Intelligent Automation

Technical terms
About 1 min read

A technology that combines the execution power of RPA with the judgment of AI to autonomously perform complex decision-making and unstructured data processing beyond simple repetitive tasks.

Also known as
iaai-powered rpa

Detailed explanation

As of 2026, Intelligent Automation (IA) refers to a form that combines generative AI and Agentic AI, moving beyond simple rule-based robotic process automation (RPA). It uses machine learning, natural language processing (NLP), and computer vision to interpret unstructured documents, learn autonomously, and derive optimal workflows. Recently, it has evolved into multi-agent orchestration, where multiple AI agents collaborate to manage entire complex business processes end-to-end and flexibly respond to exceptions.

Why It Matters in Tool Selection

Simple RPA tools stop working when defined rules change, but intelligent automation tools make independent judgments to adapt to changing data and environments. In the 2026 business environment, whether a tool has 'Agentic' capabilities is a key criterion for minimizing human intervention and achieving long-term cost reduction.

What to Look For

  • Does it recognize unstructured data, such as PDFs, emails, and voice, with over 95% accuracy?
  • Does it propose alternatives on its own or learn and adapt when exceptions occur?
  • Is it integrated with generative AI (LLMs) to enable human-level, reasoning-based decision-making?
  • Does it integrate seamlessly with existing legacy systems and modern API-based SaaS?

Example

A process in supply chain management where, upon detecting signs of inventory shortage, the AI compares prices across multiple suppliers on its own, analyzes historical delivery reliability to draft an optimal purchase order, and then sends an approval request to the manager.

Related terms

RPAAI Orchestration