AI-Powered CPM: Elevate Data, Insights & Forecasts

AI is rapidly changing the game in the CPM landscape. As AI and CPM solutions become more integrated, CFOs explore a role shift among their team members: fewer manual and repetitive tasks and more room for analytics and consulting. How can AI add value to your CPM processes? How does this change the day-to-day work of finance professionals and speed up your business?

A corporate performance management solution is software for finance teams to streamline and automate important financial reporting processes like Financial Close and Consolidation, Financial Planning & Analysis, ESG Reporting, and Disclosure Management. Such solutions have been in the market for over 30 years now and have always promised key capabilities like:

• Improve data collection, quality, and validation
• Reduce manual and repetitive tasks by automation and digitization
• Create workflows, task management and improve controlled collaboration
• Enable analysis and insights into the numbers behind the numbers
• Improve forecast accuracy
• Simplify reporting

With AI integrated into your CPM solution, it strengthens the capabilities to live up to these promises. AI can speed up your business and make your people’s day-to-day jobs more interesting and challenging.
The possibilities of a CPM solution with integrated AI are growing every day, and they can be of value to your organization.

How? Let us inspire you with some examples:

1. Improve data collection, quality, and validation

AI can automate data-centered repetitive tasks like organizing data from multiple sources, dimensions, and types for analysis, identifying anomalies in extensive data sets, and reconciling information on behalf of finance teams. This highly increases the quality of your data, as machines are far better at identifying errors in spreadsheets with thousands of cells than the hardworking teams that have been staring at those numbers all day.

Here are some situations in which AI can be very helpful:

• Automatic data mapping of for example a new local G/L account to the Group Chart of Accounts used for reporting speeds up the reporting process, leaving more time for (strategical) analysis and decision-making.
• Detection of outliers in numbers based on historical information increases the quality and reliability of your data.
• AI can be trained using customized machine learning models focusing on operational data.
• An AI assistant helps you manage and analyze your data, which saves you a lot of time on tracing and converting information.
• Reduce manual and repetitive tasks by automation and digitization

The use of AI for automating and digitizing manual and repetitive tasks makes finance roles shift from data collection, crunching, and reconciliation to a more analytical and consulting perspective. No more spending hours and hours collecting data from various sources, matching and crunching the various data files, manually entering them, and pivoting in Excel. Doesn’t that sound good?

2. Create workflows, task management and improve controlled collaboration

Whereas creating workflows was already possible in most CPM solutions, AI can power up account reconciliation and transaction matching by making these workflows and tasks smarter and more interactive. This also decreases your dependence on IT specialists or consultants, for instance when you need a piece of code or a custom report.

AI can also be a driver for improved collaboration. Think of the new demands for organizations around ESG reporting and the EU regulations around CSRD. Integrated reporting and transparency combining financial performance with ESG performance is a key aspect of this. AI can help provide narratives on the numbers, improving the collaboration, understanding, and alignment between finance and sustainability teams.

3. Enable analysis and insights into the numbers behind the numbers

AI can handle volumes like no human ever can. Data trends identified by people usually have a linear and one-dimensional scope. Data trends identified by AI go much deeper, as the skills of AI to identify correlations between data types are much more sophisticated. For example, AI could not only tell you the trajectory of sales, but also…