Assistant Professor
Systems Thinking for AI & Automation
What You'll Learn
- Determine which workflows to automate and how, using proven assessment frameworks.
- Explore different tools that support the automations you want to incorporate.
- Interpret and adapt AI-generated outputs for practical use in personal and professional life.
- Design workflows that use quality data you provided to support scalable, AI-ready practices.
3 Modules
3 Hours
1 hr per module (approx.)
About Systems Thinking for AI & Automation
AI can help you work faster—but only when it's applied to the right problems. In Systems Thinking for AI & Automation, you'll learn how to identify tasks worth automating, avoid common automation mistakes, and create workflows that save time and reduce manual effort.
Through hands-on activities, you'll apply the IDEA framework—Identify, Document, Experiment, Adjust— a practical approach to making more thoughtful automation decisions. You'll explore the technologies behind modern automation, including no-code tools, low-code platforms, APIs, AI-assisted scripting, and generative AI. You'll also learn how to organize information and knowledge so AI tools can find, use, and build on it more effectively.
Whether you're looking to streamline your work, improve team processes, or make better use of AI tools, this course will help you design practical, scalable tools that support productivity today and adapt to future advances in AI and automation.
This is the second course in the Applied AI: Data Analysis, Workflows, and Decisions series, a three-course series on practical ways to integrate AI into your personal and professional routines.
Skills You'll Gain
- Critical Thinking
- Data Literacy
- Data Validation
- Decision Making
- Problem Solving
- Strategic Thinking
- Systems Thinking
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Systems Thinking for AI & Automation.
This course will help you develop practical strategies for integrating AI and automation into everyday workflows. Through hands-on activities and guided explorations, you'll learn how to evaluate which tasks are worth automating, estimate returns on investment (ROI), and avoid common pitfalls when deciding which processes to automate.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module One: To Automate or Not to Automate?
- Evaluate which tasks are worth automating using a structured decision framework.
- Estimate the return on investment (ROI) of automating repetitive tasks.
- Reflect on when automation creates value and when it may introduce hidden costs.
Module Two: Automation Ecosystems: Tools, APIs, and Access
- Compare different approaches to automation, from no-code platforms to custom scripts.
- Understand the role of APIs in connecting systems and enabling automation.
- Recognize how AI can extend automation tools by generating or adapting code.
- Explore how to use GenAI tools to generate examples safely.
Module Three: Capturing, Representing, and Accessing Information
- Explain why capturing information systematically is a form of automation.
- Understand the principle of representing information so AI can access and reuse it (e.g., embeddings).
- Identify tools and workflows that make accessing information low-friction.
- Reflect on how to balance usefulness with simplicity.
Grading Policy
There are three quizzes in this course, each worth 33.33% of your final grade. You must earn an overall grade of 80% in order to pass the course and earn the certificate.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
Basic familiarity with computers and internet search is recommended.
