How AI Transforms Finance: From Data Overload to Strategic Players

How AI extends existing ERP capabilities 

AI, automation, and machine learning can extend the capabilities of ERP. These new technologies enable ERP Cloud platforms to "train" against incoming data, automating data segmentation, tagging, storage, and recall across various tasks.

This eliminates manual tagging requirements and time-intensive dataset cleaning. The recent Dresner ERP Market Study 2025 validates this trend, showing that 93% of organizations view AI as necessary for improving data sharing and integration across the organization.

Practical AI Applications in Finance

By automating low-value tasks, ensuring quality assurance, and providing a cleaner stream of data, these technologies enable teams to perform higher-value work faster and more accurately.

Automated Forecasting Capabilities

Consider this scenario: instead of manually performing forecasts, you select the drivers to explore, apply your data to an AI/ML model, and test what happens when you include new drivers or revise existing ones. The system includes or excludes variables only if they demonstrate significant effects.

This approach enables instant forecasting against multiple variables and creates automated models that continuously improve forecasting quality as datasets update through ongoing machine learning model training.

Complex Analysis Made Simple

Modern Cloud-based systems provide the computing power for AI to analyze large, complex datasets from both internal and external sources more efficiently than humans, and without data fatigue. 

This capability can accurately identify trends and potential organizational impacts, helping departments safeguard against risks and capitalize on opportunities.

Reducing Administrative Burden

AI's most compelling application may be its most mundane: handling basic administrative and data tasks. Automation passes low-value work to machines, giving teams time for high-value activities. Consider that 33% of the average professional's time is currently spent on administration—AI can significantly reduce this burden.

The Dresner Enterprise Performance Management Market Study 2025 confirms growing adoption interest, with preferences flipping to 60% for AI adoption versus 40% against in the previous year. 

Popular use cases include chatbots for casual user assistance, guided recommendations based on job roles, and natural language querying of finance data—all rated as highly beneficial by 70% or more of survey respondents.

Strategic Workforce Considerations

AI implementation requires careful consideration of team structure and roles, particularly as AI moves from interrogating a system of record based on prompts, to potentially becoming a system of action. 

CIOs and CFOs must reconsider how teams work, who will be on those teams and how AI can support. New roles may be required, making AI strategy alignment with organizational people strategy essential.

Rather than replacing people with machines, AI will evolve the roles finance professionals play—often for the better.

Unit4's AI-Powered Finance Platform

Unit4's people and customer-centric solutions already leverage powerful AI and ML processes that simplify finance workflows. That said, we are taking a measured approach to the adoption of AI, keeping an eye on regulation and compliance.

“We are not just saying we have AI for the sake of AI – we are trying to be pragmatic with what we do.  It is about reducing complexity and accelerating value to the end user.”  Antonella Crimi, Global Head of Analyst Relations at Unit4. 

To learn more about Unit4’s approach to AI governance, or our product innovation, visit our website, talk to sales, or get a demo today!