As software delivery cycles shrink and release expectations rise, teams are under increasing pressure to automate more, without slowing down development or relying on specialized coding skills. No-code test automation platforms promise to bridge this gap by making automation accessible to every tester, regardless of programming background. We will explore how modern tools empower manual testers to become automation contributors, and discuss where no-code fits and doesn’t fit in today’s testing strategy.

1. A clear understanding of what no-code automation really is
2. Where no-code automation fits in a modern QA strategy
3. How no-code enables non-technical testers to contribute
4. Common pitfalls and how to avoid them
5. Real-world examples of no-code automation in action
6. Steps to successfully adopt no-code tools in your organization

AI pilots often look promising—but they rarely scale. Teams get stuck reviewing AI-generated requirements, test cases, and automation scripts, spending more time managing the pilot than improving quality. In this talk, we’ll explore why “AI as a side experiment” fails in real delivery environments and how to architect AI as a first-class citizen in your testing strategy.

We’ll walk through a practical approach to embedding AI directly into the quality process—where it continuously observes work, manages AI-generated artifacts, and closes gaps across requirements, test design, and automation. Instead of asking teams to become prompt engineers or adopt new tools, the AI integrates seamlessly with existing workflows in Jira or Azure DevOps, augmenting testers and developers without disruption. Think of it as an eagle’s-eye view of your project: always watching, and proactively improving quality at scale.

1. Why AI pilots fail to deliver long-term value in testing and quality engineering
2. How to move from manual AI review cycles to continuous, embedded AI assistance
3. Designing AI to manage and evolve requirements, test cases, and automation artifacts end-to-end
4. Making the need for prompt engineering and specialized AI training optional for teams that don’t have the time and/or budget
5. What a “seamless AI teammate” looks like when integrated into Jira or Azure DevOps
6. How an observability-driven AI can detect gaps, reduce rework, and raise quality without slowing delivery

Test automation has long been measured by how much we automate — number of tests, coverage percentages, pipelines running green.
But in the age of AI, that mindset is breaking down.
With AI copilots capable of generating tests in seconds, automation is no longer limited by who can write code. It is limited by who can make the right decisions.
This talk explores how AI is fundamentally democratizing test automation — shifting the most valuable skill from writing scripts to deciding what deserves automation, where it should live, and why it matters.
We’ll challenge common automation myths such as:

More tests equals better quality
UI automation is the ultimate goal
Coverage numbers equal confidence

Instead, we’ll reframe automation as a decision system focused on risk reduction, long‑term value, and meaningful confidence.
Using real-world examples, we’ll show how impressive-looking automation — especially complex, frequently changing UI flows — can silently consume hundreds of hours in maintenance, while cheaper, faster, and more reliable validation at the API or contract layer delivers greater value.
AI doesn’t make automation smarter by writing more tests.
It makes automation smarter by exposing bad automation decisions faster.
This session is about building impactful automation strategies in an AI‑driven world — where success is measured by confidence, not coverage.

1. A new mental model for automation in the age of AI
2. Practical questions to decide whether a test should exist at all
3. Guidance on choosing the most valuable testing layer
4. A clearer understanding of automation cost vs impact
5. A shift from “how many tests do we have?” to “what risks are we covering?”
6. Strategies to use AI as a decision copilot, not a test factory

Mei is currently the General Manager of Innovation and Technology at TTC Global. She started her career as an automation engineer – and with a passion for all things testing and quality, she has gradually transitioned into managing test programmes ranging in different sizes, building testing capabilities and centres of excellence across multiple projects and domains. Her current role merges strategic leadership with a hands-on approach to foster testing solutions across various sectors, which has involved contributing to business development and supporting various organisations’ test teams through her technical and consultancy expertise.

Mei has been successful in leading and implementing value-driven changes in client organisations, and she has extensive experience in directing the testing of numerous Digital Transformation Programmes of varying sizes – specifically deploying SaaS products such as Infor M3 & SAP technologies (ANZCO and Sealord), D365 (Farmlands) and Salesforce, Workday, & Infor LN/EAM/IPS (DIA – 3Waters).

Mei has built testing capability with 50+ testers, across multiple projects under different testing phases and SDLC methodologies –augmenting test teams using near and offshore consultants.

She collaborates at C-Suite level with ease, conveying messages to various stakeholders and advising on how to uplift quality throughout organisations taking into account Cloud and SaaS architecture.

More recently, she has become one of the key members of TTC Global’s AI Leadership Team – and is very passionate in sharing her insights to the testing community.

Her industry experience includes Food and Beverage, Banking, Finance, Digital, Media, Utilities, Retail, and Insurance.