A training program can present many features and leave the team unsure where to go next. To build an AI learning path, pick a piece of work that runs through the activities and lets you see what each stage added.
A team that prepares meetings with suppliers, for example, already knows the documents and the recurring questions. That material becomes the basis for learning to talk to AI, organize context and test a usable deliverable.
Five stages, five pieces of evidence
- Get to know the environment.
Where files go in, how to check the deliverable and which information the company allows you to use. Evidence: being able to start and resume a task. - Explain purpose, audience and criteria.
The team compares two summaries of the same materials and identifies what was missing from the first request. - Organize the context.
Current documents, definitions and examples are easy to reach. Evidence: someone else picks up the work without getting the whole explanation again. - Explore deliverable formats.
A supplier comparison may work better as a table or an interface than as paragraphs. The choice follows the intended use. - Apply it.
Prepare a real meeting, review the result and record the corrections.
Each stage leaves behind material that can be examined.
Match the depth to each person's role
Whoever runs the area needs to recognize possibilities and evaluate proposals. Whoever will operate a routine also needs to run it and fix failures. The path can share examples and use different exercises for these responsibilities.
At the end, ask the team to show one input, one deliverable, one correction and the reason for that correction. That way you can talk about the work actually done, not just the impression that the training was interesting.
From the perspective of AI Thinking by Dani Hayoshi, learning about AI also means widening your view of what can change. A learning path continues when it leaves behind a better question and an application the team can come back to. To choose an outside program, see Corporate AI training.
Dani Hayoshi is the author of AI Thinking. Her work connects artificial intelligence, education and entrepreneurship. About Dani.