The software quality landscape is undergoing a seismic shift. From manual test cases to continuous testing pipelines, QA has constantly evolved. But now, with the rise of Generative AI and Agentic AI, we stand on the brink of a transformation unlike any before—a shift that redefines not just how we test, but who we become as quality professionals.

In this keynote, Harish Ramakrishna takes you on a journey through the evolution of QA, revealing how AI is not just disrupting tools and techniques but reshaping roles, mindsets, and the very essence of quality engineering. With real-world examples and forward-looking insights, we’ll explore how GenAI is generating test assets, predicting failures, and designing smarter test strategies, while Agentic AI autonomously executes, learns, and improves test ecosystems in real time.

This is not a story of replacement. It’s a call to reimagine QA careers—where testers become quality strategists, prompt engineers, and AI collaborators. Join us to gain clarity, courage, and a practical roadmap to thrive in the AI-augmented world of software testing.

• Understand the impact of Generative and Agentic AI on the QA ecosystem.
• Discover emerging QA roles and skillsets for the AI era.
• Learn how to future-proof your QA career with practical steps and mindset shifts.
• Are you ready to not just survive, but lead in this AI-driven transformation?

As an AI expert specializing in digital transformation and test automation, I bring a strategic lens to how AI accelerates enterprise agility, scalability, and innovation. My session explores how intelligent automation is reshaping software testing, reducing cycle times, and enhancing quality at scale. We’ll delve into real-world case studies showcasing the power of AI in predictive analytics, autonomous testing, and continuous delivery pipelines. Emphasizing ROI and business impact, I will highlight frameworks that blend AI with DevOps to drive resilient and adaptive transformation. Attendees will gain insights into leveraging AI responsibly for scalable, secure, and future-ready test architectures. Whether you’re leading QA, product, or transformation efforts, this session equips you with actionable intelligence to thrive in an AI-augmented testing ecosystem.

“I’m excited by the enthusiasm around responsible AI integration and look forward to continued innovation and collaboration in this space.”As an AI expert specializing in digital transformation and test automation, I bring a strategic lens to how AI accelerates enterprise agility, scalability, and innovation. My session explores how intelligent automation is reshaping software testing, reducing cycle times, and enhancing quality at scale. We’ll delve into real-world case studies showcasing the power of AI in predictive analytics, autonomous testing, and continuous delivery pipelines. Emphasizing ROI and business impact, I will highlight frameworks that blend AI with DevOps to drive resilient and adaptive transformation. Attendees will gain insights into leveraging AI responsibly for scalable, secure, and future-ready test architectures. Whether you’re leading QA, product, or transformation efforts, this session equips you with actionable intelligence to thrive in an AI-augmented testing ecosystem.

Different tools used in creating a Playwright framework covering Automation, Stubbing, Security scan, Accessibility and few others

People can understand various types of NFT and FT testing covered in a single framework with regards to Playwright and its usage.

AI based Cloud-Native testing strategies are critical to ensure the reliability and performance of applications built on microservices architecture. A comprehensive approach includes unit testing, integration testing, contract testing, and end-to-end testing. Unit testing focuses on individual components, ensuring they function as expected. Integration testing validates the interaction between different services. Contract testing, facilitated by tools like Pact, ensures that services adhere to the agreed-upon API contracts. End-to-end testing verifies the complete workflow of the application. Open source tools such as JUnit, TestNG for unit testing, WireMock for mocking APIs, and Selenium for end-to-end testing play a crucial role in these strategies. Additionally, tools like Kubernetes for container orchestration and Istio for service mesh can help in creating realistic test environments, making the testing process more robust and reliable

• AI based test code generation
• Comparison of Diffblue and GitHub Co-Pilot
• Understanding the criticality various testing framework for Test Automation

This topic focuses on the integration of micro services with messaging systems, specifically using AWS Simple Email Service (SES) for simulating email-based workflows and testing them asynchronously.

• Simulating AWS services for performance tests
• Testing the components with no end points
• Mock test data and simulate the production behaviour
• Scale the systems for better maintenance

Welcome to my session on “Nuances of testing applications based on Microservices architecture”.
In this session, we discuss the unique challenges and considerations of testing microservices applications. The session is going to be filled with real-life experiences on

• Service Isolation: testing needs to ensure that services function correctly in isolation as well as when integrated with other services.
• Inter-Service Communication, latency and potential points of failure.
• Data Consistency and Concurrent updates
• Deployment and Environment Parity: to ensure that tests of microservices run in an environment that closely mirrors production.
• Security Testing: Security assessments and protection against common vulnerabilities
• Performance Testing of loading Microservices
• Continuous Integration and Continuous Deployment (CI/CD)
• Automated testing:

The session is aimed to provide best practices and detailed guidance on implementing and testing microservices.

Looking forward to a very interactive session

Key takeaways are Service Isolation, Inter-Service Communication, Data Consistency and Concurrent updates, Deployment and Environment Parity, Security Testing, Performance Testing and Automated testing.

Performance testing has devolved into an activity that is primarily seen as a coding/scripting exercise. Teams and management believe that performance testing only requires coding a few scripts that ramp up a certain number of virtual users and see what happens. Furthermore, this is a task done in isolation with little to no input from business and other key stakeholders that help build the product. The results are then treated as a tick box in the release process and forgotten about unless something untoward happens during production.

We believe that performance testing is an art that requires a strategy of its own – one that needs to revisit the reason why performance testing is needed in the first place. Every product or application requires a different strategy of its own – one that involves key stakeholders that help define the application.

The session walks through the various stages of creating a performance strategy. While it cover the basics of what a performance strategy includes, it will provide a hands-on experience that allows attendees to live through the creation of such a strategy. This includes exercises that will help visualize the business needs, engage with the required stakeholders to define goals and objectives, and perform exercises that help create a performance strategy.

With emergence of generative AI, you can expedite some of the process of the strategy. The session will showcase how you can do so with couple of examples.

P.S: I might have a co-speaker with me. I am checking his availability. We have presented this earlier. But will share examples of code, test results in the updated version. This will show how application design, business context can influence the final outcome.

• Understand holistic picture of performance test
• Importance of performance test strategy
• Know your stakeholders
• Expedite the process with generative AI

Transformational Banking & Insurance SME | AI/ML & Tech Leader

With 20+ years in Banking & Insurance, I drive digital transformation for top banks like JPMC, HSBC, and Citi. Skilled in AI/ML, AWS, Agile, DevOps, and Solution Architecture. Proven leadership in managing 150+ teams, optimizing operations, and leveraging innovation for business growth. AWS Cloud Architect & SAFe 5 certified.

Hands-on technical Engineering Leader on a mission to improve engineering productivity, development velocity, and quality of releases.

At Capgemini Engineering, I lead AI innovation across Semicon, robotics, and smart IoT, embedding GenAI at the edge with tools like NVIDIA cuDNN. I’ve driven business growth by implementing AI strategies, including automation that significantly cut processing time and elevated our tech capabilities with Large Language Models.