Strategically Implementing AI-Driven Process Automation

This training on your foundational knowledge of using AI in process management and focuses on the development and evaluation of AI-driven automation, as well as its structured implementation and integration into your business processes.

Day 1 – Strategy, Use Cases, and Tool Selection

Understanding AI Automation in a Business Context

• The Role of AI in Process Automation.
• Traditional automation (e.g., RPA) vs. AI-driven approaches.
• Distinction: Experiment vs. Scalable Solution.

Systematically Identify and Evaluate Use Cases

• Criteria for suitable processes.
• Definition of a specific, custom use case.
• Define the target state, inputs/outputs, and a general process structure.

Assess Feasibility: Processes, Systems, Data

• Which systems are involved?
• What data is available?
• What interfaces are available?
• Where do typical obstacles arise?

Understanding Tool Landscapes and Making Informed Choices

• Comparison of Central Automation Platforms (Make, Zapier, N8N, MS Power Automate).
• Decision-making criteria for tool selection (cost, integration, scalability).

Day 2 – Understanding Workflows & Creating Prototypes

Fundamentals of AI Automation Workflows

• Setting up workflows (triggers, processing, output).
• The Role of AI in Processes.
• Presentation of a sample workflow for reference.

Hands-On: Building Your Own Workflow Prototype

• Let's build a complete sample workflow together.
• Applying selected elements to our own processes.
• Identification of limitations and next steps for implementation.

Ensuring the Stability and Quality of Automation Systems

• Identify common sources of errors in automation workflows.
• Implement simple measures to improve stability and quality.
• Develop an understanding of which requirements become relevant during day-to-day operations.

Day 3 – Implementation in the Company, Governance & Scaling

Organizing and Managing AI Automation in the Enterprise

• Roles, responsibilities, and collaboration between the business unit and IT.
• Centralized vs. Decentralized Implementation of Automation.
• Integration into existing processes and systems.

Consider Governance, Data Protection, and Risks

• Relevant legal frameworks (e.g., GDPR, EU AI Act).
• Typical risks associated with AI automation (data, quality, bias).
• Measures to minimize risk in your own project.

Evaluating and Preparing Automation Projects in a Structured Manner

• Assessment of processes, systems, and interfaces prior to a project.
• Assessment of technical, organizational, and legal requirements.
• Identifying specific requirements and next steps.

Transfer and Scaling in Practice

• Applicability to one's own work context.
• Prerequisites for further implementation.
• A discussion about experiences, unanswered questions, and challenges.