Strategically Implementing AI-Driven Process Automation
From AI Applications to Automation: Workflows, AI Agents, and Enterprise Implementation
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Machine Learning Engineer
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.
Your digital add-on: In addition to the training, you’ll receive free access afterward to a guide on how to use AI as your personal learning transfer assistant or coach to successfully apply what you’ve learned to your daily work.
In your online learning environment, you will find useful information, downloads and extra services for this training course once you have registered.
- You can systematically identify and realistically evaluate suitable processes for AI automation.
- You make informed decisions when selecting AI tools and integrating them into the process.
- You'll understand how to effectively integrate AI into automation workflows—and where the limitations and common pitfalls lie.
- You are able to follow a structured sample workflow and apply it to your own contexts.
- You know how AI automation is organized and managed within a company.
- You will develop concrete strategies for implementing and further developing AI automation projects within your company.
- You'll take away: a prioritized automation use case, a workflow outline, and an implementation checklist covering data protection, IT coordination, and next steps.
The program alternates between keynote presentations, live demos, and hands-on exercises. You’ll implement a jointly developed sample workflow step by step and apply key elements to typical processes in your work context. You’ll learn about automation tools (such as Make, Zapier, N8N, and MS Power Automate) as well as LLMs and chat interfaces (such as ChatGPT and Claude).
process managers subject matter experts who have already gained some experience with AI and want to structure and drive automation initiatives within their organizations. Particularly suitable for process experts working at the intersection of business functions, digital transformation, and process management.
Eligibility requirements:
This advanced seminar is designed for participants who have some practical experience using AI tools (e.g., ChatGPT) as well as knowledge of process management.
Programming skills or experience with automation tools are not required.
Please bring a recurring process from your daily work that is time-consuming, error-prone, or highly manual. It’s enough to think about one or even several such processes. You’ll work on one of these processes in detail during the course.
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