Process Automation – Clarity in the Jungle of Terms

Process automation is advancing rapidly. New technologies bring new terms that sound similar but have different meanings in detail. In this blog post, we compile the essential descriptions through comprehensive research in publicly available sources, publications, and provider information.

Process Automation

Intelligent Process Automation (IPA):

Intelligent Process Automation (IPA) is a combination of technologies for capturing, analyzing, automating, and managing digital processes. The software becomes intelligent through the use of artificial intelligence, which is employed to make predictions and respond faster to (new) data. This sets IPA apart from other forms of process automation, especially the overarching term Digital Process Automation.

Digital Process Automation (DPA):

Digital Process Automation (DPA) is a combination of technologies for capturing, analyzing, automating, and managing digital processes. DPA includes technologies such as interface-based or robot-controlled process automation. DPA focuses on the automation or partial automation of tasks related to business practices that typically require human interaction.

Interface-Based Process Automation:

Process automation involves the use of software for the automatic execution of various processes, which means using information technology tools to control, regulate, optimize, monitor, and guide these processes or the technical objects, work and production resources in which they take place. Interface-based process automation occurs system-to-system using interfaces accessible to both systems. Dies unterscheidet Sie z. B. von Robotic Process Automation.

Source: Meister Automation,


Business Process Management (BPM):

Business Process Management deals with the identification, design, documentation, implementation, control, and optimization of business processes.

Business Process Model and Notation (BPMN 2.0): BPMN 2.0 (Business Process Model and Notation Version 2.0) is the current standard for business process modeling.


Workflow and Case Management are Essential for End-to-End Orchestration and Automation of Processes


A workflow is a work process, usually with assigned responsibilities and transition guidelines. The term also refers to the smooth flow of business processes.

Workflow Management:

Workflow management is the IT support or (partial) automation of business processes. The task of workflow management is to ensure the execution of workflows using IT systems based on a specification. Workflow management is a way to perform process automation and technically support business process management.

Workflow Management Engine:

A software application that manages and monitors all business processes involved in workflow management. The states of individual workflows are crucial, as they determine whether and which actions are triggered in the next step. Workflow engines have three primary functions:

  1. Continuously check if the current process status is correct and valid.
  2. Control whether the current user is authorized to execute the task.
  3. Execute the task if points 1 and 2 are fulfilled. If the task is executed as desired, the workflow engine communicates the new task status. If the task is not completed as specified, it returns an error message.

Source: Echolon,

Case Management:

Case management refers to the control of a specific case situation. In process automation, this means automating a case-specific process that can be unstructured and different each time. Case management systems support and guide the case handler through the work steps.

RPA as a Complement to Workflow Management

Robotic Process Automation:

Robotic Process Automation (RPA) is an approach to process automation where activities/tasks are learned and automated by so-called software robots. Software robots primarily use application software through the presentation layer, similar to how a human performs tasks. This makes Robotic Process Automation usable even when no accessible interfaces exist for systems.

Low-Code Driving Factor for Process Automation

Low-Code Software:

Low-code software uses graphical code elements such as drag-and-drop elements. However, complex rules still need to be added using low-code (e.g., if statements). This specific graphical development environment eliminates the need for self-written code, simplifies the development of new software for professional developers, and makes it possible for business users.

No-Code Software:

No-code software exclusively uses graphical code elements such as drag-and-drop elements. All settings can be made graphically. This specific graphical development environment eliminates the need for self-written code.

Business Developer:

Business developers are non-technical users with limited or no technical background who use low-code software as developers. In process automation, business developers usually work with low-code or no-code applications that provide strong visual support. A classic example would be a business user who has only used Excel functions with simple syntax, such as if functions.

Citizen Developer:

Citizen developers are non-technical users with some technical understanding who use low-code software as developers. They may not have completed an IT degree but have technical knowledge. In process automation, citizen developers typically work with low-code applications that provide visual support but require short scripts, such as rules, to be added. A classic example would be a business user who has previously used Excel macros.

Professional Developer:

Professional developers are IT users who work as developers with or without low-code software. In process automation, professional developers typically work on data integration, complex process rules, or the implementation of software solutions in operation. However, low-code software can also serve as a supportive tool for professional developers to create, test, and deploy software more quickly.

Central Data & Document Management for Process Automation

(Enterprise) Content Management:

Enterprise Content Management (ECM) includes strategies, methods, and tools for capturing, managing, storing, preserving, and providing content and documents to support organizational processes in a company. ECM brings together structured, semi-structured, and unstructured information. ECM is an umbrella term for input management, document management, and output management.

Input Management:

Input management is an approach to digitally capture business-relevant data. Input management software captures and extracts data to make it usable for other software, such as process automation. Detection, extraction, and validation of data often involve the use of artificial intelligence. High-quality input management can capture and analyze any form of structured and unstructured data for further processing in process automation or ECM systems.

Document Management:

Document management refers to the (database-supported) management of electronic documents of any kind. These systems are used to manage files with check-in/check-out and versioning, as well as document storage.

Form Management:

Form management is a subset of document management and refers to the creation and management of forms needed for use and execution in process automation. Data contained in forms often serves as triggers for workflows.

Output Management:

Output management involves the creation, generation, control, and distribution of electronic or physically existing documents to all intended recipients within or outside a company. In process automation, this means forwarding the process results to process customers and ensuring that the data processed in the process is stored in ECM systems and databases used by the company.


Artificial Intelligence and Machine Learning are Important for Data Recognition & Automation

Artificial Intelligence:

Artificial intelligence (AI) refers to technologies that complement and strengthen human capabilities in seeing, hearing, analyzing, deciding, and acting. AI is often used as an umbrella term that encompasses several technologies, including machine learning, deep learning, computer vision, and natural language processing (NLP). These technologies, individually or in combination, make applications intelligent.

Source: Microsoft,

Machine Learning:

Machine learning (ML) is a subfield of artificial intelligence (AI). Algorithms can recognize patterns and regularities in datasets and develop solutions from them.

Source: Microsoft,

Analysis of processes, whether static or dynamic, retrospective or real-time, is crucial.

Process Intelligence:

Process intelligence is the overarching term for the visualization and analysis of business processes.

Workflow Intelligence:

Workflow intelligence is the visualization and analysis of business processes that have been mapped and automated in a workflow application. Workflow intelligence primarily represents process data in static diagrams and tables that do not have a direct process reference.

Process Mining:

Process mining is the visualization and analysis of business processes based on event logs using specific process analysis algorithms. Process mining can be applied to any process as long as the necessary data points are available. Process mining primarily represents process data in process models (process variants).

Event Log:

Event logs are records of IT-based processes. Typically, they record the case identification number (Case-ID), a timestamp for start and end times, and the activity name. Process models can be derived from and analyzed using these data points. The recorded data points are usually at the task level.

Task Mining:

Task mining is a technology that allows companies to capture and analyze user interactions with graphical interfaces. The recorded data points are typically at the activity level and can be used to automate activities, especially through RPA.

Authors: Harald Feick & Philipp Hässig



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