AI for QA Leaders and Managers

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:

1. Identify QA areas with the highest potential return on investment (ROI) from AI adoption.
2. Assess team readiness for AI implementation and align the strategy with actual capabilities.
3. Design an AI implementation roadmap for a QA team, including phases, milestones, and quick wins.
4. Build a structured repository of best practices and prompts for the team.
5. Define success metrics, including what to measure, how to measure it, and when to evaluate results.
6. Manage organizational change, including resistance, enthusiasm, expectations, and misunderstandings.
7. Communicate the value of AI to different stakeholders, including team members, management, and clients.
8. 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