Why take this course
QA leaders face a paradox today: their teams are already using AI (often informally), while the organization lacks a clear strategy, standards, and success metrics. As a result, some employees experiment enthusiastically, others ignore the topic, and management keeps asking, “What results are we getting?”—with no clear answer. [sii.pl]
This course equips QA leaders and managers with practical tools to take control of AI adoption by helping them answer key questions:
- Where does AI create the greatest value in QA, and how can it be measured?
- Which areas should be standardized first, and which can wait?
- How can you build a sustainable library of best practices and prompts?
- How can AI be introduced into a team without chaos, resistance, or unnecessary risk?
- How do you communicate AI value effectively to management, clients, and team members, each of whom has different expectations?
This course does not simply advocate using AI—it focuses on implementing AI in a way that delivers measurable business outcomes within a few months.
What you’ll learn
After completing this course, participants will be able to:
- Identify QA areas with the highest potential return on investment (ROI) from AI adoption.
- Assess team readiness for AI implementation and align the strategy with actual capabilities.
- Design an AI implementation roadmap for a QA team, including phases, milestones, and quick wins.
- Build a structured repository of best practices and prompts for the team.
- Define success metrics, including what to measure, how to measure it, and when to evaluate results.
- Manage organizational change, including resistance, enthusiasm, expectations, and misunderstandings.
- Communicate the value of AI to different stakeholders, including team members, management, and clients.
- Establish AI governance, including policies, roles, responsibilities, and control mechanisms.
Certification & Exam
Participants receive a personalized certificate of course completion.
The course does not include a final exam.
Optionally, for closed-group sessions, the trainer may conduct a peer-review session of implementation roadmaps as a course wrap-up.
Who is this course for
This course is intended for professionals responsible for QA team performance and decision-making regarding testing practices, including:
- QA Leads managing testing teams and defining quality standards
- Test Managers overseeing testing processes and QA budgets
- Heads of QA / QA Directors responsible for quality strategy across the organization
- Delivery Managers with QA accountability who make decisions about tools and processes
- Test Architects responsible for defining testing tools and approaches from a technical perspective
No technical skills are required.
Participants should have experience leading teams or being responsible for QA processes.
Topics covered
Day 1: Where We Are and Where We’re Going
Objective: Assess the current state of the team, identify the highest-ROI AI opportunities, understand organizational maturity, and design an implementation strategy.
- AI in QA – A Leader’s Perspective
AI application landscape in QA: from simple use cases (e.g., test case generation) to advanced agent-based workflows
What works reliably today and what remains future potential
Setting realistic expectations regarding AI capabilities and limitations
Leadership perspective: not “how to use AI,” but “what to implement, in what order, and with what expected impact”
Workshop: mapping team testing processes and identifying AI opportunities
- ROI Identification and Prioritization
Value vs. implementation effort prioritization framework
Quick wins that can be delivered within a week
Strategic investments with long-term value
Identifying initiatives to avoid
Real-life examples from different organizations
Workshop: creating a personalized prioritization matrix
- Assessing Team Maturity
Five-level AI maturity model for QA
Maturity factors: skills, tools, processes, culture, and governance
Aligning strategy with maturity level
Workshop: AI maturity self-assessment and improvement planning
- Standardization and Best-Practice Library
Why standardization is more important than individual expertise
What to standardize: prompts, output formats, validation practices, workflows
Designing a team prompt library
Distinguishing mandatory practices from recommendations
Workshop: designing a prompt library structure and governance model
Daily Assignment
Review your prioritization matrix and identify your top quick win.
Day 2: Implementation, Measurement, and Change Management
Objective: Create an implementation roadmap, define success metrics, manage organizational change, and communicate AI value effectively.
- Metrics and Measuring Results
What to measure: time, quality, coverage, team satisfaction, adoption rate
Establishing baselines and measuring post-implementation results
Common measurement pitfalls
Reporting outcomes to management using business-focused language
Workshop: defining 3–5 success metrics for an implementation plan
- Managing Change Within the Team
Understanding different employee attitudes toward AI
Tailored engagement strategies for each group
The role of the AI Champion
Common implementation mistakes
Choosing between formal training, mentoring, and learning by doing
Case study workshop: designing a change management plan
- Governance and Risk Mitigation
AI governance in QA: policies, roles, and responsibilities
Minimum governance requirements before implementation
Handling incorrect outputs, data leakage, and overreliance on AI
Stakeholder communication strategies
Workshop: creating a minimum governance framework consisting of five core rules
- Final Workshop: AI Implementation Roadmap
Building an AI roadmap for participants’ own teams
Components:30/60/90-day implementation phases, Quick wins, Standards, Success metrics, Governance, Team enablement and training
Strategic canvas exercise
Peer review and roadmap presentations
Trainer feedback and recommendations
- Final Reflection
Defining the first actions to take after returning to work
Supporting resources: templates, canvases, checklists, and example libraries
Recommended prerequisites
- At least 2 years of experience as a QA Lead, Test Manager, or a similar decision-making role
- Good understanding of testing processes and QA team organization
- General awareness of AI tools (previous hands-on experience is welcome but not required)
- No programming skills required
Equipment requirements
- Laptop with internet access and a web browser
- Account in an AI tool/platform
The trainer provides workshop materials, including roadmap templates, strategic canvases, checklists, and sample prompt libraries.
Participants may optionally bring information about their teams (size, structure, tools, processes) to support workshop activities.
