Our Trainings

Target Audience

Large enterprises and scaling technology companies; leadership teams that want to assess their delivery organization through an AI lens.

Training Objective

Run a two-week assessment of project and product delivery processes, surface AI opportunities and delivery risks, and hand leadership an actionable roadmap.

Outcomes

• A current-state snapshot of delivery processes
• A prioritized list of AI opportunities
• An assessed delivery risk map
• An actionable roadmap for leadership

Content

• Review of project and product delivery processes
• Identification of AI usage opportunities
• Assessment of delivery risks
• Preparation of a leadership roadmap

Target Audience

Senior executives, PMO leaders, program and project managers.

Training Objective

Give the leadership team a practical framework and a shared language for where and how to use AI in project management.

Outcomes

• Identifying AI use cases in project management
• Evaluating AI approaches to risk and delay prediction
• Spotting automation opportunities in management reporting
• Framing human–AI task allocation and governance principles
• Evaluating use cases for Cursor, GitHub Copilot, and Jira AI

Content

• AI in project management: current state and opportunities
• Risk and delay prediction
• Automating management reporting
• Human–AI task allocation
• AI governance
• Theoretical and hands-on tool practice: Cursor, GitHub Copilot, and Jira AI

Target Audience

Startup founders, CTOs, product leaders, and teams preparing for an investment round.

Training Objective

Rapidly mature the startup's product development and delivery operation, making it operationally ready to scale and to enter the pre-investment process.

Outcomes

• A clarified roadmap and prioritization approach
• A working OKR/KPI structure
• Streamlined product development processes
• Investor-ready operational maturity

Content

• Roadmap and prioritization
• Setting up the OKR/KPI structure
• Designing product development processes
• Jira configuration and delivery governance
• Pre-investment operational readiness

Target Audience

Software teams building AI products, data and ML professionals, product managers, and AI project leads.

Training Objective

Build the ability to design reliable, traceable, and evaluable AI solutions by combining large language models with Retrieval-Augmented Generation architectures.

Outcomes

• Framing LLM use cases and RAG requirements correctly
• Designing ingestion, chunking, embedding, and vector search flows
• Measuring answer quality, hallucination, and citation behavior
• Designing security, privacy, and human oversight controls

Content

• Transformer and LLM fundamentals
• RAG architecture: ingestion, chunking, embeddings, and retrieval
• Hybrid search, reranking, and context-window design
• Prompt templates, tool use, and structured outputs
• RAG evaluation: correctness, faithfulness, relevance, and latency
• Hallucination reduction, citations, and security
• Productionization, observability, and cost optimization

Target Audience

Aspiring specialists, career changers, and professionals new to agile ways of working.

Training Objective

Build a solid grasp of the agile mindset and core frameworks so participants can play an effective role in an agile team.

Outcomes

• Command of agile terminology
• Effective participation in Scrum/Kanban teams
• Applying backlog and user story concepts

Content

• Agile Manifesto and 12 Principles
• Waterfall vs. Agile
• Scrum framework: roles, events, artifacts
• Kanban basics and flow management
• User stories and the backlog
• Agile team dynamics

Target Audience

Professionals with 1–3 years of hands-on experience; Scrum Masters, product owners, and team members.

Training Objective

Deepen team-level agile practices through real cases and workshops, and build a habit of data-driven improvement.

Outcomes

• Independently running team-level agile practices
• Data-driven improvement using flow metrics
• Effective sprint planning and refinement facilitation

Content

• Sprint planning and estimation techniques
• Backlog refinement and prioritization (WSJF, MoSCoW)
• Flow metrics: lead time, cycle time, throughput
• Retrospective techniques and continuous improvement
• Value stream mapping workshop
• Real-case simulations

Target Audience

Agile coaches, transformation consultants, managers, and transformation leaders with 3+ years of experience.

Training Objective

Equip participants to design, scale, and sustainably manage agile transformation across the organization.

Outcomes

• Designing organization-wide agile transformation
• Comparing scaling frameworks and choosing the right model
• Building transformation roadmaps and governance

Content

• Agile maturity and organizational assessment methods
• Agile operating model design
• Product and portfolio management
• Scaling framework comparison (SAFe, LeSS, Spotify)
• Building the transformation roadmap
• Sustainable operating model and governance

Target Audience

All professionals new to AI; business units, managers, and aspiring specialists.

Training Objective

Teach AI concepts through a business-value lens and create a shared language for identifying AI opportunities.

Outcomes

• Identifying AI opportunities
• Building a shared language between business and technical teams
• Recognizing AI project risks early

Content

• AI, machine learning, and generative AI concepts
• AI use case and opportunity assessment
• The role of data quality in AI success
• Industry AI application examples
• Common causes of AI project failure

Target Audience

Project managers, business analysts, product owners, and technology leaders.

Training Objective

Build the competence to manage AI initiatives from idea to pilot and scaling with a business-value focus.

Outcomes

• Managing an AI initiative end to end
• Planning PoC and pilot processes
• Tracking cost-benefit and ROI
• Evaluating use cases for Cursor, GitHub Copilot, and Jira AI

Content

• AI project lifecycle and differences from classic projects
• Proof of concept (PoC) and pilot planning
• Roadmap development and prioritization
• Human–AI workflow design
• Cost, benefit, and ROI tracking
• Organizational adoption and change management
• Theoretical and hands-on tool practice: Cursor, GitHub Copilot, and Jira AI

Target Audience

Software developers, testers, and technical teams new to AI tooling.

Training Objective

Establish the foundation for using AI code assistants consciously, productively, and safely in daily development.

Outcomes

• Using AI tools effectively in the daily development flow
• Writing effective prompts
• Assessing the limits and risks of AI-generated code

Content

• Overview of SDLC stages
• The AI code assistant ecosystem
• Prompt engineering fundamentals
• Opportunities and limits of AI-assisted coding
• Safe usage principles

Target Audience

Software developers, test engineers, DevOps engineers, and solution architects.

Training Objective

Integrate AI into every stage of the software lifecycle to measurably increase delivery speed and quality.

Outcomes

• Integrating AI across all SDLC stages
• Setting up test automation and AI-assisted code review
• Measuring delivery with quality metrics
• Evaluating use cases for Cursor, GitHub Copilot, and Jira AI

Content

• SDLC maturity assessment
• AI-enabled requirements and backlog management
• User story and acceptance criteria generation
• AI-assisted development and code review
• Test scenario generation and test automation
• DevSecOps and CI/CD integration
• Release management and quality metrics
• Theoretical and hands-on tool practice: Cursor, GitHub Copilot, and Jira AI

Target Audience

All professionals new to goal management; team leads and aspiring specialists.

Training Objective

Teach outcome-oriented thinking over output and how to write effective OKRs.

Outcomes

• Writing effective OKRs
• Thinking outcome-first
• Aligning team and organizational goals

Content

• The OKR concept and how it differs from KPIs
• Objective–key result writing workshop
• Value-based measurement approach
• Aligning team and organizational goals

Target Audience

Leaders, managers, team leads, and agile coaches.

Training Objective

Develop the coaching stance and agile leadership mindset that grows teams' self-management capacity.

Outcomes

• Growing teams' self-management capacity
• Adopting a coaching stance
• Building psychological safety and a feedback culture

Content

• Agile leadership mindset and servant leadership
• Coaching and mentoring techniques
• Team maturity models
• Feedback culture and psychological safety
• Conflict management
• Team motivation in the era of human–AI collaboration

Target Audience

Transformation leaders, senior executives, agile coaches, and transformation consultants.

Training Objective

Build the competence to design and lead an enterprise transformation program end to end.

Outcomes

• Designing a transformation program end to end
• Applying change management models
• Managing executive sponsorship and stakeholders

Content

• Transformation strategy and vision communication
• Continuous improvement and transformation roadmaps
• Change management models (Kotter, ADKAR)
• Transformation metrics and governance
• Executive sponsorship and stakeholder management

Target Audience

Business analysts and aspiring BAs, product owners, process specialists, and professionals involved in requirements management.

Training Objective

Build end-to-end business analysis competence in elicitation, analysis, modeling, and validation based on the BABOK v3 framework.

Outcomes

• Effectively applying elicitation techniques
• Running stakeholder analysis and communication plans
• Performing process and data modeling
• Writing user stories and acceptance criteria

Content

• Business analysis fundamentals and BABOK knowledge areas
• Stakeholder analysis and communication planning
• Elicitation techniques: interviews, workshops, observation, document analysis
• Process modeling (BPMN) and data modeling
• Requirement types and prioritization (MoSCoW)
• User stories and acceptance criteria
• Solution evaluation and AI-assisted analysis tools

Target Audience

Test specialists, QA engineers, developers, and business analysts involved in quality processes.

Training Objective

Build the competence to systematically uncover hidden defects and findings missed by scripted tests, using exploratory testing and experience-based techniques.

Outcomes

• Designing and running charter-based exploratory testing sessions
• Applying boundary value analysis and error guessing strategies
• Performing root cause analysis on findings
• Leveraging AI-assisted test case generation

Content

• Experience-based test techniques (ISTQB approach)
• Exploratory testing: charters, timeboxes, and session management
• Boundary value analysis and equivalence partitioning
• Error guessing and defect clustering
• Persona- and scenario-based test design
• Pair and mob testing practices
• Findings reporting and root cause analysis
• AI-assisted test design and automation opportunities

Target Audience

Aspiring product managers and product owners, project managers, entrepreneurs, and professionals working with product teams.

Training Objective

Build modern product management practice from discovery to delivery, covering vision, strategy, roadmapping, prioritization, and metrics.

Outcomes

• Crafting product vision and strategy
• Running product discovery processes
• Building roadmaps and prioritizing effectively
• Defining product metrics and OKRs

Content

• Product management fundamentals and the PM mindset
• User research, personas, and customer journey mapping
• Product discovery: opportunity solution trees and dual-track agile
• The vision–strategy–roadmap line
• Prioritization frameworks (RICE, MoSCoW, Kano)
• MVP design and hypothesis validation
• Product metrics and OKRs
• Stakeholder management and AI-powered product work

Target Audience

Transformation leaders, managers, team leads, agile coaches, and AI transformation program owners.

Training Objective

Develop leadership practices that create direction amid uncertainty, resistance, and technology-driven role changes while strengthening psychological safety, learning, and shared ownership.

Outcomes

• Distinguishing technical problems from adaptive challenges
• Communicating a transformation vision in meaningful, behavioral terms
• Strengthening psychological safety and feedback conditions
• Aligning change stakeholders and progressing through experiments

Content

• Adaptive leadership and complex change
• Transformation leadership: vision, meaning, and coalition building
• Psychological safety, learning teams, and conversational quality
• Coaching, feedback, and difficult conversations
• Ethical decisions and human–AI collaboration in AI transformation
• Stakeholder mapping, working with resistance, and change experiments
• A 30-60-90 day practice plan for leadership behaviors