Oluyomi Emmanuel: Constructive Independence as a Scrum Master Success Metric

“Success is when the team increases its capacity to notice, discuss, and solve the problems that stand between them and meaningful outcomes.” – Oluyomi Emmanuel

Oluyomi’s definition of Scrum Master success has changed over time. Today, he measures success by the team’s growing ability to notice, discuss, and solve the problems that stand between them and meaningful outcomes. One practical signal is when someone in the team says or does something he would previously have needed to prompt: a developer challenges an unclear requirement, a tester raises a risk before anyone asks, or a team member names a problem directly. Oluyomi calls the strongest indicator “constructive independence.”

That does not mean the team no longer needs support. It means the team’s mental model has matured enough that people act earlier, with more ownership and less dependency. To keep himself accountable, Oluyomi runs a small personal retrospective at the end of each workday. He starts the day with a few things he wants to measure himself by, then later uses Copilot as a reflection partner to compare intention with action.

Vasco notes that this is one useful way AI tools can help Scrum Masters maintain the discipline of reflection without relying only on memory.

Self-reflection Question: What would tell you that your team is becoming constructively independent?

Featured Retrospective Format for the Week: True or False Statements

Oluyomi does not pick a retrospective format before understanding the sprint. He watches patterns during the iteration: how work waits, how conflict is handled, whether decisions are revisited, and whether people ask for help early. One format he often uses is a set of true-or-false statements about the sprint and collaboration. For example: “We finished work before starting more work” or “We asked for help early enough during the sprint.”

Team members vote silently first, either on a Miro board or with physical cards. The value is not the majority answer. The value appears when different people interpret the same sprint differently. Instead of asking who is right, Oluyomi asks what each group observed that led to its answer.

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About Oluyomi Emmanuel

Oluyomi EmmanuelOluyomi is a Scrum Master, Agile Coach, and Product Lead with over seven years of experience helping teams work better and deliver meaningful digital products. Having worked across energy, consumer platforms, and fintech, he is passionate about building high-performing teams, creating clarity, and turning Agile principles into real business and customer value.

You can link with Oluyomi Emmanuel on LinkedIn.

BONUS: From AI Curiosity to Practical Project Tools With William Davis

AI becomes useful when it moves from generic answers into the daily work of solving real problems. In this BONUS episode, William Davis shares how he started building his own AI-assisted project management tools, what went wrong when the code grew too fast, and how Scrum Masters can use AI more carefully at individual, team, and project levels.

When AI Stops Being a Curiosity

“I really need to get a handle on how to productively use AI in my job.”

William’s shift started with a familiar problem: release plans are full of uncertainty, but stakeholders still need to understand what might happen and when. Instead of handcrafting uncertain delivery ranges in Excel, William used AI to build a desktop application that created probabilistic Gantt charts. In his work, these were not traditional command-and-control schedules. They were release plans that showed stakeholders a realistic range of possible delivery dates, making uncertainty visible instead of hiding it behind a false promise.

The Euphoria and the Crash

“Anybody can prompt an app into existence, but if you want an app that is actually enterprise-worthy, it does take a little bit of software engineering knowledge to know what questions to ask.” Continue reading BONUS: From AI Curiosity to Practical Project Tools With William Davis

AI Assisted Coding: The Five Levels of AI-Augmented Coding—From Prompter to Orchestrator

What separates vibe coding from truly effective AI-assisted development? In this episode, Adam Bilišič shares his framework for mastering AI-augmented coding, walking through five distinct levels that take developers from basic prompting to building autonomous multi-agent systems.

Vibe Coding vs AI-Augmented Coding: A Critical Distinction

“The person who is actually creating the app doesn’t have to have in-depth overview or understanding of how the app works in the background. They’re essentially a manual tester of their own application, but they don’t know how the data structure is, what are the best practices, or the security aspects.”

Adam draws a clear line between vibe coding and AI-augmented coding. Vibe coding allows non-developers to create functional applications without understanding the underlying architecture—useful for product owners to create visual prototypes or help clients visualize their ideas.

AI-augmented coding, however, is what professional software engineers need to master: using AI tools while maintaining full understanding of the system’s architecture, security implications, and best practices. The key difference is that augmented coding lets you delegate repetitive work while retaining deep knowledge of what’s happening under the hood.

From Building Features to Building Systems

“When you start building systems, instead of thinking ‘how can I solve this feature,’ you are thinking ‘how can I create either a skill, command, sub-agent, or other things which these tools offer, to then do this thing consistently again and again without repetition.'” Continue reading AI Assisted Coding: The Five Levels of AI-Augmented Coding—From Prompter to Orchestrator

BONUS: Thinking Like an Architect in the Age of AI-Assisted Coding

How can engineers leverage AI to write better code—and think like architects to build systems that truly scale? In this episode, Brian Childress, a CTO and software architect with over 15 years of experience, shares hard-won lessons from teams using AI coding tools daily, and explains why the real challenge isn’t just writing code—it’s designing systems that scale with users, features, and teams.

The Complexity Trap: When AI Multiplies Our Problems

“Most engineering projects and software engineers themselves lean more towards complexity, and I find that that complexity really is multiplied when we bring in the power of AI and its ability to write just tons and tons and tons of code.”

Brian has observed a troubling pattern: AI tools can generate deeply nested components with complex data flows that technically work but are nearly impossible to understand or maintain. When teams don’t guide AI through architectural decisions, they end up with code that becomes “a little too complex for us to understand what is actually going on here.” The speed at which AI produces code makes understanding the underlying problem even more critical—we can solve problems quickly, but we must ensure we’re solving them the right way.

In this segment, we mention our longer AI Assisted Coding podcast series. Check that out for further insights and different perspectives on how our software community is learning to make better use of AI Assisted Coding tools.

Vibe Coding Has Its Place—But Know Its Limits

“Vibe coding is incredibly powerful for designers and product owners who want to prompt until they get something that really demonstrates what they’re trying to do.” Continue reading BONUS: Thinking Like an Architect in the Age of AI-Assisted Coding

AI Assisted Coding: From Designer to Solo Developer – Building Production Apps with AI

In this special episode, Elina Patjas shares her remarkable journey from designer to solo developer, building LexieLearn—an AI-powered study tool with 1,500+ users and paying customers—entirely through AI-assisted coding. She reveals the practical workflow, anti-patterns to avoid, and why the future of software might not need permanent apps at all.

The Two-Week Transformation: From Idea to App Store

“I did that, and I launched it to App Store, and I was like, okay, so… If I can do THIS! So, what else can I do? And this all happened within 2 weeks.”

Elina’s transformation happened fast. As a designer frustrated with traditional software development where maybe 10% of your original vision gets executed, she discovered Cursor and everything changed. Within two weeks, she went from her first AI-assisted experiment to launching a complete app in the App Store. The moment that shifted everything was realizing that AI had fundamentally changed the paradigm from “writing code” to “building the product.” This wasn’t about learning to code—it was about finally being able to execute her vision 100% the way she wanted it, with immediate feedback through testing.

Building LexieLearn: Solving Real Problems for Real Users

“I got this request from a girl who was studying, and she said she would really appreciate to be able to iterate the study set… and I thought: “That’s a brilliant idea! And I can execute that!” And the next morning, it was 9.15, I sent her a screen capture.”

Lexie emerged from Elina’s frustration with ineffective study routines and gamified edtech that didn’t actually help kids learn. She built an AI-powered study tool for kids aged 10-15 that turns handwritten notes into adaptive quizzes revealing knowledge gaps—private, ad-free, and subscription-based. What makes Lexie remarkable isn’t just the technology, but the speed of iteration. When a user requested a feature, Elina designed and implemented it overnight, sending a screen capture by 9:15 AM the next morning. This kind of responsiveness—from customer feedback to working feature in hours—represents a fundamental shift in how software can be built. Today, Lexie has over 1,500 users with paying customers, proving that AI-assisted development isn’t just for prototypes anymore.

The Workflow: It’s Not Just “Vibing”

“I spend 30 minutes designing the whole workflow inside my head… all the UX interactions, the data flow, and the overall architectural decisions… so I spent a lot of time writing a really, really good spec. And then I gave that to Claude Code.” Continue reading AI Assisted Coding: From Designer to Solo Developer – Building Production Apps with AI

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Overcome Team Resistance and Gain Leadership Buy-In
Discover practical, real-world solutions from leading Agile practitioners. Download three free chapters from 'Tips from the Trenches Scrum Master Edition' and start transforming your Agile practices today!
Overcome Team Resistance and Gain Leadership Buy-In
Discover practical, real-world solutions from leading Agile practitioners. Access three free chapters from 'Tips from the Trenches Scrum Master Edition' and start transforming your Agile practices today!
Overcome Team Resistance and Gain Leadership Buy-In
Discover practical, real-world solutions from leading Agile practitioners. Download three free chapters from 'Tips from the Trenches Scrum Master Edition' and start transforming your Agile practices today!
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