Imagine your team just delivered a Sprint at a pace that felt almost too good to be true. The code was generated quickly, the documentation was polished, the grammar was perfect, the persona cards looked stunning. And yet, something feels off. Is this real? Did anyone actually verify this?

This is the dilemma facing Scrum Masters across the industry right now. When AI tools enter the development process, everything speeds up. But speed without oversight is not delivery; it is risk in disguise. And managing that risk is squarely within the Scrum Master's domain.
 

When AI tools are used in a development process (for code generation, documentation, UX design, or anything else), the outputs can feel polished on the surface. But fast and polished is not the same as correct, ethical, or safe.

The discomfort many Scrum Masters feel when their teams adopt AI is not irrational. It surfaces a set of critical questions that every AI-using team should be asking:

These aren't paranoid questions. They're governance questions, and they have a home in your team's Definition of Done. More importantly, they are exactly the kinds of questions a Scrum Master should be asking on behalf of the team.

The Scrum Master has always been responsible for upholding the values and practices that make Scrum work: transparency, inspection, and adaptation. In an AI-assisted environment, each of these takes on new weight.

Transparency. As we know, Scrum has always been about transparency. It's one of the three pillar of empiricism. When AI generates code, documentation, or design artefacts, the team must be transparent about which outputs are AI-assisted. Without that visibility, stakeholders cannot make informed decisions, and the Retrospective loses its grounding in reality. The Scrum Master must champion a culture where AI use is declared openly, not hidden behind polished deliverables. People should not be secretive in how they use AI in their work. In fact they need to be transparent about the prompt they use. It's not to micro-manage people, but in fact it is so the whole team can cover each other's back from AI false output.

Accountability. AI does not own its output. The developer who ships AI-generated code is accountable for it. The team that signs off on AI-drafted documentation owns it. The Scrum Master plays a critical role in making this principle explicit and ensuring it is embedded in the team's ways of working. "The AI wrote it" is not a valid excuse for a defect, and the Scrum Master must reinforce that norm consistently.

Meeting the Definition of Done. The Definition of Done is the Scrum Master's most practical tool for governing AI output. As AI becomes part of the workflow, the Scrum Master must work with the team and organisation to ensure the Definition of Done evolves to include the guardrails that AI use demands. A Definition of Done that was written before AI was in the picture is no longer fit for purpose.

 

When a team adopts AI tooling, the Definition of Done doesn't become lighter. It actually needs more rigour, not less. The Scrum Master is best placed to lead this conversation and ensure the necessary additions are agreed, understood, and upheld.
 

Human output verification. A human must review and sign off on any AI-generated artefact. Testers, UX researchers, business analysts, anyone qualified can do this. The Scrum Master ensures this step is not skipped under time pressure.

Check for bias. AI models carry the biases of their training data. Gender, age, and ethnicity bias are real risks, and explicit checks must be part of done. For example, when generating a user persona without specifying demographic details, some AI tools will make assumptions that reflect the dominant patterns in their training data rather than the actual audience you are designing for. That can actively mislead your product decisions. Be explicit in your prompts, and verify the output.

Check for hallucination. AI can confidently state things that are simply wrong. Always verify factual claims, especially in regulated industries.

Check for unethical or illegal output. AI has generated harmful content without human oversight before. There are documented cases of AI tools producing discriminatory, offensive, or legally problematic content when left unchecked. Governance means checking outputs before they go anywhere, every time.

It's not just about the output. The input side matters too. Two things to examine:

The prompt itself. Vague prompts produce vague (or wrong) results. The more explicit the prompt, the closer the output is to your expectation, and the less room there is for hallucination. Sometimes the problem isn't the AI; it's the user's prompt. The Scrum Master can encourage prompt review as a legitimate part of the team's quality process.

The data source and knowledge cutoff. Which model are you using? When was it last trained? If your industry requires up-to-date facts (legal, medical, financial), you need to know whether the model's training data is current enough to be trusted. Using a model with a stale knowledge cutoff in a fact-sensitive industry can get you into serious trouble.

 

Accountability stays with humans. One of the most important principles to establish clearly: the developer is still accountable for the code they ship, even if an AI tool wrote it. "The AI coding assistant wrote it, not me" is not a valid response to a production defect. The human who approved and merged that code owns it. The Scrum Master must make this expectation explicit in team agreements and reinforce it in every Sprint.
 

Here's something powerful that often gets overlooked: the Definition of Done is already a governance mechanism. It's a guardrail that ensures teams do what they're supposed to do before calling something finished. For organisations adopting AI, this makes the Definition of Done one of the most important documents they have.
 

The Scrum Guide states in the increment section: 
 

This means AI governance doesn't need a separate, parallel system. It can live inside the corporate Definition of Done, and by the rules of the Scrum Guide, every team in the organisation must then inherit and follow it.

Companies asking "where do we put AI governance?" already have the answer. Put it in the Definition of Done at the organisational level. Every team that uses AI inherits those checks. That's scalable, embedded governance rather than a bureaucratic overlay. And the Scrum Master is the natural advocate for making this happen.
 

Some organisations are creating entirely new roles to manage AI governance. But in many cases, the capability already exists. The Scrum Master understands quality, accountability, and process integrity. They know how to coach teams through change without resorting to command and control. And they have a direct connection to the Definition of Done.

The goal of AI governance in a Scrum context is not to micromanage or distrust people. It is to cover each other's backs, because AI output is genuinely variable, and teams need a shared mechanism for managing that variability responsibly. That is precisely what good Scrum Masters do.

As AI becomes more deeply embedded in how software is built, the Scrum Master's role does not diminish. It becomes more essential. The speed that AI introduces only increases the need for someone who is focused on doing things properly, not just doing things fast.
 

If you are a Scrum Master wondering where to begin, start with the Definition of Done. Here is a checklist of additions to propose when AI is in use:

Bring this to your next Sprint Retrospective. Make it a team conversation. Then take it to the organisation and advocate for it becoming part of the corporate Definition of Done. That is the Scrum Master's job in the AI world: not to slow things down, but to ensure that what gets delivered is genuinely done.

Adapted from Professional Scrum Master AI Essentials Training: a session on Definition of Done & AI governance in Scrum teams. Original content by Joshua Partogi.