As AI becomes embedded across every layer of Quality Engineering, the QA function is undergoing a major shift — from task execution to strategic leadership. This keynote explores the emergence of new roles such as Test strategists and insight analysts, and how QA leaders and consultants can guide this transformation. With a focus on intelligence-driven planning, automation alignment, and predictive reporting, the session offers a practical roadmap to reposition QA as a business partner that drives value, foresight, and delivery confidence.

Grounded in enterprise-scale transformation programs, the talk includes a live demo of a real AI QA agent in action — showcasing how AI is already enabling smarter test strategy, automation decisions, and quality insights in daily delivery. Attendees will walk away with a clear blueprint for leading this evolution within their own teams and organizations.

• We’ve reached the point where not applying AI in QA is no longer just inefficient — it puts delivery speed, competitive advantage, and team progress at risk.

• AI elevates QA strategy. It enables smarter planning, risk-based prioritization, and faster, more aligned decisions across teams.

• Insight > Output — predictive metrics and AI-enhanced dashboards shift QA from reporting function to strategic decision partner.

• Automation needs AI — to improve test selection, increase stability, and deliver long-term ROI. Intelligence makes scaling possible.

• Adoption is measurable and real — with proven success across large programs and high levels of weekly usage among QA teams.

• QA role is evolving — from tester to insight architect, analytics partner, and AI-enabled strategist.

AI is no longer just assisting QA — it’s actively thinking, generating, analyzing, and evolving like a full-fledged team member. This keynote explores why it’s time to stop resisting and start embracing AI as a core part of the quality function.

While the technology is still maturing — with limitations in reasoning, context, and edge-case awareness — it’s already outperforming traditional approaches in speed, scale, and adaptability. Testing isn’t disappearing; it’s transforming. As scripting gives way to autonomous agents, the role of the human tester must elevate — toward business strategy, ethics, and contextual understanding.

AI won’t replace us — but testers who use AI will replace those who don’t.

• Stop Resisting: Accepting and adapting is more productive than debating.
• Know the Limits: Machines still lack emotional, ethical, and contextual intelligence.
• Elevate Your Role: Human testers must move toward strategy, not scripts.
• Act Now: Early adopters will shape the future — the rest will catch up late.
• Mindset Shift: Start viewing AI as a collaborator, not a utility.

As software complexity increases and release cycles shrink, traditional testing and test automation are hitting its scalability and adaptability limits. In this talk, we explore how AI-powered agents are empowering and evolving Quality Assurance—from automating test plan and test case generation, self-checking requirements and acceptance criteria, and self-healing scripts to intelligent test prioritization.

Drawing from real-world experience and hands-on experimentation, I’ll showcase how teams can begin integrating AI agents & tools into their QA workflows today—without overhauling existing infrastructure or replacing the essential role of QA professionals.

Expect live demonstrations, practical strategies, and an honest look at how QA can evolve from task execution to strategic quality leadership with the help of AI.

• Understand the difference between test automation and autonomous testing using AI agents.
• Introduction of how LLMs (like ChatGPT or Gemini) can assist in generating test plan and test cases.
• Explore AI-driven approaches in test failure analysis, requirement and AC validation, and test prioritization.
• Gain insight into the risks, limitations, and best practices when applying AI in testing environments.

As software systems grow in complexity and scale, the need for efficient, reliable testing becomes ever more critical. This paper explores a novel approach to test planning and automation that harnesses the capabilities of large language models (LLMs) to support and empower quality assurance teams. We introduce a framework in which LLMs collaborate with human testers to generate detailed test plans, suggest automation scripts, and assist in executing test cases. Rather than replacing human expertise, our system leverages AI to handle repetitive and time-consuming tasks, allowing testers to focus on creative problem-solving and critical decision-making. We detail the design and integration of this AI-augmented workflow, present case studies demonstrating improvements in speed and coverage, and discuss best practices for effective human-AI collaboration in software testing. Our results indicate that combining the strengths of LLMs with human insight leads to smarter, more adaptive, and efficient testing processes, paving the way for a new era of collaborative software quality assurance.

• Expect the audience to walk way with the understanding of:

• LLMs can significantly boost QA productivity by automating repetitive test planning and script generation tasks.

• Human expertise remains crucial—AI works best as a partner, supporting testers rather than replacing them.

• Test planning and automation become faster and more comprehensive with AI assistance, leading to better coverage and fewer missed scenarios.

• AI-powered tools make automation more accessible for teams with varying coding experience.

• The future of testing is collaborative: Combining AI efficiency with human insight leads to higher-quality, more reliable software.

In the rush to ship fast and automate everything, many organizations still treat quality assurance and cybersecurity as two separate disciplines. QA teams focus on functionality and performance, while security teams focus on vulnerabilities and threats — often without shared tools, language, or workflows. The result? Gaps that attackers can exploit and missed opportunities for catching security flaws early.

In this talk, we’ll explore the reasons why QA and cybersecurity teams remain siloed, even in mature DevOps organizations. Drawing from real-world experience in test automation and insights from security best practices, we’ll identify practical strategies to integrate security thinking into automated QA workflows. Whether you’re a QA engineer, SDET, or security practitioner, this session will help you drive real collaboration across disciplines and build more secure, reliable systems.

Understand the Disconnect

• Learn where and why QA and security teams often misalign, from culture and priorities to tooling and workflows.

Spot the Risks

• See how common security issues (like BOLA and broken access control) can slip past automated tests when security isn’t integrated.

Unify Your Pipelines

• Discover how to incorporate lightweight security checks (e.g., SAST, DAST, API fuzzing) into your CI/CD alongside functional tests.

Build Shared Ownership

• Walk away with actionable steps to foster cross-team collaboration, from shared acceptance criteria to DevSecOps feedback loops.

Start Small, Think Big

• Learn how to embed security awareness in everyday QA work — even if you’re not a security expert.

In this talk, I’ll share my personal journey of adopting AI tools in my day-to-day QA work — what worked, what didn’t, and how it transformed my testing process.

This talk is for anyone curious about leveraging AI in real QA workflows — whether you’re just getting started or looking to level up.

You’ll walk away with practical insights, hard truths, and a clear-eyed view of whether AI is a friend, a foe, or maybe… both.

In addition – tools, plugins, approaches you can use at your project

AI in QA has moved far beyond chatbots and code autocompletion — but knowing how to apply it effectively is the real challenge. In this talk, we’ll cut through the hype and explore five proven AI applications that are delivering measurable results in QA automation today. Drawing from real-world SDET experience, I’ll walk you through how AI can accelerate test creation, optimize data, enhance defect detection, and seamlessly integrate into existing frameworks. You’ll leave with practical strategies, tool recommendations, and an understanding of how to future-proof your QA practice with AI.

• Move from Theory to Practice – Understand how to apply AI in real QA projects rather than just exploring concepts.
• Boost Test Coverage & Speed – Learn how AI can accelerate test case design, execution, and maintenance.
• Enhance Defect Detection – Discover AI-driven techniques for identifying defects earlier and reducing production leaks.
• Optimize Test Data Management – See how AI can generate, cleanse, and manage data for more effective testing.
• Integrate AI into Your Toolchain – Practical tips for embedding AI into your current automation framework with minimal disruption.
• Avoid Common AI Pitfalls – Learn from real-world lessons to ensure your AI adoption succeeds.
• Future-Proof Your QA Skills – Insights into emerging AI trends that will shape the SDET role in the next 3–5 years.

Mike Hrycyk is passionate about quality. Quality in software, quality in requirements and quality in management. The biggest strength Mike brings to any organization is his ability to form the big picture of the entire enterprise in his head. This allows Mike to use this knowledge in figuring out the true requirments of the stakeholder, to better know the factors that will then be required in producing a properly integrated functional design or to know which systems will be impacted so that an appropriate test strategy and plan can be formed. Mike likes to build relationships within an organization to allow communication to become easy, productive and efficient.

Snehal is a seasoned Quality Engineering leader with over 20 years of experience driving innovation and excellence across industries such as E-Commerce, Digital Media, Energy, Healthcare, Finance, and Oil & Gas. Currently serving as Director of Quality Engineering at Slalom, she has hands-on experience leveraging multiple cloud platforms, app modernization, AI, and GenAI to optimize quality engineering processes and drive agile transformations.

An MIT Sloan alumnus with certifications in AWS, Azure,Databricks and ISTQB, Snehal has a proven ability to build high-performing teams and deliver impactful results. She is passionate about empowering teams through trust, transparency, and continuous learning, ensuring organizations achieve lasting business value and operational efficiency. Snehal’s innovative approach helps organizations optimize quality strategies, driving faster delivery of high-quality solutions.

Arash Taheri is a seasoned technology leader with over 18 years of experience spanning software quality assurance, QA automation testing, SaaS, business analysis, development, and project management. Throughout his career, he has successfully led global teams of various sizes, delivering high-quality products and services in fast-paced, complex environments.

Known for his strong, charismatic leadership, Arash builds and inspires high-performing teams, fostering collaboration among cross-functional stakeholders to remove obstacles and maximize resources. As an experienced technology leader, he has driven Agile transformations and championed best practices that improve efficiency, scalability, and product quality.

With a deep commitment to customer-centric innovation, Arash partners closely with product management to design, test, and deliver exceptional user experiences. His guiding philosophy is to “start with the customer and work backwards,” ensuring that every solution meets the highest standards of flexibility, scalability, and quality.