Updated October 14, 2026
An AI learning plan should help people complete a defined task and explain how they checked the result. Reading, demonstrations and courses can provide background. Connect that learning with practice on real types of work, specific feedback and clear limits on what participants may do.
Start with a capability the team needs now. Keep the exercise small enough that someone can review the work properly and identify what needs to improve.
Choose a task and a learning target
“Learn AI” leaves too much open. A more useful target is to prepare a short summary from approved documents, verify each material statement and identify information that is missing.
Write down what a competent result would contain. Include the checks the person must perform, the information they may use and the actions requiring approval. Use the practical machine-learning guide to clarify which type of tool might suit the task.
Practise within an agreed boundary
Select a small set of approved examples, including an incomplete or ambiguous case. Decide who can access the material and keep the exercise separate from customer commitments or changes to live records.
Ask participants to retain the input, relevant instructions, output and their own corrections. Record the tool and version where available. This gives the reviewer enough context to discuss the work rather than guessing how an answer was produced.
Give feedback on the work
Review an example together. Identify a claim without support, an important omission or a step that required unnecessary effort. Explain the consequence and agree on a specific change to try.
A fluent answer can still need correction. Ask the participant to point to the evidence, explain what remains uncertain and describe when they would refer the task to someone else. Leave room for questions about the exercise itself; unclear instructions may need attention before another attempt.
Turn learning into something reusable
Keep a short learning log: the task, what went wrong, what changed and what the next attempt showed. Capture useful checks in a job aid with a named owner and revision date.
Share the reasoning behind a correction as well as the corrected output. Another colleague should be able to understand when the guidance applies and where its limits lie. Remove examples containing information the wider team should not receive.
Check the capability on a different case
Use another approved example to see whether the person can apply the checks independently. Review the result against the same criteria and record any support still needed.
Agree on the next step: further practice, a narrower task or permission to work within a defined scope. Revisit the guidance when the tool or workflow changes. The aim is a team that can explain and review its work, with a record that makes the next learning cycle useful.
Discuss practical AI learning and adoption with Daly Ventures.
