When software produces the first draft of a report, a new colleague may see the result without learning how the information was selected or checked. That is a training problem worth examining. It does not prove that all entry-level jobs are disappearing or that every employer has stopped developing people.
If you have years of experience, you can help make the reasoning visible. The same approach can support a younger colleague, someone returning after a career break, or an experienced worker moving into an unfamiliar role.
Identify the judgment hidden inside the task
Choose one recurring piece of work and ask what a learner needs to understand beyond its final appearance. A weekly project update, for example, may require distinguishing an estimate from a commitment, finding conflicting notes, and deciding who should confirm a missing fact.
Write those abilities down before assigning a tool. Otherwise, training can become a lesson in obtaining a polished answer rather than recognizing whether that answer is usable.
Do not assume speed means understanding. Someone may operate the interface confidently while still needing help with the underlying work.
Protect time with an experienced person
Name a mentor or reviewer and give them time for the role. Agree when the learner can ask questions and which decisions require supervision. AI-generated feedback can support a discussion, but it cannot approve someone as competent for your workplace.
For context, U.S. Registered Apprenticeship programs combine paid work, structured learning and mentoring with other defined elements. The informal exercise below is not a registered program or a qualification. It borrows no claim to that status.
Try an explain, attempt, compare routine
- The experienced person demonstrates a small example and explains their decisions, including what would make them stop.
- The learner attempts a similar example using approved reference material and the support their role requires.
- AI produces a separate draft from the same fictional or permitted input.
- The learner compares both drafts with the source, identifying omissions and uncertain statements.
- The reviewer discusses the differences and asks the learner to explain the final changes.
This is a suggested practice routine, not evidence that one training method works for every job. Adapt it to the task and the learner’s needs. Do not withhold necessary accessibility tools or required assistance to make the exercise “independent.”
Use an example with something to question
Here is a fictional exercise for a project update. One note says, “The new form is ready for review.” A second says, “Testing has not been completed.” A third says, “The team hopes to launch next week.”
Ask the learner to write three sentences that preserve those distinctions. If AI changes the notes to “The tested form will launch next week,” the learner should be able to identify what changed and which claim needs confirmation.
Review my draft against these fictional source notes. Point out statements that change certainty, omit a limitation or introduce a new fact. Ask me questions about those differences before suggesting revisions. Do not score my professional competence or invent the missing information.
The mentor checks the AI’s criticism too. It may miss the important error or object to something that is actually correct.
Increase responsibility through observed work
Keep a brief learning record: the task, the evidence checked, help required and the next practice goal. Look for the ability to explain a decision, recognize an exception and ask for assistance. Avoid relying on a generated “readiness percentage.”
Move to live work only through the organization’s normal approval and supervision process. Completing a practice example does not establish competence for safety-critical, regulated or sensitive tasks.
Use our work-instruction drafting process to capture the approved steps. Make room for the learner to question instructions that seem inconsistent or out of date.
Check what automation removed
Ask the team which useful learning experiences have become less frequent: finding a source, checking a calculation, explaining an error or talking with the person who uses the result. Deliberately include those experiences where they still matter.
If you are the learner, ask for a worked example, a supervised attempt and feedback on your reasoning. A good question is: “What would you need to see me do before you were comfortable with me handling this?”
The goal is supported progress, with evidence of what someone can do. Technology may change the task, but training still needs a person who takes the learner’s development seriously.
Reviewed September 30, 2026. Prepared with AI assistance and checked against the sources linked in this article. Examples are illustrative. Read our editorial and information disclaimer.
Illustrative photo by TheStandingDesk on Unsplash.
