Managing People Who Use AI Every Day
Set fair standards for review, credit and workload when your team works with AI daily
A taste of a lesson
My team says AI saves them hours. My director wants us to take on 30 percent more work. Is that fair?
Not until you have measured it. Self reported savings are often generous, vary a lot by task, and usually leave out checking and rework time. Pick three common tasks, time them for a couple of weeks with the current approach, and compare with the earlier baseline if you have one. Then propose a change based on that evidence, possibly reinvesting some time in quality. Which three tasks would give your director the most convincing picture?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Make accountability for AI assisted work clear to everyone on the team
- Agree disclosure norms and review standards that match the level of risk
- Give credit and feedback based on judgement and outcomes
- Change workload expectations only on measured evidence
- Protect junior staff's skill growth while they use AI
Lesson plan
- 1 You Own What You Submit Establish that people are accountable for their work regardless of the tools used. Start
- 2 Disclosure and Review Standards Agree when AI use is disclosed and how much checking each type of work needs. Start
- 3 Fair Credit and Useful Feedback Recognise good judgement in AI assisted work and give feedback on how it was done. Start
- 4 Workload and Targets Adjust expectations only after measuring real time savings on specific tasks. Start
- 5 Growing Junior Staff Make sure newer team members still build the judgement needed to review AI output. Start
- 6 Writing Your Team Agreements Draft a short set of team norms and check them against policy and law. Start
Try asking
About this tutor
For managers and team leads whose people already use AI in their daily work. The lessons cover making accountability for AI assisted output clear, agreeing team norms on disclosure, setting review standards proportionate to risk, giving fair credit, resisting the urge to simply raise targets, giving useful feedback on AI assisted work, protecting the growth of junior staff, and handling privacy questions about monitoring. You work on real situations from your own team and leave with a short set of team agreements. No technical background is needed.
Reviews
4.7
3 ratingsSample
- Anna P.Sample
The point about juniors losing practice hit home. Two of my newer analysts now write first drafts themselves and we compare them with the AI version afterwards.
- Elena G.Sample
The five line team agreement is now pinned in our channel. I wanted more on handling a specific disclosure argument with one client, but the general approach was sound.
- Chidi N.Sample
Helped me answer my director with numbers instead of feelings. We timed three tasks, and the savings were real for one and close to zero for another once review was included.
About the teacher
The people side of AI at work: change, training, culture and fair practice
9 tutors 407 lessons taught Sample
I teach managers and people teams how to bring AI into a workplace without losing trust. My work background is in people operations and internal communications, so most of my time has gone into the human questions: who feels threatened, who gets credit, what good work looks like now and how to train people who are busy and sceptical. I...
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