AI coding tools are helping teams move faster than ever, but there's a hidden cost.

In this episode, we break down new insights from a DevOps industry report revealing a growing "velocity paradox": teams are shipping more code, but experiencing more failures, rollbacks, and burnout.

You'll discover why AI adoption is heavily skewed toward coding, but not testing, pipelines, or observability, and how that imbalance is creating fragile systems that break under pressure.

More importantly, you'll learn what high-performing teams are doing differently to maintain quality while scaling speed.

✔️ Why AI is increasing deployment failures (and how to stop it)
✔️ The "velocity vs quality" trap hurting modern DevOps teams
✔️ How to reduce flaky tests and pipeline instability
✔️ Why observability and feature flags are now critical, not optional
✔️ Practical ways to improve your CI/CD pipeline for AI-driven development
✔️ The role of QA engineers in the age of AI (and why it's growing, not shrinking)

If you're a tester, automation engineer, or DevOps leader trying to keep up

AI is changing how we build and test software, but most teams are struggling to turn that promise into real results.

In this episode, we break down what it actually takes to scale quality engineering across global teams without creating bottlenecks, burnout, or broken processes.

You'll learn:

• why most test automation and transformation initiatives fail
• how to separate AI hype from reality
• what high-performing teams are doing differently to ship faster with confidence

Today's expert, Sunita McCoy, a Global Engineering Leader and Transformation Specialist, shares practical insights from leading large-scale engineering transformations, including:

• how to build a culture that supports AI adoption
• why "quality as a phase" is dead
• how to shift toward treating quality as a product

If you're a QA leader, automation engineer, or DevOps professional trying to improve reliability, reduce risk, and future-proof your skills in the age of AI, this episode gives you a clear path forward.

Mobile test automation is still one of the biggest bottlenecks in modern software delivery. In this interview, QApilot's Co-founder Aditya Challa explains why most AI testing approaches fail and how to fix them.

Learn more about QApilot: https://links.testguild.com/flutterqa

If your mobile tests are flaky, slow, or hard to trust, you're not alone.

Most teams are trying to apply LLM-based AI to problems that actually require deterministic reliability—and that's where things break down.

In this video, you'll learn:

• Why mobile test automation breaks at scale
• The real issue with "99% accurate" AI in testing
• LLMs vs deterministic AI (and why it matters for mobile apps)
• How flaky tests destroy confidence in your pipeline
• How QApilot approaches mobile testing differently
• What reliable, scalable mobile automation should look like

What this means for you:

Fewer false positives, faster releases, and mobile tests you can actually trust.

00:00 Why Mobile Test Automation Is Still Broken
01:10 QApilot Overview
01:51 Why Mobile Testing Tools Fail
03:13 Why Appium Isn't Enough
05:09 QApilot's Approach to Mobile Testing
07:10 Scaling Mobile Testing Across Devices
08:02 Autonomous Testing + Human in the Loop
10:55 How QApilot Works (Architecture + Agents)
13:45 Real Example: Mobile App Crawling in Action
16:31 Finding Bugs Automatically (Performance + Accessibility)
18:52 Device Farms & Real Device Testing
21:50 Future of Mobile Testing (SRE + AI + Quality Layer)
27:06 Real Customer Results & Case Study
31:02 Why QApilot Focuses Only on Mobile
34:04 Where QApilot Fits in CI/CD
36:00 How to Try QApilot + Final Advice