AI agents vs. RPA: When each approach is worthwhile for SMEs

RPA (Robotic Process Automation) and AI agents are often mentioned in the same breath, but they solve different problems. Those who make the wrong choice end up with either an overengineered system for a simple task – or a rigid system that fails at the first exception.

What RPA does well

RPA follows fixed, predefined rules: read data from system A, enter it into system B, trigger an action when condition X is met. For stable, clearly structured processes with few exceptions this is the faster and more cost-effective solution – for example, automatically transferring invoice data into an accounting system as long as the format remains the same.

Where RPA reaches its limits

• Every new exception requires an additional rule branch
• Unstructured inputs (free text, changing formats) are difficult to process
• Tasks with multiple decision points quickly become confusing
• Changes to connected systems often require manual adjustment of the rules

What an AI agent additionally does

An AI agent plans the path to the goal itself instead of being preprogrammed for every individual case. It comes into play where a task requires multiple steps, decisions, or access to different systems and cannot be sensibly forced into rigid if-then rules – for example, a customer inquiry that, depending on its content, goes through different systems and checks.

The practical decision question

The key question is not “RPA or AI agent,” but: How stable and unambiguous is the process really?A process that can be described with a few clear rules and rarely has exceptions is well suited to RPA – faster to implement, less effort. A process with many variants, decisions, and changing input formats is the domain of the AI agent. In practice, many companies combine both: RPA for the stable substeps, an agent for the cases that require decisions.

Practical example: invoice intake

A company always receives invoices from the same supplier in the same structured format – here, a fixed rule is enough to transfer the data. If, however, invoices come from many different suppliers in different formats, with changing field labels, a simple rule quickly turns into a confusing set of rules. This is exactly where an AI agent takes over the classification and forwards only the unclear cases to a human.

By the way: Consulting and guided implementation of such tools is eligible for funding. Through INQA-Coaching 80% of consulting costs are covered nationwide, up to €11,520. Funding applies to the introduction, not the software itself – the funding check shows in a few minutes what is eligible for you.

Frequently asked questions about AI agents and RPA

Is an AI agent always the better choice?

No. For stable, clearly defined processes, RPA is usually implemented faster and is more cost-effective. The agent is worthwhile where decisions and variations come into play.

Can RPA and AI agents be combined?

Yes, in practice this is often the most economical approach: RPA for the stable subprocesses, an agent for the cases that require decisions.

What happens if a process automated with RPA changes?

The rules have to be adjusted manually. With an AI agent, the adjustment is often smaller because it does not depend on a rigid rule branch.

Does an AI agent require more technical preparation than RPA?

In most cases, yes, especially when connecting the systems involved. However, this additional effort is worthwhile precisely when the task is too complex for rigid rules.

Next step

More on the technical foundation on the product page AI agents. For an assessment of your process, you can reach us via the contact page.

As of September 2026.