AI Agents in Controlling: What They Can Do, When They Are Worth It, and How to Get Started

AI agents in controlling are autonomous software programs that independently perform analysis tasks, reporting processes, and planning steps by accessing data sources, ERP systems, and BI platforms, without a human controlling each individual step. They should not be confused with simple chatbots.

AI agents act purposefully, combine multiple tools, and escalate to humans only when a decision falls outside their defined boundaries. For controlling teams, this means: less routine, more analysis.

What are AI agents in controlling?

An AI agent receives a goal, not a command. He independently decides which steps to take, which data sources to tap into, and when to report the results. In the context of controlling, it looks like this: The controller asks: “Why is our personnel expense in the southern region above budget?” The AI agent independently queries the planning database, compares actual and budget values, analyzes cost centers, and delivers a structured response with the three most relevant drivers in just a few seconds.

This is not science fiction. We are already using AI in controlling based on IBM watsonx Orchestrate and Claude in finance environments. The agents access IBM Planning Analytics (TM1), SAP data, and other sources.

What tasks do AI agents take on in controlling today?

AI agents are not jack-of-all-trades. They are strongest when tasks are repetitive, data-driven, and rule-based. The following table shows what is already functioning productively today:

Important: AI agents always work with guardrails. They cannot create bookings without approval and cannot change data without a log. The principle of Human-in-the-Loop remains standard.

How do AI agents technically function in controlling?

AI agents for controlling consist of three layers:

1. Language model as the core: A large language model (LLM) like IBM Granite or Claude understands natural language queries and plans the sequence of work steps.
2. Tool access: The agent connects via defined interfaces (APIs, MCP servers) to data sources such as IBM Planning Analytics, SAP, Power BI, or internal databases.
3. Orchestration: A platform like IBM watsonx Orchestrate manages which agent takes on which task and when, ensuring logging and escalation.

We implement this architecture modularly. That means: You start with an agent for a clearly defined task, e.g., automated variance analysis, and gradually expand.

When are AI agents worthwhile in controlling?

AI agents do not pay off in every company immediately. These three conditions should be met:

• Data basis is available: AI agents need structured, accessible data. If data is stuck in silos or is not machine-readable, the agent won’t get far.
• Processes are defined: Tasks that an agent is supposed to take on must first be clearly described. What no human can systematically explain, no agent can execute.
• Team is ready: AI agents are changing the role of the controller. Whoever introduces agents needs a team that wants and can work with AI support.

Companies with around 200 employees and a structured controlling system using a BI platform like IBM Planning Analytics benefit the most quickly. The ROI typically ranges from 6 to 12 months.

How can one successfully start using AI agents for controlling?

We recommend a structured approach in three steps:

1. AI Discovery Workshop (1 day): Together, we will identify the three processes with the greatest automation potential. Result: a clear picture of what is immediately feasible and which agent delivers the fastest ROI.
2. Pilot project (4 to 8 weeks): An agent is built for a specific task, tested, and integrated into the existing environment. No big bang, no months-long project.
3. Rollout and expansion: The first agent will become the blueprint template for further areas of application. We support the ongoing operations as part of the BI Managed Services.

Numbers and facts about AI agents in controlling

• According to McKinsey (2024), up to 40 percent of typical controlling tasks can be accelerated or taken over by AI automation.
• Our projects show: The first productive AI agent will be ready for use in 4 to 8 weeks.
• The most common first application: automated variance analysis in the monthly closing.
• The typical ROI period for the first AI agent in controlling is between 6 to 12 months.

Glossary: AI Agents in Controlling

Frequently Asked Questions about AI Agents in Controlling

What is the difference between an AI agent and a chatbot in controlling?

A chatbot answers questions. An AI agent acts. He independently plans which steps to take, accesses systems, and delivers a result without a human controlling the process. In controlling, this means: The agent analyzes, consolidates, and reports, the controller decides.

Can AI agents make mistakes?

Yes. That’s why productive AI agents always work with guardrails and a human-in-the-loop principle. All actions are logged. At defined points in the process, the agent requires human approval. We configure these boundaries together with the customer.

Which systems need to be in place for AI agents in controlling?

At least one structured data source (e.g., IBM Planning Analytics, SAP, or an ERP system) and an orchestration platform (e.g., IBM watsonx Orchestrate). We will review in the discovery workshop which infrastructure is already in place and what needs to be added.

Does our controlling team need programming skills?

No. Modern platforms like IBM watsonx Orchestrate enable the creation of AI agents without code. We build and configure the agents, the controlling team specifies what the agent should do.

BI2run: AI Agents for Finance and Controlling

We are a specialized provider of AI agents in finance and controlling environments in the German-speaking region. We combine expertise in IBM Planning Analytics, IBM watsonx Orchestrate, and modern AI architectures. Our AI agents are not demos, but productive systems running in real companies.