- Update the skills of managers and team leaders for an environment that works with AI tools.
- Learn how to effectively identify the team’s attitude towards AI and key opportunities.
- Master the specifics of managing people and tasks in an environment where people work with AI.
- Improve the quality control of outputs and create a feedback system for working with AI.
- Support people in learning and using AI safely.
- Understand the strategic aspects of working with AI, its priority use and managing the team’s mission.
Manager in the AI era – new challenges and opportunities for teams
- Why the role of leadership is changing in the AI era
- How the job of a manager and expectations from him are changing
The team’s background and attitude towards working with AI
- AI SWOT Analysis with Team Engagement
- How to Lead a Discussion on Key Insights, Opportunities, and Concerns
- Important Deliverables for a Manager and How to Work with Them
Concerns about AI
- Identifying AI concerns in the team (loss of work, error rate, loss of expertise, etc.)
- Managerial approaches to working with identified concerns
- Open communication, visualization of benefits, appreciation of proper use
People and task management
- How to identify suitable tasks for AI
- Task planning – “AI-first” and “human-first” tasks.
- Task assignment in a hybrid (human / AI) environment
- How to maintain clear goals and expectations when working with AI
- The impact of AI on team capacity, capacity planning
AI output quality control and feedback
- How to detect inaccuracies, hallucinations and risks
- Setting team standards when working with AI (peer-review, fact-check, data validation)
- How to give feedback on AI outputs
- Practical tips for constructive feedback in hybrid work
Motivation and team development
- How to support learning and experimentation with AI
- Reverse mentoring method
- Building good practices (AI standards, library of quality prompts, etc.)
- Space for personal growth in a hybrid environment
Data security and protection in practice
- The role of the manager in AI security, data and confidential information protection, other risks
- Data sorting, rules for entering data into LLM, approval of AI tools
A manager’s strategic perspective
- Naming areas for priority deployment of AI
- How we will use the acquired capacity
- Proactivity and stakeholder management when working with AI