Agile management in the AI era
The program was developed taking into account key international approaches and practices of Agile management, in particular the principles of ICAgile, Scrum.org, and PMI Agile Certified Practitioner (PMI-ACP), with a focus on the practical application of Scrum, Kanban, flow management, value creation, and adaptive team management in the context of AI transformation.
Course Objective
To teach managers and leaders how to use Agile approaches alongside AI tools for faster decision-making, effective planning, and increasing team productivity.
Who is this course for?
- team managers
- product and project managers
- analysts
- heads of departments
- founders
Course Outcomes
- a set of AI management tools
- Agile process templates
- AI templates for planning, organizing, controlling, and motivating
- a roadmap for Agile + AI implementation
What will you get from the course?
Program
- What is Agile really: values, principles and mindset
- Agile Manifesto
- From task management to value creation
- Empiricism: transparency, inspection, adaptation
- How AI changes the work of teams and managers
- Human + AI: a new model of teamwork
Practice: assessment of own work process from an Agile position mindset.
- Scrum Values
- Scrum Team: Product Owner, Scrum Master, Developers
- Product Goal, Sprint Goal
- Product Backlog and how to work with it
- Sprint
- Definition of Done
- Increment
- Scrum Events
- Inspection & Adaptation
Practice: building a Scrum process on a real case.
- Product Backlog as a value creation tool
- User Stories
- Acceptance Criteria
- Product Goal and Sprint Goal
- Prioritization
- Backlog Refinement
- Estimation
- How AI can help the Product Owner and the team:
- requirement analysis
- decomposition
- generation User Stories
- Acceptance Criteria
- search for risks and dependencies
Practice: creation and improvement of Backlog using AI.
- Kanban mindset
- Flow and flow management principles
- Visualize Workflow
- Work In Progress (WIP)
- WIP Limits
- Pull System
- Cycle Time and Lead Time
- Bottlenecks
- Continuous Improvement
- Scrum vs Kanban: when which approach use
- Scrumban
Practice: building a Kanban board and searching for bottlenecks.
- Planning in a complex and uncertain environment
- Release / Iteration / Sprint Planning
- Estimation and Forecasting
- Story Points
- Velocity
- Cycle Time
- Throughput
- Burndown / Burnup
- Data-driven Forecasting
- How AI can help analyze historical data and make forecasts
Practice: delivery forecasting based on team data + AI analysis.
- Self-managing teams
- Collaboration instead of command & control
- Role of Scrum Master / Agile Coach
- Facilitation
- Feedback
- Daily Scrum as a synchronization tool
- Sprint Review and receiving feedback
- Retrospective
- Continuous Improvement
- AI as a facilitator / assistant for teamwork
Practice: holding a retrospective + using AI to analyze team feedback.
- Value-driven development
- Customer / User Value
- Product Discovery
- Hypotheses & experiments
- Feedback loops
- MVP
- Prioritization by value
- Discovery + Delivery
- AI for analyzing customer feedback, forming hypotheses and searching for insights
- How not to turn AI into a "generator feature"
Practice: form Product Goal → hypotheses → backlog → experiment using AI.
- Agile as a way of organizing work, not a set of ceremonies
- Dependencies and cross-functional collaboration
- Scaling Agile
- Scrum + Kanban at the level of the organization
- Flow at the level of teams and products
- Metrics that matter
- Continuous improvement
- How AI changes the management of teams and processes
- The role of the manager in AI-enabled organization
- Roadmap for the transition to Agile + AI
Practice: creating your own Agile + AI Action Plan.
FAQ
The course is intended for Project and Product Managers, Scrum Masters, Team Leads, Business Analysts, team leaders and specialists who work in product and project environments and want to effectively apply Agile approaches in the context of AI transformation. Participants will systematize knowledge of Agile, Scrum and Kanban, learn to manage workflow, plan and forecast delivery, work with Product Backlog and team metrics, and use AI to improve management efficiency.
The program covers Agile mindset, Scrum, Kanban, Scrumban, Product Backlog, User Stories, prioritization, planning, evaluation, flow management, Agile metrics, team interaction, Product Discovery and Continuous Improvement. A separate focus is the practical application of AI for requirements analysis, backlog work, planning, forecasting, team data analysis, retrospectives and Product Discovery. Modern AI tools, Jira and other teamwork tools can be used during training.
Training takes place online in the format of practical classes. The course consists of 8 classes, which combine short theoretical blocks, analysis of real cases, practical exercises and work with AI tools. The duration and schedule of training can be adapted to the format of a group or corporate order.
After completing the course, you will have a systemic understanding of Agile approaches and a practical set of tools for working with Scrum and Kanban, you will be able to organize team work more effectively, manage flow, plan and forecast delivery, and use AI in daily management processes. Participants will also receive practical templates and exercises created during training. The cost of the course depends on the training format, the number of participants and the duration of the program — current conditions are available during registration or upon request.
