Get the Most Out of This Course

Before you learn to take a workflow apart, take a few minutes to see the whole picture: what this course gives you, how it is built, and how to work through it so the skill actually sticks. A little orientation now makes every chapter that follows easier to use.

Discover the Course Objectives

Almost everything we do together now runs through a chain of digital tools, and increasingly through AI as well: a form to collect information, a spreadsheet to hold it, a chat to organize people, an AI assistant to draft or summarize. When that chain works, the effort feels effortless. When one link quietly fails, a capable group can still end up with a wrong result and no idea where it came from.

This course builds the skill of reading those chains and keeping them trustworthy. You will learn to look at a real, everyday effort, see how its tools and people connect, find where it is likely to break, judge whether its outputs can be trusted, and repair problems at their true source rather than patching the surface.

The point is not to memorize theory. It is to walk away with a method you can apply to a real effort of your own, whether that is a community event, a student project, a volunteer crew, or a shared hobby. Wherever people and tools depend on each other to finish something, this is the skill that keeps the result honest.

By the end of this course, you will be able to:

• Map a real digital and AI workflow, showing tools, actors, inputs, outputs, and handoffs, and locate where AI acts ;

• Analyze a workflow for fragility, spotting single points of failure, manual re-keying, stale data, and unclear ownership ;

• Evaluate digital and AI outputs against explicit criteria for correctness, completeness, and appropriateness ;

• Troubleshoot workflow problems methodically, separating a symptom from its root cause, then fix or escalate ;

• Apply human judgment with an accept, fix, escalate, or override decision, and turn a one-off fix into a lasting improvement.

Meet Your Teacher

This course was created by the OpenClassrooms Team with the help of artificial intelligence.

Discover the Course Plan

The course runs in two parts, and they follow a deliberate order.

Part 1 is about analysis. You will learn to map a workflow end to end, naming the tools, the people, what goes in and comes out at each step, where work is handed from one person or tool to the next, and where an AI assistant is doing part of the job. Then you will learn to spot the weak points in that map, the places most likely to fail.

Part 2 is about evaluation. You will learn to judge the outputs a workflow produces, decide whether they are actually correct rather than just polished, troubleshoot problems back to their real source, and make a clear call about what to accept, fix, escalate, or override.

Analysis comes before evaluation for a simple reason: you cannot fairly judge or repair a workflow you cannot yet see. Mapping it first gives you the picture you need to know where to look when something seems off.

Know What You Need to Begin

You need no prior experience with technology projects, no special software, and no background in AI. This course is written for true beginners, and every new term is defined the first time it appears.

Everything you practice can be done with free and commonly available tools: a free form builder, a shared spreadsheet, a group chat, and a free AI assistant. You will never be asked to use a paid product or a private work system, so you can follow along whatever your situation.

If you would like extra background, two optional companion courses sit comfortably beside this one: Understanding the Web, which explains how the internet and online tools connect, and Adopt the Right Mindset for Working With AI, which builds a healthy, critical stance toward AI outputs. Neither is required to start, and this course defines what it needs as it goes.

Get the Most Out of Each Chapter

Work through the chapters in order. Each one builds on the last, and the method only holds together when the pieces connect.

Read the worked examples closely. They walk through the method on real, everyday efforts, from a student club to a community event, so you can see each move in action before you try it yourself. When you reach a practice prompt, attempt it before you check the model solution provided at the end of that chapter. The thinking you do on your own is where the learning actually happens.

Each part also ends with a short quiz. These checks are there only for you: they help you confirm what has landed and spot what to revisit. Nothing is graded, so use them honestly rather than anxiously.

Take your time, stay curious, and keep testing each move against your own effort. By the end you will have a method you trust for making sense of any digital and AI workflow, and the confidence to act when one of them breaks.

With the big picture in view, let’s begin the method itself by defining what a workflow really is, and how a single tool, a workflow, and a system differ.