Why Every Scrum Master Should Know How Code Works

Why Every Scrum Master Should Know How Code Works

(and why AI can’t fix what ignorance breaks)

A few months ago, I ran a deep analysis of a struggling enterprise software project. From the outside, it appeared to be a “technical failure” — characterised by poor velocity, constant regressions, testing chaos, and a general sense that the team was always one sprint behind. However, upon examining the repository, the pattern was clear: not a single one of the problems was truly technical. Every issue traced back to process failure that a technically literate Scrum Master could have caught.

The Pattern Behind the Chaos

Looking across the repo, we found:

Issue	Root Cause	Preventable by Scrum Master?
145 stale branches	No branch cleanup policy	✅ Enforce DoD: “Delete after merge”
30 unmerged branches	No merge discipline	✅ “Merge to develop every sprint”
270 BDD tests “complete” but 239 missing	No validation of “done”	✅ “Tests must run in CI”
90% of tests on feature branches	No integration requirement	✅ “CI on main, not feature”
Main branch 5 months stale	No merge cadence	✅ Sprint ritual: “Merge before retro”
Logging & monitoring off	No production readiness gate	✅ “Observability required for release”

10 out of 10 were process failures, not capability failures.

The team wasn’t lazy or incompetent. They were just unprotected — nobody was enforcing discipline, surfacing debt, or pushing back when leadership demanded “just one more feature.”

That’s not a developer problem. That’s a Scrum Master problem.

The Real Job of a Scrum Master

Too many Scrum Masters come from a background in project coordination, not engineering. They can run a meeting, update Jira, and manage a burndown chart — but they can’t tell if a “test” is real or if the code in develop hasn’t been merged for six weeks.

A proper Definition of Done isn’t a checklist — it’s a contract of technical hygiene.

What “Done” should mean:

☐ Code reviewed and merged to develop within 2 days

☐ All tests passing in CI

☐ Branch deleted

☐ Monitoring configured

☐ Jira linked to PR

If your Scrum Master can’t read a CI report, that contract is meaningless.

Why Non-Technical Scrum Masters Miss the Red Flags

Here’s what happens when the Scrum Master doesn’t understand code:

270 tests “complete
- ”Non-technical SM: “Great!”Technical SM:
- “Show me the CI logs. Only 31 actually ran — the rest are unimplemented specs.”
145 branches in Git
- Non-technical SM: “Developers like branches.”
- Technical SM: “These are unmerged features. That’s 145 invisible risks.”
“All checks passed.
- ”Non-technical SM: “Perfect.”
- Technical SM: “This workflow only runs on main. Ninety percent of tests never execute.”

Scrum without technical literacy is like aviation without instruments — you can keep the plane level for a while, but you’ll never know if you’re upside down.

The XP Connection: Process Is Technical Practice

Extreme Programming (XP) got this right decades ago. Continuous Integration, Test-Driven Development, Refactoring — these are process controls as much as technical ones.

XP Practice	Why Non-Technical SMs Struggle
TDD	Can’t tell if tests exist, let alone run
CI/CD	Don’t read build logs or branch reports
Refactoring	Don’t recognise code smells
Pair Programming	Don’t know when to suggest it
Simple Design	Can’t identify over-engineering

You don’t need to be a developer. But you do need to understand what good development looks like.

The AI Temptation

When I analysed the codebase, I used an LLM to scan the repository. It was incredibly effective — because I knew what to ask.

A non-technical Scrum Master could ask the same tool,

> Show me the testing summary

and get back a tidy report.

But they wouldn’t feel what those numbers mean.

AI Insight: “145 branches haven’t been merged."

Human Intuition: “That means integration hell is coming.”

AI Insight: “Test coverage 31/270.”

Human Intuition: “Our CI is lying to us. DoD’s being gamed.”

AI can spot anomalies. It can’t tell you which ones matter to delivery velocity. That judgment still comes from technical empathy.

From Curiosity to Competence

When I reflect on my own journey, it wasn’t ambition or job description that made me technical — it was curiosity. I’ve always needed to understand how things actually work, and that itch pulled me closer to the code.

I was lucky to work with a few XP monks early in my career — the kind of developers who treated refactoring as meditation. They didn’t just ship features; they shaped my understanding of what “done” really means. They showed me that sustainable pace isn’t a calendar problem; it’s a craft problem.

I still remember one of my first projects as a newly minted project manager. It was 3 AM. The release was stuck. I sat behind two senior developers as they debugged, rebuilt, and smoke-tested line by line until the system finally deployed. I wasn’t writing code, but I was learning the language — the rhythm, the cause-and-effect thinking, the quiet urgency of engineering.

Years later, during a career break, I finally formalised that fascination and taught myself Python. Not because I planned to change careers, but because I wanted to speak the same language as the people I was supposed to serve.

That decision changed everything. Suddenly, conversations about CI failures, merge conflicts, or test coverage weren’t abstract anymore. They were real, tangible, solvable.

Maybe We Should Be Testing Scrum Masters Too

Developers have coding interviews. Why not Scrum Masters?

A simple technical literacy test could change the game:

Whiteboard Git Flow: Explain how a feature branch merges to develop and then to main.
CI/CD Scenario: Tests pass locally but fail in CI — what do you ask first?
Coverage Report: 80% of tests skipped — what’s your next action?

You don’t need to write production code. But if you can’t run git status, read a test report, or tell what CI/CD is doing, you can’t protect your team’s Definition of Done.

AI as a Wingman, Not a Crutch

Used well, AI can augment a Scrum Master’s insight:

Summarise CI logs or test trends
Generate visual repo diagrams
Highlight branch activity or long-lived PRs
Draft retro questions from commit history

But AI is a lens, not a substitute for judgment. A good Scrum Master still has to decide what matters.

The Cost of Ignorance

When I modelled the cost of these process failures, the math was brutal:

Issue	Estimated Cost (Annual)	Preventable by Technical SM?
Untested compliance logic	R2–10M risk	✅ Yes
Feature merge delays	30% velocity loss	✅ Yes
Broken CI illusions	False confidence	✅ Yes
No monitoring	Undetected incidents	✅ Yes

Estimated impact: R10–18M per year.

Cost of a technical Scrum Master: R1M.ROI: ~1000%.

Cheap Scrum Masters are expensive.

The Job Description That Should Exist

Scrum Master (Technical)

You’ll need:

- 3–5 years in software development or testing

- Working knowledge of Git, CI/CD, and basic scripting

- Ability to interpret test coverage and build reports

- Familiarity with XP practices (TDD, refactoring, pair programming)

You’ll do:

- Enforce technical quality gates

- Make technical debt visible

- Translate technical risk into business language

- Protect sustainable pace- Coach developers in disciplined flow.

You don’t have to write code —but you do have to understand what good code looks like.

Closing Thought

The idea that “Scrum Masters don’t need to understand code” is one of the most expensive myths in agile history.

AI can help. Frameworks can guide. But without technical literacy, Scrum Masters are just meeting facilitators watching teams drown in their own branches.

You can’t protect what you don’t understand. So pick up a shell, learn to run pytest, break a repo or two —and join the real conversation about sustainable software.