AI GLOSSARY

Intelligent Automation

Intelligent Automation (IA) combines traditional process automation with AI—language understanding, decision-making, and learning. It is the next evolutionary step beyond RPA and enables end-to-end automation even for unstructured processes.

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Components
RPA, AI, BPM, Analytics

Areas of Application
in Enterprise Environments

Core Technologies
From LLM to Workflow

Best Practices
for Productive IA

End-to-End Processes

Entire process chains are automated—not just individual steps.

Mastering Unstructured Data

Emails, PDFs, and free-form text are understood—not just structured forms.

Less manual work

Routine tasks run in the background—the team focuses on what adds value.

Faster Processes

What traditionally takes days can be done in minutes with AI.

Scalable

Volumes are growing without a linear increase in staff.

Error Reduction

Rule-based steps don’t make typos—AI makes consistent decisions.

What Is Intelligent Automation?

Intelligent Automation (IA) —also known as hyperautomation or cognitive automation —is the combination of traditional process automation with AI techniques to create a holistic solution.

Core components: RPA (rule-based automation), AI/ML (classification, extraction, natural language understanding), BPM (process orchestration, e.g., via Camunda), process mining (process analysis from data), and analytics (performance and exception monitoring).

The difference from traditional RPA: RPA follows fixed rules—it stops in unclear cases. Intelligent automation can handle uncertainty: LLMs understand unstructured queries, AI models classify data, and humans are only involved in genuine special cases.

For small and medium-sized businesses, IA is the pragmatic path to end-to-end process automation—with clear business cases in accounting, customer service, HR, and procurement.

RPA

Robotic Process Automation — automates rule-based clicks and data transfers.

AI Classification

Incoming documents and inquiries are automatically categorized.

Document Processing (IDP)

OCR and LLMs extract data from PDFs, invoices, and contracts.

BPM / Camunda

Process orchestration — controls the workflow using BPMN models.

Chatbots & Voicebots

Automate customer interactions—including handoffs to human agents.

AI Agents

Autonomous completion of multi-step tasks with access to tools.

Process Mining

Process Analysis from Event Logs — Identifying Opportunities for Optimization.

Analytics & Dashboards

Key performance indicators and exceptions — for continuous improvement.

Best Practices for Intelligent Automation

These six principles make IA successful:

Process Before AI: First, model the process clearly; then incorporate AI steps.
Start small: Choose a specific, clearly defined process—then scale up.
Involve the business unit: IA changes workflows—the business unit must help shape the process.
Plan for “human-in-the-loop”: There will always be edge cases—involve humans in a meaningful way.
Monitoring & KPIs: Make the automation rate, error rate, and turnaround time measurable.
Change Management: No success without adoption—plan roles, training, and communication.

Approach 1

RPA

Rule-based automation. Quick to implement for clear, structured processes. Has limitations with unstructured data.

Traditional

Approach 2