Building Autonomous Automation: Making Generative AI the Brain of RPA

With the rapid decline in the working population, many companies are facing the barrier of a "serious labor shortage." Traditional RPA (Robotic Process Automation: tools that automate routine tasks on a PC) has contributed to speeding up operations that repeat fixed procedures, but it has been unable to handle "non-routine tasks" (tasks without fixed procedures) that require judgment.

However, in the consulting field as of 2026, "autonomous automation"—which brings administrative work as close to zero as possible by integrating Generative AI as the "brain of RPA"—has become a reality. This article explains how to build practical automation solutions that can be understood even without specialized knowledge.

1. Generative AI as a Judgment Axis to Break Through RPA's Limitations

Traditional RPA was like a "hand" that simply followed pre-determined "if A, then B" rules. Consequently, work inevitably stalled in situations where a human had to read and judge the content, such as reading invoices with formats that varied by supplier or responding to emails containing ambiguous instructions.

By integrating LLM (Large Language Models: the foundational technology for AI that understands and generates language like a human), the AI acts as a "brain" that understands context and converts unorganized information into a manageable format. This enables automation involving "semantic interpretation," which was previously impossible.

For example, in the field of Own EC Site Construction & Growth Support, systems where AI categorizes free-text inquiries from customers and instructs appropriate RPA actions (such as checking inventory or drafting replies) are achieving significant results.

2. Agentic Workflows to Eliminate Administrative Tasks

Next-generation automation is evolving from mere "integration" to "autonomy." This is called an Agentic Workflow (a system where AI thinks for itself, determines the next step, and executes it). In this model, the AI acts like a secretary, breaking down tasks into detail and selecting and executing the appropriate tools.

Taking order processing automation as an example, when an "exception" such as an out-of-stock item or an address error occurs, the AI autonomously determines that "a confirmation email should be sent to the customer," drafts the message, and completes the transmission. According to statistical data, companies that have introduced this autonomous approach have seen a dramatic reduction in administrative man-hours.

Observations in the field show that the more complex the task—such as those with more than 10 decision branches (if-then conditions)—the more the "reasoning ability" (the power to think logically) of Generative AI proves its true value. By having AI handle the exception processing previously done by humans, it is possible to achieve virtually zero administrative work.

3. Barriers to Implementation and the Essentials of "Data Structuring"

However, it is not enough to simply introduce AI. The most important aspect of actual support is organizing the "unstructured data" scattered throughout the company. Unstructured data refers to data that is difficult for computers to calculate or process as-is, such as email bodies or handwritten notes.

Furthermore, measures against hallucination (a phenomenon where AI tells plausible lies) are essential. We recommend the concept of Human-in-the-loop (a design where humans intervene in the AI's processing steps). This is a system where a human performs the final check only when the AI determines it "lacks confidence in its own judgment (low confidence level)." This maximizes automation while preventing errors.

Finally, the purpose of automation is not just cost reduction. Shifting the saved time to "creative tasks that only humans can do," such as strategy planning for Own EC Site Construction & Growth Support or improving customer experience, is what truly constitutes DX (Digital Transformation: the transformation of business and life through digital technology).