As artificial intelligence becomes central to how organizations operate and make decisions, the role of quality engineering is being redefined. In this keynote, Sri Atluri will share real stories from the field and challenge traditional views of quality as a final step. Instead, he will explore how quality can become a force that drives innovation, builds trust, and strengthens resilience across the enterprise. This session will offer practical insights on how to lead through complexity, prepare teams for what’s coming, and shape the future of quality with clarity and confidence. Join us for a thought-provoking session that speaks to the future of engineering, leadership, and the evolving role of quality in the AI era.

• Redefining Quality Engineering in the AI Era
  – Quality is no longer just a post-development checkpoint—it’s becoming a strategic driver of innovation.
  – The talk will challenge the conventional mindset of quality as a final step in the software lifecycle.
• Trust and Resilience Through Intelligent Quality
  – AI introduces new complexities; this session aims to position quality engineering as a trust-building mechanism.
  – Emphasis will likely be placed on using quality practices to foster business resilience in dynamic environments.
• Real-World Stories for Practical Impact
  – Expect grounded insights and learnings from real field experiences that highlight successful shifts in QE paradigms.
  – This supports actionable takeaways, not just theoretical concepts.

In this talk, Adam Sandman will introduce the latest trends in AI, including GenAI and AgenticAI. He will then discuss how AI is changing the software delivery lifecycle, and how QA needs to change to adapt. Finally, he will discuss ways to ensure safety and quality of the applications being delivered.

With all the hype around AI in software testing, it’s easy for manual testers to feel anxious, sidelined, or uncertain about their place in the future. Do you need to become a developer overnight? Learn machine learning? Or worse—are you being replaced?

In this talk, we’ll cut through the noise and take a human-centered look at what AI really means for testers. You’ll learn how manual testing skills—like curiosity, empathy, and critical thinking—are not only still relevant, but more essential than ever. We’ll explore practical, approachable ways to start engaging with AI tools without needing a technical background, and discuss how testers can confidently evolve their role in an increasingly AI-assisted world.

• Manual testers aren’t being replaced—they’re being redefined.
• You don’t need to be an AI expert to start using AI effectively.
• Mindset matters more than tools.

This session explores how to seamlessly integrate automated testing into the DevOps pipeline, enabling faster, more reliable releases. By aligning test automation with Agile practices, teams can confidently deliver high-quality products at the end of each sprint, ensuring continuous delivery and improved efficiency throughout the development lifecycle.

Learn how to integrate automated testing into the DevOps pipeline to enable faster, more reliable releases.

• Understand how to align test automation with Agile practices for seamless sprint-based delivery.
• Discover strategies to build resilient, maintainable automation frameworks that support continuous development.

As enterprises deepen their investment in automation—ranging from functional UI testing to API regression suites and RPA integrations—quantifying ROI becomes essential for governance, roadmap prioritization, and stakeholder alignment. This session deconstructs automation ROI beyond simplistic cost-avoidance models, incorporating engineering-centric metrics such as test execution velocity, release throughput, defect containment ratio, and test maintainability. Attendees will explore how to establish automation KPIs that reflect both system-level efficiency and business-level outcomes, and how to avoid common pitfalls like false positive debt, tooling misalignment, and over-engineered scripts. Through a practical framework and proven strategies, the talk equips teams to deliver automation programs that scale with measurable and sustainable value.

• A structured approach to calculating and communicating automation ROI.
• Key financial and non-financial metrics to track automation success.
• How to align automation goals with organizational strategy.
• Common misconceptions and mistakes in ROI measurement.
• A practical framework to define and evaluate automation outcomes in your context.

The integration of Generative AI (GenAI) into the financial industry presents unprecedented opportunities, but also a complex landscape of regulatory compliance. FINRA and SEC guidance clearly state that existing rules, from supervision and communication standards to recordkeeping and ethical conduct, fully apply to AI- generated activities. This session will delve into how software testing, particularly through human-in-the-loop (HITL) and robust test automation strategies, becomes indispensable for financial firms to meet these stringent control requirements. We’ll explore the critical aspects of the AI lifecycle, from data preparation and model training to deployment and ongoing monitoring and demonstrate how a disciplined approach to quality assurance can ensure compliance. We will cover the “technology-neutral” stance of regulators, emphasizing that firms are fully responsible for AI outputs, regardless of their origin. This necessitates rigorous validation processes, moving beyond traditional software testing to address AI-specific risks like data bias, model explainability, and the potential for unintended outcomes. Attendees will learn how to implement effective HITL strategies to review and approve AI-generated content, especially customer communications, ensuring adherence to FINRA Rule 2210 (Communications with the Public) and preventing misleading claims. Furthermore, we’ll discuss how test automation can be leveraged to continuously monitor AI model performance, detect drift, and verify compliance with defined benchmarks and ethical guidelines. This includes automating checks for data integrity, bias detection in training data, and validating AI outputs for accuracy and fairness. Drawing parallels from FDA’s 21 CFR Part 11, we’ll highlight the importance of traceability and auditability in AI systems, showing how robust testing practices contribute to maintaining detailed audit trails of AI model lifecycles and linking AI outputs to human oversight and approvals. We will also explore how Agile methodologies can be adapted to bake compliance checkpoints into every stage of the AI development lifecycle. By treating regulatory requirements as explicit “user stories” and incorporating compliance-related non-functional requirements and test cases into sprint planning, teams can proactively build AI systems that are compliant by design. This session will provide actionable insights for test automation engineers, QA professionals, and developers on how to design and implement testing frameworks that not only assure the quality and reliability of GenAI but also serve as verifiable evidence of regulatory adherence, ultimately safeguarding firms against potential enforcement actions and fostering trust in AI-driven financial services.

In the rapidly evolving field of Quality Engineering, integrating advanced AI technologies poses significant challenges, particularly in upskilling current staff and incorporating multi-agentic platforms. This presentation addresses these challenges by exploring the transformative potential of Agentic AI in QE. Join Michael Giacometti as he will discuss practical strategies for upskilling QE teams to effectively leverage these technologies, ensuring they are equipped to handle the complexities of Agentic AI. Additionally, Michael will detail how integrating multi-agentic tools can streamline workflows, enhance efficiency, and improve overall software quality. Through real-world case studies and examples, attendees will gain insights into successful implementation strategies and the tangible benefits of Agentic AI. By the end of this session, conference delegates will leave with actionable takeaways on how to enhance their QE processes, drive innovation, and stay ahead in the competitive landscape of AI and software testing.

VP, Electronic Trading Technology, Bank of America | MBA, Stern School of Business, NYU, 2018 (Specializations: Strategy, Technology, Finance) |

MS in Computer Science, Northeastern University | AWS Certified Solutions Architect (Associate).

Hardworking and motivated Computer Science graduate with cloud expertise. Certified AWS Solutions Architect Associate with experience at IBM, AWS, and JP Morgan. Skilled in deploying apps to AWS, GCP, Kubernetes, IBM Cloud, and Heroku. Strong interpersonal skills and a passion for technology.

Senior Technology Executive with 20+ years in FinTech, specializing in Platform Quality Engineering, DevOps, and Program Management. Expert in driving scalable, high-quality software delivery using cloud, AI, and agile practices. Proven leader in transformation, innovation, and building high-performing global teams with a strong quality-first mindset.

Seasoned QA leader and test automation architect with 20+ years of experience. As VP of QA Engineering at Goldman Sachs, I drive test strategy, lead high-performing teams, and architect automation platforms that ensure quality at scale across enterprise systems in Agile and SAFe environments.

Expert in test automation, validation, and CoE management. Proven success in driving AI-powered solutions that improve efficiency, ensure compliance, and align with business goals. Skilled in leading cross-functional teams and implementing advanced automation frameworks.

Expertise

• AI & Automation Strategy
• CoE Leadership & Governance
• Test Validation & Compliance (FDA, EUDAMED)
• RPA & Emerging Tech (Blue Prism, Selenium, Watson X)
• Data Integration & BI Validation
• Change Management & Stakeholder Collaboration

Highlights

• Led enterprise AI transformation and automation roadmap
• Managed CoE for test automation and quality governance
• Delivered measurable gains in efficiency and compliance
• Championed innovation and cross-functional alignment