AI proposes, humans approve without always checking: three models to give everyone their proper role in the decision.
An AI sorts 300 job applications, replies to an unhappy customer, triggers a supplier order. Each time, the same question of accountability comes up. This module gives you the guidance to answer it: proven models for assigning roles, a slider to place AI in the decision loop, and the conditions for human accountability that is more than just words.
What you’ll take away
- the RAPID model for assigning five key roles for each team decision
- a three-position slider to set the role of AI, decision by decision
- the practical conditions for real human oversight, as required by the AI Act
Who it’s for
Managers who want to choose their decision model with AI, and team members who want to be fully accountable for what they approve.
Learning objectives
- Distinguish three models for clarifying roles (detailed job description, self-management, and mission and goals with RAPID) and when each one applies.
- Place each decision on the human–AI slider: in the loop, on the loop, or out of the loop, based on the stakes and reversibility.
- Recognize the traps that hollow out human accountability: automation bias and being a mere “fuse” who takes the blame.
- Create the conditions for a real human decision: understand, check, and be able to challenge AI while following the rules for its use.
How does this module work?
The steps of the Adaptive module.
- Self-assessmentOn nine dimensions, to gauge current habits and guide the practice.
- Expert videoA ~10 min video with three pauses for reflection.
- Practice scenariosThe first two scenarios chosen from the self-assessment, then sixteen more, optional. Every answer gets feedback: what it leads to, and one way to improve.
- Dated commitmentOne action to try, written by the learner or chosen from the suggestions.
- Memo to downloadKey takeaways, questions, commitment, personal notes, and checklists.
Duration: ~20 min. Excluding bonus practice scenarios and optional in-depth content.



