Artificial intelligence is increasingly finding its way into controlling, financial planning, and reporting. While many companies are still talking about pilot projects and isolated tools, productive AI functions are now emerging directly in established planning systems.
IBM Planning Analytics (TM1) integrates an AI layer with the Planning Analytics Assistant that supports analysis, explanation, and scenario evaluation—both in the cloud and on-premises. The platform thus addresses the key requirements of CFOs, controlling managers, and finance organizations: transparency, data sovereignty, traceability, and efficiency.
This article shows which specific AI functions are already available today, how they can be used in controlling, and where their realistic limits lie.
What exactly does AI mean in controlling?
AI in controlling does not mean that algorithms make strategic decisions independently. The real added value lies in the automation of routine analytical tasks, the explanation of complex relationships, and support in interpreting data.
Typical areas of application are:
- Automated analysis of large amounts of data
- Explanation of key figures and calculation logic
- Detection of anomalies and trends
- Cause and effect analyses
- Simulation and comparison of scenarios
The key thing here is that results remain traceable—keyword: explainable AI in controlling.
The Planning Analytics Assistant: AI directly in the TM1 model
IBM summarizes its AI functions under the term Planning Analytics Assistant. It is available both in cloud environments and on-premise, meaning that sensitive financial data does not necessarily have to be outsourced to external cloud systems—an important aspect for regulated companies and data-driven organizations.
There are basically two categories:
- Standard AI functions Ready to use, no implementation effort required.
- Custom AI functions Tailored solutions for specific technical issues, e.g., scenario optimization or ESG analyses.
Standard AI functions for analysis and reporting
Automatic interpretation of diagrams
Graphics can be automatically analyzed and interpreted in text form. AI generates both continuous text and structured bullet points that can be used directly for presentations or reports.
Benefits in controlling:
- Reduction in manual commenting
- Consistent interpretation of reports
- Time savings in monthly closing and management reporting
Explanation of individual key figures (“Explain Cell”)
A common bottleneck in controlling is the question: “How did this figure come about?” The assistant can explain individual cells directly—including formulas, calculation logic, and textual descriptions.
Benefits:
- Greater transparency and traceability
- Faster training of new employees
- Support with inspections and audits
Outlier analysis in financial data
The AI automatically detects anomalies in time series or key figures and flags potential anomalies. Different analysis algorithms can be used without having to implement additional models.
Benefits:
- Early identification of data errors or special developments
- Support with forecast validation
- Quality assurance in reporting
Impact analysis and driver identification
The impact analysis shows which key figures influence each other and to what extent. These relationships are explained visually and in text form.
Benefits:
- Transparent driver analysis
- Well-founded cause-and-effect assessment
- Support for management decisions
Automated PDF reports
Analysis results can be automatically converted into structured PDF reports—including outlier and impact analyses.
Benefits:
- Consistent reporting
- Reduced manual effort
- Scalability for regular reports
Data Explorer: Text-based search in TM1
The data explorer enables semantic searches across cubes, views, and structures. Even fuzzy or linguistically divergent search queries are interpreted.
Benefits:
- Lower barrier to entry for specialist users
- Faster navigation in complex models
- Support for self-service analytics
Custom AI features: intelligently compare scenarios
In addition to the standard functions, individual AI use cases can be implemented. One example is the comparison of several optimized scenarios, for example in ESG or cost management.
Here, the AI analyzes various scenarios and automatically generates textual evaluations and comparisons. Users can formulate specific questions via prompts.
A distinction is made between AI assistants and AI agents:
- Assistants that follow clearly defined analysis paths
- Agents that autonomously determine solutions
These customized solutions require professional modeling and technical implementation—but offer considerable strategic added value.
Limits and realistic expectations
AI does not replace professional responsibility in controlling. Results must still be interpreted, validated, and critically examined. The quality of the statements depends directly on the quality of the data and models.
The real added value lies not in the automation of decisions, but in:
- Acceleration of analysis processes
- Transparency of complex logic
- Relief from manual routine work
Conclusion: AI as productive support in the financial sector
IBM Planning Analytics shows how AI can be used productively and in a controlled manner in controlling—without any black box character and with clear data sovereignty. In particular, the combination of explainable analysis, automated reporting, and individual scenario evaluation opens up new efficiency potential for finance organizations.
If you would like to check which of these AI functions can be usefully applied in your existing planning and reporting landscape, it is worth having a brief technical discussion.
In a non-binding appointment, we will look at your current processes, data models, and objectives together – and evaluate where automation and AI can deliver real added value.
