Course Code: aireadyhf
Duration: 7 hours
Prerequisites:
- Basic familiarity with everyday workplace workflows (email, docs, meetings).
- Helpful (not required): prior exposure to AI tools such as ChatGPT or Microsoft Copilot
Audience
- Team Leaders and Middle Managers
- Project / Product Managers
- Heads of Functions (Operations, Customer Service, Sales)
- HR Business Partners
Overview:
AI succeeds or fails in teams because of human factors: trust calibration, accountability, decision habits, and psychological safety. This instructor-led, live training (online or onsite) is aimed at team leaders and middle managers who want to adopt AI safely and effectively, without creating hidden risks, friction, or loss of trust.
Delivery language: English, German, or Italian (trainer-led, online or onsite).
By the end of this training, participants will be able to:
- Define when to use AI and when not to (use-case boundaries and stop rules).
- Set practical verification standards for AI outputs (evidence level, second source, red flags).
- Establish clear accountability and decision hygiene (owner, escalation triggers, decision log).
- Create a team AI Working Agreement (team agreements that are simple, enforceable, and auditable).
- Manage trust and psychological safety during adoption (scripts for difficult conversations and resistance).
- Run a light incident response for AI-related mistakes and near-misses (contain, learn, update rules).
- Build a 30-day rollout plan with team rituals and ownership.
Format of the Course
- Interactive lecture and facilitated discussion.
- Practical exercises using realistic workplace scenarios.
- Workshop: build your own Team Agreements and adoption plan.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Format of the Course (time split)
50% lectures, 45% labs, 5% checks.
Course Outline:
Prerequisites
No technical background required. Helpful (not required): basic familiarity with AI tools such as ChatGPT or Microsoft Copilot.
Audience
- Team Leaders and Middle Managers
- Project / Product Managers
- Heads of Functions (Operations, Customer Service, Sales)
- HR Business Partners (optional)
Introduction (Human Factors in AI Adoption)
- Why AI adoption fails in real teams: human factors, not tools
- Trust calibration: under-reliance vs over-reliance (automation bias)
- Accountability: “AI can assist, humans remain responsible”
1. Calibrated Reliance (Safe Use in Daily Work)
- Use-case boundaries: what is appropriate for AI, what is not
- Stop rules: when to pause, verify, or escalate
- Common failure patterns and early warning signs
2. Verification Standards (Quality Without Slowdowns)
- Practical verification levels (light, standard, strict)
- Red flags: hallucinations, outdated facts, missing sources, sensitive content
- “Second source” and traceability basics (what to log)
3. Accountability and Decision Hygiene
- Ownership: who validates, who decides, who signs off
- Escalation triggers and decision thresholds
- Decision log: minimum evidence and documentation
4. Team Agreements Workshop (Core Deliverable)
- Working agreement structure: trigger, action, evidence, owner, consequence
- Examples for common workflows (emails, analysis, customer comms, internal docs)
- Aligning agreements with company policy and confidentiality rules
5. Trust and Psychological Safety
- Typical fears: replacement, loss of competence, loss of status
- Manager scripts: how to talk about AI without hype or panic
- Conflict patterns: “pro-AI” vs “anti-AI” and how to de-polarize
6. Light Incident Response (AI Mistakes and Near-Misses)
- Classify incidents: low/medium/high impact
- Contain and communicate (internally and with customers when needed)
- Learning loop: update agreements, templates, and rituals
7. 30-Day Adoption Plan
- Team rituals: weekly check-in, prompt review, incident review, decision review
- Metrics that matter: adoption quality, rework, escalations, trust indicators
- Next steps and follow-up plan