Make AI training part of useful daily work

Choose work you can practise

Choose one task you do often and put a short practice session after your next AI lesson. Use an example whose result you can check, then note what helped and what needed correction.

TalentLMS released workplace learning research on October 27 that looks at what happens after training. The findings offer a useful prompt for anyone learning AI, introducing a new tool or choosing practical training for a team.

Read the announcement: TalentLMS: Habit Hugging report and survey methodology.

Look at what the survey measured

The online survey covered 1,500 full-time employees in the United States in September 2026. Respondents were aged 25 to 64 and had received training on a new tool, technology or way of working in the previous year. They were not a sample of all AI users.

Across the new ways of working they had been taught, 41% said they used them consistently. Half said making the new approach part of everyday work would help, while 49% wanted more opportunities to practise.

TalentLMS sells training software. Its research records what people said about their experience, rather than observing their work or testing which teaching method works best. The results cannot show that every team faces the same barriers.

Make learning visible in the workday

The survey also found an association with how managers worked. Consistent use of a new approach was reported by 49% of employees whose managers used it too, compared with 22% where managers did not. That does not prove the managers caused the difference.

For a team trying AI, a practical response is to learn together. A leader can show the task they tried, the result they checked and the correction they made. Invite people to explain where the tool adds work or leaves them unsure.

Give people time to become familiar with the task. If the new approach is awkward, use that feedback to improve the instructions or reconsider where the tool belongs. The useful outcome is better work that people understand.

Try a follow-up you can repeat

Use made-up or approved information for your first practice. A short summary of a familiar document is enough to begin. Keep the original nearby so you can check names, dates and missing details.

1. Choose one repeated task and describe what a useful result should include.
2. Try it after the lesson, keeping the source information beside the answer.
3. Check accuracy and note any extra work needed to correct the result.
4. Adjust the instructions, then make time to try the task again.
5. Discuss whether the approach helped and what guidance you still need.

Ask what happens after the lesson

Our perspective is that practice and review will remain useful as AI tools change. A familiar task gives you a way to judge a new tool without starting your learning from the beginning each time.

If you are choosing training, ask how it connects to the work you want to improve and what support follows the lesson. If you are building alongside someone, bring your example and the questions it raised. Those details make the next session more useful.