture user inputs and consolidate corporate data, but their capabilities have been limited by the technologies available at the time of their implementation. In the age of AI, EPM is evolving into an intelligent nucleus around which the rest of the business can operate faster and smarter.
Making the right choices
Traditional finance solutions such as spreadsheets, legacy software systems, and in-house applications have long struggled to keep up with the complex needs of modern enterprises, leading to the increasing popularity of EPM. Helping organisations understand business performance in real time, EPM helps overcome bottlenecks, augmenting management decisions and creating a connected value chain.
Smart EPM solutions use sophisticated ML models and integration across platforms and functions to acquire and assimilate data from any source, analyse it at a detailed level, and provide output in the form of actionable plans for the right people, at the right time. This, however, is not as easy at it appears at first glance, and requires the right configuration and implementation in order to yield the right results.
Good data quality, for example, is essential. Already, this is a stumbling block for many organisations trying to get the most out of their EPM solutions, with increasing volumes of data making it difficult for decision-makers to extrapolate the insights they need to positively impact business outcomes. Similarly, adhering to global data protection regulations is already challenging enough, so any AI-enabled platform must be built to align with all relevant laws and standards.
As a result, the right choice of platform has never been more important. As providers increase the ML capabilities in their EPM solutions, finance decision-makers must start evaluating them based on more stringent criteria to ensure that they can find the right balance between smart automation, transparency, and alignment with business performance.
Operationalising ML
CFOs may only have started on their AI journeys relatively recently, but they are all aware that it is not a question of whether AI and ML will play a role across financial processes, but how they can best use it to achieve their goals. Finance teams that figure out how to operationalise ML will not only be able to leave manual processes behind, increasing workforce capacity and saving time and money but will effectively be future proofing their businesses.
With the right intelligent EPM implementation, finance professionals can close faster, detect anomalies, automate data analysis, and streamline processes. Processes that previously required weeks to complete can be completed in days, or in some cases, hours, informing short- and long-term decision-making while enabling lines of business through shared data.
As progressive organisations continue to transform their finance functions, emerging technologies like AI, ML, integrated analytics, and business intelligence are enabling reliable, accurate, and adaptive planning and performance management systems. AI-powered accelerated data understanding and augmented decision-making may be in their infancy, but they promise to be the future of enterprise growth strategies.
