When AI starts taking over part of a team’s work, three things suddenly become less clear for the manager: how the output is produced, how long different tasks actually take, and who is ultimately accountable for the result. This training focuses on helping managers regain control over all three – by defining how work is divided between people and AI, clarifying who approves the output, and setting clear standards and performance indicators for AI-supported work and its results.
Participants will develop team standards for reviewing outputs and using AI effectively and safely. They will also practise giving feedback on work produced with the support of AI.
- Learn how to effectively divide work between people and AI.
- Clarify accountability for outputs – who approves them and according to which criteria.
- Introduce performance indicators that maintain both speed and quality.
- Increase confidence when giving feedback on work supported by AI.
- Establish team standards for reviewing outputs and using AI safely.
- Learn how to redefine a team member’s role when AI takes over part of their responsibilities.
The Manager in a Team Where AI Does Part of the Work
- Why the limits of what AI can handle cannot always be assessed from the outside
- The difference between how quickly a team actually works and how quickly it thinks it works
- Why the same AI tool can affect a junior and an experienced team member differently
- What this means for everyday performance management
Dividing Work Between People and AI
- How much can AI do independently? Four levels of AI involvement and when each is appropriate
- The control point: where in the process does a person review, approve or stop the output?
- Who signs off on the output and what this responsibility entails
- Assigning tasks so that it is clear what can and cannot be delegated to AI
- What makes human review effective – and what turns it into a mere formality
Measuring Performance Without Being Misled
- Why actual AI adoption cannot be measured by the number of licences or employees trained
- A pair of performance indicators: speed and quality of output verification
- How performance indicators can be manipulated – and how to recognise this in advance
- Team capacity: how to identify overload even when the team appears to be keeping up
Team Standards for Working with AI
- Where the team can use AI without additional review and where outputs need to be checked by a colleague
- Where source verification is mandatory
- Where AI should not be used at all and what information must never be entered into AI tools
- What the manager is accountable for and when an issue needs to be escalated
Feedback and Roles in a Team Where People and AI Work on the Same Output
- Discussing an output that clearly shows signs of AI involvement – participants practise the conversation in pairs, taking turns as the manager and team member
- Why asking “Did you use AI for this?” can derail the conversation
- How to separate evaluation of the output from evaluation of the person
- What changes in a team member’s role when AI takes over part of their responsibilities
- What can go wrong when redefining a role by removing precisely those simpler tasks that may have provided learning, context or a sense of progress