In this episode, host Michael Bernzweig interviews Khurram Mir, CMO and founder of Kualitatem and Kualitee, about how quality engineering is changing in the AI era. Khurram explains why traditional QA—built as a late‑stage gate for waterfall releases—can’t handle today’s rapid deployment cycles and AI‑enabled systems. He shows how to shift testing left, embed quality across the SDLC, and use generative AI for test case generation, synthetic data, self‑healing automation, and smarter defect analysis so quality becomes a growth enabler instead of a release bottleneck.

Key topics:

• The limitations of traditional QA in rapid release cycles and how to adapt
• Practical AI tools for test case generation, synthetic data, and self-healing automation
• The importance of embedding quality across the entire SDLC, not just in QA
• Red flags indicating waterfall thinking still dominates an organization’s QA approach
• Roadmap for AI adoption: pilot, integrate, and mature with predictive capabilities
• Upskilling testers into business-savvy quality engineers through critical thinking
• The shift from reactive testing to proactive, risk-driven quality management
• Using AI for defect triage, test data creation, automation maintenance, and integration mapping
• Aligning teams and KPIs to foster shared ownership of quality
• Data pipeline best practices for AI reliability and real-time transformation

Timestamps:

00:00 - Introduction to AI-driven quality engineering revolution
00:29 - Khurram Mir’s personal journey from software tester to QA innovator
01:13 - Key elements for executives to start with quality engineering
03:00 - Red flags signaling waterfall QA in modern organizations
04:36 - Measurable outcomes of investing in quality early
05:21 - Impact of maturity levels on quality transformations
09:44 - Evolution of QA from waterfall to continuous deployment models
11:00 - Why traditional QA models break in AI-enabled fast release cycles
13:22 - Role of QA as a system, embedded from requirements to deployment
15:52 - The impact of generative AI on test case creation and defect prediction
17:27 - How AI addresses the "blank page" problem in test design
19:40 - Synthetic data generation for healthcare and regulated industries
21:52 - Self-healing automation and intelligent defect analysis
23:02 - Roadmap to AI adoption: start small, scale responsibly
24:29 - Quality as a growth enabler, not a cost center
25:05 - Live Q&A session highlights
34:36 - Deep dive into data pipeline best practices for AI reliability

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In this insightful conversation, Karen Nikoghosyan, CEO of eSwap Global, shares his entrepreneurial journey from a young software engineer in Armenia to leading a multi-channel inventory and order management platform. Alongside host Michael Bernzweig, they discuss the transformative role of AI in e-commerce, emphasizing its impact on customer experience, operations, and the competitive landscape. The discussion also covers the challenges and opportunities of AI-driven marketing and the future of AI employees in e-commerce.

Takeaways

• Entrepreneurial journeys are rarely straight paths to success.
• AI is transforming e-commerce from customer experience to operations.
• Karen started his journey in software engineering at a young age.
• eSwap Global offers inventory and order management for multi-channel sellers.
• AI tools are essential for efficient e-commerce operations.
• AI-driven marketing is changing competition on platforms like Amazon and eBay.
• AI employees will soon be integral to e-commerce businesses.
• Personalization and automation are key to e-commerce success.
• Predictive analytics and fraud prevention are crucial in e-commerce.
• Starting with AI early is vital for staying competitive.

Sound bites

• AI is transforming e-commerce.
• Entrepreneurial journeys are rarely straight paths.
• AI-driven marketing is the future.
• Personalization is key to success.
• Automation is revolutionizing e-commerce.
• Predictive analytics is crucial.
• Fraud prevention is vital.
• Start with AI early.
• AI employees are the future.
• Karen's journey from engineer to CEO.

Chapters

• Introduction to Karen Nikoghosyan
• The Role of AI in E-Commerce
• Challenges and Opportunities in AI-Driven Marketing
• The Future of AI Employees in E-Commerce

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Host Michael Bernzweig speaks with podcast guest Greg Lind of Buildly, who discusses the evolution of Agile methodologies, emphasizing the need to adapt and integrate AI tools for more efficient software development processes. He highlights the limitations of traditional Agile practices and introduces the concept of Rapid AI Development (RAD) as a more suitable approach for modern development needs.

Takeaways

• Agile methodologies must evolve to accommodate AI integration.
• Traditional Agile practices have limitations in modern contexts.
• Rapid AI Development (RAD) is introduced as a new approach.
• AI tools can enhance backlog management and development speed.
• Collaboration between dev and product teams is crucial.
• Daily processes and micro sprints are more effective than traditional sprints.
• AI and human collaboration is key to successful development.
• Continuous integration and deployment are essential for modern workflows.
• Reducing ceremony overhead can improve development efficiency.
• Involving diverse teams in decision-making enhances outcomes.

Sound bites

• Time to Kill Agile.
• Agile's outdated for most use cases.
• AI tools manage your backlog.
• RAD: Rapid AI Development.
• Collaboration is where the key is.
• Daily processes, not weekly.
• AI and human collaboration.
• Continuous integration is essential.
• Reduce ceremony overhead.
• Involve diverse teams in decisions.

Chapters

1. Introduction to Greg Lind
2. The Evolution of Agile
3. Agile's Strengths and Weaknesses
4. The Role of AI in Development
5. Introducing Rapid AI Development
6. Collaboration and Continuous Integration
7. The Future of Agile and RAD

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