In a world where smart manufacturing, connected systems, and digital platforms are reshaping industries, Quality Assurance (QA) must evolve to match the speed, complexity, and expectations of modern enterprises. For manufacturing and IT organizations alike, Artificial Intelligence (AI) is at the heart of this transformation — powering a shift from reactive testing to proactive, intelligent quality engineering.

This keynote explores how AI and next-gen automation are redefining quality by enabling QA teams to:

• Envision the future through AI-driven insights that anticipate quality risks before they surface, using system behavior patterns, change impact analysis, and historical data modeling.
• Automate manual testing at scale using advanced low-code/no-code tools, self-healing frameworks, and intelligent test generation — drastically reducing execution time and accelerating test cycles.
• Deliver faster, safer releases by integrating AI-enabled quality checks into CI/CD pipelines, ensuring continuous feedback and resilient automation across ERP systems, MES, PLM, and IT applications.

Real-world use cases will highlight how modern QA practices are:

• Reducing time-to-market by converting thousands of manual test cases into reliable automation scripts.
• Enhancing visibility across complex digital ecosystems — from manufacturing execution systems to custom enterprise apps.
• Empowering QA teams to focus on strategic testing while AI handles the repetitive and redundant.

With this roadmap we are moving to next 3 years goal of:

• Predict defects through intelligent analysis of system logs, code changes, and production data.
• Prevent disruptions using AI-powered test generation, adaptive test maintenance, and real-time data validation.
• Perfect operations with self-healing automation, visual testing, and continuous feedback loops integrated into DevOps pipelines.

Whether you’re driving digital innovation in a factory floor or modernizing your IT landscape, this session will equip you with strategies to:

• Embrace AI and automation as catalysts for quality.
• Shift left and enable predictive QA.
• Align test outcomes with business performance metrics.

Even with modern frameworks like Selenium, Playwright, or Cypress, most testers still deal with fragile locators, flaky scripts, and too much time spent on test upkeep. This session introduces a smarter way forward: Agentic AI that works more like a human — no scripts, no frameworks, and no maintenance overhead.

• Understanding runtime agentic AI test automation
• Being able to articulate the benefits and differences between a co-pilot approach to test automation and a runtime agentic AI approach

Implementation of Devsecops allows for continuous security testing of multiple applications using DAST (Dynamic Application Security Testing) methodology and applications using SAST (Static Application Security Testing) methodology, thereby improving code quality and faster approach for security testing using various tools like CheckmarxOne, Burp Suite and other methodoligies.

To implement a strong shift left strategy, applying security at every level of the SDLC life cycle which ensures in releasing a secure product(applications) to the market. Embed security protocols into developer IDEs, DevOps pipelines, and source code repositories to ensure product security.

AI is transforming quality assurance from repetitive automation to intelligent, self-driven systems. This talk explores how agentic AI—systems that reason, adapt, and act independently—is being used to supercharge API testing and documentation in modern development environments.

• Through real-world examples, you’ll see how AI can:
• Automatically generate API test cases,
• Keep Swagger/OpenAPI docs up to date,
• Integrate seamlessly with CI/CD,
• Scale effortlessly across diverse codebases.

Whether you’re a QA engineer, developer, or engineering leader, you’ll gain actionable insights to future-proof your quality pipeline with AI-driven solutions.

• Agentic AI enables self-maintaining QA systems for APIs.
• You can auto-generate tests and docs reliably—even at scale.
• AI integrates naturally into CI/CD workflows.
• Think beyond automation—think autonomy.
• The future of QA is not just faster… it’s smarter.

Test automation is evolving, and context is now the key differentiator. Model Context Protocol (MCP) represents a fundamental shift in how AI interacts with automation frameworks. By allowing AI to “understand” where it is, what happened, and what needs to happen next, MCP introduces a new dimension of intelligence to our test pipelines. This session will showcase how MCP acts as the backbone for truly collaborative, adaptive testing between AI and humans and how you can start using it today with tools like Playwright.

• What is MCP?
  Gain a clear understanding of Model Context Protocol (MCP) and how it introduces structured, persistent context into AI-powered automation workflows.
• Why Context Matters in Testing
  Explore the limitations of traditional automation and how context-aware systems improve test reliability, adaptability, and decision-making.
• MCP + Playwright in Action
  Learn how to implement context-driven testing using Playwright and LLMs, including practical architecture, tooling, and integration strategies.
• Smarter, Adaptive Test Systems
  Discover how MCP enables dynamic test generation, self-healing scripts, and intelligent workflows that reduce flakiness and improve test feedback loops.
• Getting Started with MCP
  Walk away with actionable steps to begin adopting MCP in your automation pipelines — from initial setup to gradual scaling.
• Looking Ahead
  Understand the future role of MCP in scaling AI-compatible QA practices and why it will be a foundational element in next-generation test engineering.

False negatives in software testing pose a significant risk, allowing defects to go undetected and potentially reach production. As AI tools become more prevalent in test generation and execution, they can unintentionally contribute to these overlooked issues. This talk explores the challenges of false negatives in fast-paced testing environments, highlighting how human factors, test process complexity, and AI-driven automation intersect to impact software quality. Attendees will gain insight into understanding and mitigating the risks of hidden defects throughout the testing process.

• Recognize the risk

Understand how false-negative results can allow defects to slip into production and undermine trust in testing.

• Identify causes

Learn the most common sources of false negatives throughout the testing process.

• Prevent missed bugs

Discover best practices and strategies to reduce false negatives and improve test accuracy.

• AI as a double-edged sword

AI can accelerate testing, but may also mask defects if applied within an immature or poorly structured test process.

I am a technology leader in diverse areas with a passion for enterprise data management, business intelligence, software quality assurance, and process optimization. Throughout my career, I have successfully built and led high-performing teams and implemented cutting-edge testing methodologies to enhance system reliability and efficiency. With deep expertise in ETL development, data warehousing, test automation, and agile transformations, I bridge the gap between business and technology. I believe in fostering collaboration, mentorship, and continuous improvement, ensuring that teams stay adaptable and forward-thinking. Whether it’s reducing defects through proactive QA strategies, streamlining ETL workflows, or executing large-scale digital transformation initiatives, I am committed to delivering measurable business results.

Lead QA Engineer with proficiency in Web UI, REST APIs and Mobile Test Automation and AWS Cloud Infrastructure Testing

• AWS Certified Developer Associate
• Proficient with Web/UI, REST APIs, cloud infrastructures and CRM products with Ruby-cucumber automation test framework in Agile methodology
• Acceptance Test Driven Development
• Contributing to CICD with GitHub and Jenkins
• Experience with Java-TestNG Test Automation for APIs
• Experienced in Mobile NFC Payments testing based on HCE
• Manual and automation testing of Mobile apps using mobile cloud test tools such as Device Anywhere, Mobile Labs Device Connect, Perfecto Mobile, SeeTest
• Testing product upgrades and migrations for CRM/messaging products
• Performance testing using Apache Jmeter
• Proficient with various Test automation and Test management toolsLead QA Engineer with proficiency in Web UI, REST APIs and Mobile Test Automation and AWS Cloud Infrastructure Testing – AWS Certified Developer Associate – Proficient with Web/UI, REST APIs, cloud infrastructures and CRM products with Ruby-cucumber automation test framework in Agile methodology – Acceptance Test Driven Development – Contributing to CICD with GitHub and Jenkins – Experience with Java-TestNG Test Automation for APIs – Experienced in Mobile NFC Payments testing based on HCE – Manual and automation testing of Mobile apps using mobile cloud test tools such as Device Anywhere, Mobile Labs Device Connect, Perfecto Mobile, SeeTest – Testing product upgrades and migrations for CRM/messaging products – Performance testing using Apache Jmeter – Proficient with various Test automation and Test management tools.

Technology professional with 20+ years of experience in Software Quality Assurance, Product and Project Management across IT Ecommerce, Wireless, Aerospace, Healthcare, and Finance industries. PMP, CSQA, AWS CCP & AI certified, with expertise in Agile, Scrum, Six Sigma, and ongoing SAFe learning. Proven track record in leading complex cross-functional projects, cloud/on-prem solutions delivery, vendor procurement, and IT governance for risk and compliance. Skilled in test automation (web/mobile/AWS), DevOps, and tools like Selenium, Robot Framework, JIRA, Jenkins, and Tableau. Passionate about driving quality engineering through innovation and AI, with strong leadership in DEI and community initiatives. Conference speaker on technology, quality, and governance.