Systems Thinking for AI & Automation

Overview

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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.

Syllabus

To Automate or Not to Automate?

In this module, you will explore how to evaluate which tasks in your daily workflow are worth automating using a structured decision framework. You’ll assess potential time savings and reflect on where automation adds value, or carries hidden costs, so that you can choose the right tasks to optimize.

Automation Ecosystems: Tools, APIs, and Access

In this module, you will explore the landscape of automation tools, from no-code platforms to AI-assisted scripting, and understand how APIs enable systems to connect and communicate. Through a guided practice, you will learn to use GenAI tools to generate automation examples and evaluate them safely.

Capturing, Representing, and Accessing Information

In this module, you will discover how structured information capture can serve as a powerful form of automation. Our goal is to rethink how you can store and retrieve knowledge by making your data AI-ready, low-friction, and semantically searchable.