What Happens When Agile Fails? Watch This!
EP212 S06
Today in Advanced Quality Programs with Juan Navarro, we are discussing the integration of Agile methodologies and generative AI—a combination that is reshaping the way high-performing teams develop products. This session focuses on practical solutions implemented by real teams to address actual challenges, with detailed insights into their approaches.
#AdvancedQualityPrograms #TheQualityGuy #AgileMeetsAI
Learn how one product team turned things around after initial struggles with the agile methodology. They were experiencing failure despite following the book, but with some key adjustments achieved agile success. This video shares some agile tips to help your scrum team avoid similar pitfalls in product management.
Three months ago, I spoke with a product manager named Leila. Her team was drowning—sixty-hour weeks, bug reports piling up faster than they could fix them, customers threatening to leave. She told me, “Juan, we’re doing Agile by the book, but we’re still failing.”
Today, her team ships twice as fast with half the defects. What changed? They stopped treating AI as a threat and started treating it as a teammate.
Let me paint you a picture. You’re leading a product team. You’ve got an impossible deadline. Your QA team is overwhelmed. Your developers are burned out from repetitive tasks. Your stakeholders keep changing requirements. Sound familiar?
Here’s what typically happens: You adopt Agile. You do the standups, the sprints, the retrospectives. And yes, it helps. But you’re still stuck with the same bottlenecks.
Planning meetings that drag on for hours because nobody can agree on priorities. Wireframing that takes days when you need answers in hours. Code reviews that catch syntax errors but miss the bigger architectural problems. Testing cycles that find bugs too late when they’re expensive to fix.
The human brain is amazing, but it wasn’t designed to process the volume and complexity of modern product development. That’s not a weakness. That’s just reality.
Now here’s where it gets interesting. What if I told you there’s a way to keep everything that makes Agile powerful: the collaboration, the customer focus, the adaptability, while eliminating the friction that slows you down?
That’s exactly what generative AI does when you integrate it correctly. And I emphasize correctlybecause most teams get this wrong at first.
Let me tell you about Carlos. Senior developer. Fifteen years of experience. When his company introduced AI-powered code review, his first reaction was anger.
“Management wants to replace us with robots,” he said. His whole team felt insulted. They saw AI as a threat to their expertise, their jobs, their identity as craftspeople.
This resistance is everywhere. And honestly? It’s understandable. We’ve been told for years that AI is coming for our jobs, epic movies about taking humankind down. We’ve seen the hype cycles. We’ve watched companies waste money on technology that doesn’t deliver.
But here’s what Carlos and his team didn’t understand at first: AI isn’t replacing expertise. It’s amplifying it. It is all about application.
Think about it this way. When calculators were invented, did they replace mathematicians? No. They freed mathematicians from tedious arithmetic so they could focus on solving harder problems. That’s exactly what’s happening here.
So let me show you what this looks like in practice. Real examples. Real results.
Example One: Planning and Prioritization
Remember those four-hour planning meetings? One team I worked with cut them down to ninety minutes. Here’s how: They fed their AI system data from their last fifty sprints. The AI identified patterns they couldn’t see. It predicted which types of tasks would create delays based on team capacity, dependencies, and historical velocity.
Now when they plan, they’re not guessing. They’re making decisions based on their own proven patterns. The humans still make the final call, but they’re working with better information.
Example Two: Design and Prototyping
Another team was spending three days creating wireframes for user testing. By the time they got feedback, they’d already invested too much to change direction easily. Now they use AI to generate initial wireframes in minutes. Not final designs, starting points. The AI creates five different approaches to the same problem. The design team picks the most promising one, refines it, and gets it in front of users the same day. They’re testing hypotheses faster. Failing faster. Learning faster. That’s the whole point of Agile, right? AI just accelerates the cycle.
Example Three: Code Quality
Back to Carlos. Here’s what changed his mind. He pushed some code one afternoon. Before he finished his coffee, the AI had checked forty-seven quality standards, found three security vulnerabilities, and suggested fixes that matched his team’s coding style.
But here’s the key: the AI didn’t approve the code. It prepared it for human review. When Carlos’s colleague reviewed it, she could focus on business logic, architectural decisions, and edge cases. Not syntax errors. Not formatting issues. Not the tedious stuff.
Carlos realized: “This isn’t replacing me. This is giving me back time to do the work that actually requires my experience.”
Now, I’m not going to stand here and tell you this is easy. There are real challenges you need to address.
Challenge One: Alignment and Governance
You can’t just throw AI tools at your team and hope for the best. You need clear objectives. What problems are you actually trying to solve? Who’s responsible for AI outputs? How do you ensure quality and consistency?
The teams that succeed establish AI governance from day one. They define use cases. They set quality standards. They create feedback loops.
Challenge Two: The Human Element
Some people will resist. Some will over-rely on AI and stop thinking critically. Both are problems.
You need to train your team, not just on the tools, but on the mindset. AI is a collaborator, not an oracle. It makes suggestions based on patterns. Humans make decisions based on context, judgment, and values.
Challenge Three: Bias and Limitations
AI systems learn from data. If your data has biases, your AI will too. If your context changes, the AI might not adapt immediately.
You need human oversight. Always. The AI can process information faster than you, but it can’t understand your business strategy, your customer relationships, or your ethical obligations.
Here’s what I’ve learned working with teams across different industries: The ones who succeed with Agile and AI aren’t the ones with the fanciest tools. They’re the ones who understand the principle. Their Core business.
Agile is about responding to change. AI is about processing information at scale. Together, they create something powerful: teams that can move fast without breaking things.
You get faster iteration cycles. Better quality management. More time for creative problem-solving. Less time on repetitive tasks.
But most importantly, you get teams that aren’t burned out. Teams that actually enjoy their work because they’re focused on the parts that require human creativity, empathy, and judgment.
So, here’s my question for you: What’s one repetitive task in your product development process that’s draining your team’s energy right now?
It’s writing test cases. It’s documenting requirements. It’s analysing user feedback. Whatever it is, there’s probably a way AI could handle the heavy lifting while your team focuses on strategic thinking.
Drop your answer in the comments. Tell me what you’re struggling with. I’ll respond with specific approaches and tools that might help your situation.
And if you found this valuable, subscribe to Advanced Quality Programs. We’re exploring how quality management and modern technology come together to build better products and better teams. Thanks for listening. I’m Juan Navarro, don’t forget to explore the quality mindset (my book on agile thinking) … that’s it for now and I’ll see you in the next one.