Artificial intelligence (AI) is everywhere — in our homes, phones, and cars — so of course it’s becoming embedded in the software systems that buoy our job functions. For data-driven departments like finance, and data-dependent processes like corporate performance management (CPM), there is no better fit.
AI promises to improve finance’s efficiency, data discovery, forecasting, and analytics. But what does this look like for your specific set of processes?
We created this self-assessment to help you define how AI can augment your CPM processes.
Test 1: Perform an automation gap analysis
An automation gap analysis identifies areas of CPM where AI can augment human effort. Remember: AI should not replace your finance team’s expertise. Instead, it should support teams with advanced automation so that they can focus on the tasks where human intelligence shines: strategic big picture thinking and subjective analysis.
The goal of your automation gap analysis should be to determine your current state and your desired state based on the potential for AI to automate repetitive tasks.
Ask your team:
- What CPM tasks do they perform that are still manual?
- What automated CPM tasks do they perform that still consume time?
- Are those areas critical for performance?
- Are those areas eligible for improvement?
50% of finance functions are looking to close the gap in their ability to handle data over the next 3 years. - FSN
Test 2: Quantify time lost to manual tasks
How much time does your team really spend on each CPM task? And what is the ratio of time spent between:
Data management: Data entry, data collection, validation, anomaly detection
Data discovery: Drill down, sourcing information, updating KPIs reports
Analysis: Understanding business drivers, simulating scenarios, and preparing recommendations for leadership
Data collection, verification, and management are critical tasks, but for many teams, it’s more time consuming than it needs to be. One of AI’s biggest strengths is that it can automate and enhance repetitive data processes by learning from the patterns in existing data. Many data management tasks are both repetitive and data-driven — which makes them prime candidates for AI. And manual tasks like data mapping and anomaly detection are ripe for machine improvement.
Task your team:
- Ask your team to track how much time they spend on the following tasks:
- Data collection and input
- Reconciliation
- Account and transaction
- Calculations
- Reporting updates
- Analysis and data exploration
- Narrative and comments
- Disclosure
- Determine what AI tasks can accelerate. For example: AI in CCH Tagetik automates data verification tasks like anomaly detection, data collection tasks like mapping, and data discovery tasks like business driver analysis.
