Using and applying AI in process management

AI in process management is particularly useful when it comes to automating recurring tasks, evaluating large amounts of data, or analyzing complex processes. In the “ training ,” you’ll learn how to identify specific challenges in business processes and derive appropriate AI use cases from them. The guide takes into account the benefits, effort required, and potential risks so that you can make informed decisions about where AI actually adds value.

Generative AI can structure information, create process descriptions, evaluate feedback, and identify opportunities for improvement. It can also reduce the manual effort involved in documenting and standardizing processes. Using real-world examples, the " training" demonstrates how such AI workflows can be applied in process management and how the results can be reviewed by subject matter experts.

The quality of AI results depends heavily on how clearly the task, context, goal, and desired outcome are defined. By using appropriate prompt engineering techniques, you can structure instructions more effectively and better identify common sources of error. As part of this training, you’ll also develop prompt templates for recurring tasks, enabling you to use generative AI more effectively in your day-to-day work.

A effective AI workflow requires a clear goal, appropriate information, defined steps, and transparent quality criteria. This training course teaches you a structured approach to designing and testing AI applications and evaluating them based on their benefits, effort required, and practicality. This will enable you to develop concrete solutions from initial ideas and better assess which applications are worth implementing in your company.

Even a technically sound AI solution will only be successful if employees and relevant stakeholders understand it, accept it, and use it in their day-to-day work. That’s why the “ training ” also addresses the organizational and communication aspects of implementation. You’ll learn how to identify potential barriers early on, engage stakeholders in a targeted manner, and communicate changes in a way that’s easy to understand. This increases the chances of successfully embedding AI-supported processes in the company over the long term.