AI Agents for Data-Driven Processes

When AI Agents Are Worth It

AI agents are considered the next step in the evolution of business process automation. At the same time, many companies are uncertain: Which use cases actually benefit from autonomous systems, and where does unnecessary complexity arise?

That’s exactly where this article comes in. It explains how AI agents work, what their specific benefits are, and what conditions are essential for their successful implementation. The goal is to provide a clear understanding: Not every process needs an agent—but where it’s a good fit, it can create real added value.  

What Sets AI Agents Apart

An AI agent is more than just a traditional automation script. It independently pursues a goal, analyzes its environment based on data, makes decisions, and then carries out actions without the solution path being fully predetermined.  

Technologically, this approach is typically based on a language model that acts as a central control unit: It coordinates various tools, evaluates interim results, and works iteratively in loops until the desired goal is achieved.

It is precisely these characteristics that make AI agents particularly attractive for complex, dynamic business processes. At the same time, however, this also significantly increases the demands placed on design, integration, and operation.  

AI Agents vs. Traditional Automation: Making the Right Choice

The introduction of AI agents opens up a wide range of opportunities for companies to make their processes more flexible and intelligent. However, to harness this potential effectively, a targeted and thoughtful approach is essential. This is because not every process benefits equally from an agent-based approach. Especially for stable, clearly structured workflows, traditional automation often remains the more efficient, cost-effective, and easier-to-control solution.

For decision-makers, this means that the focus should not be on deploying as many agents as possible, but rather on carefully assessing which level of automation offers the greatest added value for each specific use case.

An overview of the three stages of process automation:

1. Traditional Automation  

• Rule-based and highly structured
• All processes and exceptions have been defined

2. Document-Based AI

• Processing Unstructured Information
• People remain a central part of the process

3. AI Agents  

• Autonomous, multi-stage process execution
• Humans only intervene in exceptional cases (“human on the call”)

Accordingly, typical use cases for AI agents are primarily found in highly complex processes with many exceptions. This is the case, for example, when workflows are not always the same and require different decisions depending on the situation. They are also particularly well-suited for environments in which multiple systems, tools, or APIs interact, or in which large amounts of unstructured data must be processed. It is also crucial that the decisions made remain traceable and can be corrected if necessary.

In contrast, traditional automation demonstrates its advantages wherever processes are stable and clearly defined. When workflows follow a fixed pattern and only a few exceptions occur, a rule-based approach is generally more efficient, more cost-effective, and significantly easier to manage.

Case Study: Automation in the Billing Process

A look at the so-called “three-way matching” process in invoicing illustrates the practical implications of this distinction. In this process, invoices, purchase orders, and goods receipts are reconciled with one another to identify discrepancies at an early stage.

While standardized cases lend themselves very well to traditional automation, the complexity increases significantly as soon as data is incomplete or inconsistent. This is precisely where an AI agent can demonstrate its strengths: It extracts invoice data (e.g., via OCR), reconciles it with ERP and master data, identifies discrepancies, and, if necessary, independently initiates further steps—such as requesting missing information or creating a ticket, including preparation for escalation.

This example clearly shows that the added value of AI agents does not come from standard processes, but from the intelligent handling of exceptions. And it is precisely this capability that makes them a valuable complement to traditional automation—provided the processes are sufficiently complex.

Success Factors: Prerequisites for Productive Use

The difference between an initial prototype and a production-ready AI agent becomes particularly apparent during actual operation. While simple use cases can often be implemented quickly, requirements increase significantly as usage grows—especially in terms of scalability, security, and controllability. Therefore, sustainable success depends not only on the technology itself, but above all on the quality of the underlying framework.

The data infrastructure plays a central role in this : Data must not only be available, but also structured and presented in a way that is easy to understand so that an agent can operate reliably. Equally important are clearly defined and digitized processes that serve as the foundation for automation. Without these prerequisites, it is difficult to fully realize the potential of AI agents.

In addition, a robust technical infrastructure is necessary to enable seamless integration into existing systems and platforms. At the same time, companies must establish clear governance and security frameworks. These include, among other things, granular access controls and role-based models, the pseudonymization of sensitive data, and protective measures against attacks such as prompt injection.

Another crucial factor is continuous monitoring and quality assurance during ongoing operations. The activities of AI agents should be systematically evaluated and documented, for example, through so-called traces. In addition, testing procedures such as golden sample tests help ensure the long-term reliability and stability of the results.

It is particularly important to focus on the actual business benefits: The use of AI agents should deliver measurable results. Metrics such as processing time, success rate, or the proportion of necessary human intervention provide a solid foundation for transparently evaluating the added value and making the return on investment clear.

Process Model: How Companies Get Off to a Successful Start

The journey from the initial idea to a functional AI agent does not begin with the selection of technology. Instead, a structured and methodical approach is crucial for identifying suitable use cases, minimizing risks, and ensuring future value from the very beginning.

Proven Steps:

1. Identify and evaluate use cases:
Focus on scenarios that can be implemented quickly and add value

2. Describe processes in detail:
Break down processes into sub-steps and define tasks and decision-making logic

3. Integration and Testing:
Integration of relevant data sources and systems, as well as extensive testing in complex scenarios

4. Monitoring and Continuous Optimization:
Continuous improvement based on actual usage

Such an iterative approach enables companies to gain experience step by step, learn early on, and further develop the use of AI agents in a targeted manner. This significantly reduces risks while simultaneously laying the groundwork for achieving sustainable and measurable added value in operational use.

Conclusion: Use AI agents strategically rather than rolling them out blindly

AI agents offer companies enormous potential to make processes smarter, more flexible, and more efficient. However, it is crucial that their deployment not be driven by a technological impulse, but rather be based on a clear strategic framework. After all, the greatest leverage lies not in the technology itself, but in selecting the right use cases and ensuring a clean, well-thought-out implementation.