How AI Agents Are Transforming Management Accounting
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Part 2 of the AI Agent Series: Three Use Cases for Faster and More Informed Decisions
Day-to-day work in the controlling environment is characterized by volatile markets and increasingly complex planning requirements. However, the time and resources needed for in-depth analyses and a systematic evaluation of scenarios are often lacking, as the controlling team is frequently occupied with preparing data in Excel silos. Artificial intelligence, particularly in the form ofAI agents, opens up new possibilities here. It not only supports finance and operations teams with analyses but also bridges the gap between data and concrete decision-making templates directly within CPM platforms such as SAC or Board.
This article explains how AI agents function in controlling, what specific value they add, and what conditions are essential for their successful implementation. The goal is for controlling to evolve from a function focused on numbers to a strategic sparring partner for management.
Use Case 1: Working with Financial Data in Day-to-Day Controlling
In the finance sector, AI agents serve as an intelligent interface between users and existing planning and analysis systems. They access existing data, key metrics, and planning logic, and translate questions from business units into specific queries, analyses, and evaluations within the platform.
A key benefit lies in the natural interaction with planning and financial data. Instead of clicking through complex forms, reports, or model structures, CFOs and their teams can ask questions in natural language and work with the AI to analyze why key metrics are trending in a certain direction. This“Chat with your Data” principlegoes beyond mere text-based responses: In addition to concrete results, it can also provide visual analyses—such as waterfall charts—that transparently illustrate the drivers of variances.
This transforms traditional reporting into a dialogue-based analysis process: variances can be explained more quickly, anomalies in the data can be detected automatically, drivers become transparent, and hypotheses can be tested immediately. For CFOs, this means one thing above all else: less time spent searching for data and performing manual analyses, and more time for steering the business, setting priorities, and preparing well-informed decisions.
Use Case 2: Reducing the workload in controlling and operational planning processes for recurring, simple questions
In the future, AI agents will support controlling and planning directly within the process. They “know” the planning logic, understand the forms, and make even rarely used functions accessible. Especially for planning tasks that occur only once a year, they provide quick guidance and concrete assistance directly within the planning tool.
For management accounting, this has a twofold effect:
- Greater efficiency, because business users can achieve results more quickly
- Greater focus on strategic tasks, since many simple questions are handled directly by the AI agent
This not only enhances quality during the planning phase but also improves operational workflows in the finance department, as processes are designed to be more efficient and require fewer follow-up questions or manual reconciliations. And that leads to a greater focus on more strategic controlling tasks.
Use Case 3: Scenarios in Minutes Instead of Days with “What-If” Simulations for Continuous Planning Capabilities
Another key lever is rapid scenario modeling. AI agents make it possible to generate new plan versions in a very short time, for example, in response to changes in oil prices, wage agreements, or inflation assumptions.
Instead of tedious iterations in individual Excel silos, ad hoc scenarios are created that allow for a well-founded evaluation of assumptions.
This results in clear advantages:
- Greater Planning Certainty in Volatile Environments
- Greater responsiveness to external changes
- Better Decision-Making Insights for Management and the Board of Directors
The combination of speed and analytical depth makes scenario planning a strategic management tool that allows organizations to better assess uncertainties, evaluate courses of action in an informed manner, and prepare for decisions in a targeted way.
Controlling Platforms as the Foundation for "
" AI Agents
For insights generated by AI-powered agents to have a real impact, they must have access to integrated, consistent planning, financial, and corporate data, as well as governance structures. This is precisely where the underlyingCPM platform —such as SAC or Board —plays a central role: It provides the consistent data management, planning logic, and processes on which AI agents are built.
The AI agents use these structures to trigger targeted analyses, reveal correlations, and identify the causes of deviations. Only in this way can the AI agents make automated, scalable, and reliable decisions. As an interface between users and systems, they translate business questions into concrete analyses within the platform and deliver results where decisions are prepared and actions are defined.
The platform ensures that these insights do not remain isolated as mere analytical results. Instead, they are directly integrated into existing planning and management processes, where they can be further processed, evaluated, and translated into concrete actions within consistent data models. For Controlling in its role as a business partner, this very consistency is crucial for quickly turning insights into effective management. Controlling is increasingly moving away from being merely responsible for numbers toward becoming a sparring partner and process partner for management.
Other Factors for Success: Governance, Trust, and Change Management
The successful deployment of AI agents is not merely a technical issue. Rather, a clear organizational framework with security measures is crucial.
In this context, appropriate governance defines which tasks AI agents are permitted to perform, how they are to handle data, and what results they are expected to deliver. It is important here to clearly delineate what they are not permitted to do. This ensures that analyses, recommendations, and actions align with the organization’s strategy, processes, and culture.
In addition, ethical considerations based on corporate goals play an important role. This includes, in particular, the conscious handling of data errors and potential unintended biases in the underlying data. If these considerations are transparently embedded in the AI agents, they can be used reliably and established as a long-term support tool in the finance sector.
Equally crucial is user acceptance. Targeted change management, clear guidelines, and transparent rules provide direction and confidence when working with AI insights. This empowers CFOs and their teams to actively incorporate these insights into their decision-making and consistently leverage their added value in day-to-day operations.
Conclusion: Greater Control Capabilities for Management Accounting ThroughAI Agents
AI agents help the controlling function integrate analysis, planning, and implementation more closely. They make it possible to derive relevant insights more quickly from a growing volume of data and to consistently translate these insights into management actions. When properly integrated into the existing system landscape, they support the entire process—from analysis to implementation—and thereby sustainably strengthen the finance department’s ability to make decisions and take action.
The tangible added value is already evident in our day-to-day work:
- Faster, interactive analyses that complement traditional reporting and reveal correlations
- More efficient planning and flexible scenario modeling to enable a rapid response to changes
- A clear link between data, decision-making, and implementation, which makes management effective and transparent
For Controlling, when used correctly, this means one thing above all else: less time spent on manual analyses and reconciliations, and more focus on prioritization, management, and implementation. AI agents thus become a strategic lever for making the finance department future-proof and further strengthening its role as an active business partner. Controlling focuses more on creating value within the company and acts more as a sparring partner for decision-making and the efficient management of processes.
We’d be happy to help you integrate AI agents effectively into your financial and planning processes and strengthen your ability to manage these processes in the long term. Just get in touch with us.
Blog post author
Dennis Stahl is responsible for the CFO Services business unit and has been helping clients to modernize FP&A processes with innovative planning and analysis solutions for more than 7 years. In his projects, he supports companies ranging from upper mid-sized companies to DAX-listed corporations from a wide range of industries. He has extensive international consulting experience in the management, design and implementation of advanced customer solutions.
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