In an era where AI systems are becoming increasingly autonomous, the role of testing has evolved from a traditional quality assurance process to a critical safeguard for trust, reliability, and responsible AI. This keynote explores the fundamental principles of testing in AI-driven environments, emphasizing the need for a nuanced balance between full automation and human oversight. While automation accelerates testing processes, it also introduces risks related to bias, blind spots, and lack of contextual awareness. A human-in-the-loop (HITL) approach can mitigate these risks, ensuring AI models are not only efficient but also aligned with ethical and safety standards.

Additionally, the rise of multi-agent and agentic models offers promising advancements in testing methodologies. By leveraging AI agents to test, audit, and refine other AI systems, we can enhance robustness, improve anomaly detection, and establish self-improving feedback loops. These models introduce new paradigms in AI testing, enabling scalable and adaptive validation across various industries, from healthcare to finance and autonomous systems.

However, testing AI is not just about functionality—it is also about responsibility. Responsible AI frameworks demand rigorous testing strategies to prevent unintended consequences, ensure fairness, and uphold accountability. Data integrity plays a pivotal role in this process—inaccurate, biased, or compromised data can propagate errors at scale, leading to flawed decision-making and systemic failures. Ensuring high-quality, unbiased datasets through stringent testing and validation mechanisms is as crucial as testing the AI models themselves.

As we move towards increasingly autonomous AI ecosystems, the challenge lies in designing trustworthy AI systems that balance automation with human judgment while leveraging multi-agent intelligence to improve testing efficiency. This keynote will explore these themes, offering insights into best practices for AI testing, emerging methodologies in multi-agent testing, and the critical role of responsible AI and data integrity in shaping the future of AI assurance.

In this talk, we will explore the latest innovations shaping the future of Test Automation. I will be sharing my experience on leading Test Automation Centre of Excellence at Telstra, on how we are leveraging Gen AI, AI/ML , Innovative Automation frameworks , Service Virtualisation, Contract Testing to revolutionise Quality Engineering at an enterprise level. Key topics include AI-driven test case generation, self-healing tests, test data automation, virtualized environments, contract testing and automated quality gates. Attendees will gain insights into how cutting-edge tools and techniques can accelerate release cycles, reduce defects, and enhance overall testing productivity.

Whether you are a software engineer, quality engineer, or a tech leader, this session will equip you with actionable strategies to stay ahead in the rapidly evolving landscape of test automation

This session will discuss automation, test data management, test scripting, exploratory testing enhance testing with Large Language Model (LLM) Identify opportunities to improve test quality with AI Construct test automation with AI tools Ideas during exploratory testing using AI tools Leverage AI tools to aid the design process of new features Improve testability with AI tools Maximize output with prompt engineering

Leverage AI and LLMs and GenAI to improve your software and data testing efficiency Learn new tools and tricks for Testing

Digital transformation is reshaping Quality Assurance (QA), revolutionizing how businesses handle quality, streamline operations, and deliver exceptional products and services. By embracing advanced technologies, companies are rethinking their testing, validation, and assurance processes. Here’s how digital transformation is impacting QA:

1. Automation of Testing– This is one of my favourite aspects of digital transformation. While creating automation scripts and suites requires significant effort upfront, once they’re in place, they become a powerful tool. The key is ensuring that the automation is done correctly—this means minimizing maintenance while maximizing efficiency and value.

– Automated Test Scripts and the integration of Continuous Integration and Continuous Testing (CI/CD) are crucial to this process.

– Robotic Process Automation (RPA):Beyond software, RPA also automates repetitive tasks and processes in non-software areas, boosting efficiency. For example, in my recent experience, we were using a Java Selenium-based framework called Gebspock, which was working well. However, with the rapid changes in business dynamics, especially with GenZ squads joining our team (who often say “TGIF” on Friday mornings—thankfully, I had to Google what it meant!), we decided to explore UiPath. With this RPA tool, we were able to achieve faster test automation.

What Does Digital Transformation in QA Mean?
It means using smart technology like Artificial Intelligence (AI), automation, and cloud computing to test products faster and more accurately. Instead of relying on people to manually check for issues, automated tools can do the job much quicker.

Why Is This Important?
Faster Testing: Automated systems can run thousands of tests in minutes.
Fewer Mistakes: Machines reduce human errors, making tests more reliable.
Saves Money: Companies can test more efficiently while cutting costs.
Works Everywhere: Digital tools allow testing across different devices and platforms at the same time.
Trends in Digital QA
AI and Machine Learning: Predict problems before they happen.
Automation: Big companies like Microsoft and Amazon use automated tools like Selenium to speed up testing.
Cloud-Based Testing: Netflix runs tests online, so there’s no need for expensive hardware.
Continuous Testing & Updates: Facebook constantly tests and improves its apps without major delays.
Real-Life Example: Tesla
Tesla updates its car software over the air, just like a phone update. This means they can fix issues and add new features without customers having to visit a service centre.

Challenges of Going Digital
Expensive to Start: AI and automation tools require investment.
Learning Curve: QA teams need to learn new technologies.
Data Security Risks: Cloud-based testing means companies must protect sensitive data.
Final Thoughts
Switching to digital QA makes testing faster, more accurate, and more efficient. While there are challenges, the benefits—better quality, lower costs, and happier customers—make it worth it. Companies that embrace these changes will stay ahead in today’s tech-driven world.

So, yes, embracing digital transformation in QA involves some risks, but the potential rewards—such as improved efficiency and faster testing—make it a path worth exploring.

Take the risk, be the change. The question is, how? The answer lies in embracing Digital Transformation in Quality Assurance (QA) By exploring new tools and technologies, we can align ourselves with the evolving landscape of businesses, seamlessly managing and enhancing software quality in the process.

Mark is a seasoned testing leader with 25+ years’ experience across Australia and the UK. He has built and led large-scale testing practices across Infosys, Capgemini, HCL, and EDS, driving strategy, delivery, and capability development across ANZ and APAC for major clients in retail, energy, banking, and telecom.

Experienced transformation executive with 20+ years in IT and business transformation. Passionate about delivering business value, uplifting delivery quality, and recovering at-risk programs. Expertise spans banking, insurance, healthcare, and logistics, with a focus on measurable outcomes and inspiring high-performing teams. Strengths: Ideation, Adaptability, Positivity.

• Experienced Leader in Quality Assurance and Engineering, successfully leading big QA teams, shaping testing strategies, and collaborating closely with cross-functional stakeholders.
• Proven ability in delivering to customer priorities, engaging, guiding, and influencing senior stakeholders / Leaders on strategic initiatives.
• By combining process optimization with innovative testing methodologies, I strive to make quality engineering a driver of productivity and customer satisfaction. I firmly believe that technology can enhance efficiency and create opportunities that can lead to a more inclusive workplace.
• I’m driven by the belief that exceptional quality isn’t a destination—it’s a journey that requires dedication, collaboration, and a keen eye for detail.

Darren is an experienced Quality Director with over 20 years in software development and quality assurance. He specializes in integrating quality within development lifecycles, leading large-scale assessments, and driving transformation. A certified testing professional, he excels in building high-performing teams and implementing strategic delivery centers.