Thanks for the question.

This is one of those situations where a Scrum Master can either become a process police or a coach.

Because on the surface, the problem looks simple:

The team said they would finish something, and they did not.

But if you stay only at that surface level, the conversation quickly becomes unhelpful.

The Product Owner starts thinking, “The team is not serious about its commitments.”

The team starts thinking, “The PO does not understand how complex this work is.”

Leadership starts thinking, “Maybe this team is not performing.”

And the Scrum Master gets pulled into the middle, trying to defend the team while also acknowledging that, yes, the problem is real.

This is exactly the moment where your coaching stance matters.

Repeated carryover is a delivery problem. It is ALSO a signal, “Something about the way we plan, refine, slice, collaborate, or communicate is not working.”

The Scrum Master’s job is not to protect the team from accountability. It is also not to allow accountability to become blame.

The job is to help the team and stakeholders inspect the practices being followed honestly, learn from them, and make the next Sprint a little better than the last one.

Let’s look at where the real issues may be hiding.

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It sounds like a simple question, but it exposes a more serious dilemma about the Scrum Master role.

• Some people see the Scrum Master as a facilitator

• Some see them as a process curator

• Some expect them to be an Agile expert

• And some believe they should only ask powerful questions and never give answers

So where does coaching really fit?

Let’s find out.

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The short answer

Scrum Masters do coach, but they are not only coaches.

They coach:

• the Developers on self-management, collaboration, and continuous improvement;

• the Product Owner on product thinking, backlog management, stakeholder collaboration, and value delivery;

• the organization on process adoption, empirical thinking, removing impediments, and creating an environment where teams can succeed.

However, Scrum Masters also need to use other “stances” when coaching is not enough.

A Scrum Master may need to:

• teach Scrum when people do not understand it;

• mentor a new Product Owner or Scrum Master;

• facilitate events and conversations;

• advise leaders on organizational impediments;

• challenge behaviours that reduce transparency or accountability;

• support the team in resolving conflicts;

• help protect the team from harmful interference;

• help the organization inspect and adapt.

So the question is not simply, “Do Scrum Masters coach?”

The question is:

“When should a Scrum Master coach, and when should they use another stance to help the team?”

Why does the dilemma exist?

The dilemma exists because coaching can be misunderstood in two opposite ways.

1. “A Scrum Master is not a coach; they are just there to ensure Scrum happens.”

This view reduces the Scrum Master to a process administrator.

In this mindset, the Scrum Master:

• schedules events;

• manages Jira boards;

• reminds people about events;

• tracks action items;

• asks for updates;

• reports progress;

• removes small blockers.

This is a narrow and weak interpretation of the role.

A Scrum Master is not just a Scrum event coordinator. They help people and organizations become more Agile through the Scrum framework.

That requires coaching, facilitation, teaching, mentoring, and organizational change work, etc.

2. “A Scrum Master should only coach and never tell, teach, or advise.”

This is the opposite misunderstanding.

Some Scrum Masters interpret coaching as:

• never giving direction;

• never offering suggestions;

• never correcting misunderstandings;

• never teaching Scrum;

• never challenging bad practices;

• only asking open-ended questions.

This can also be ineffective.

For example, if a team does not understand the purpose of the Sprint Goal, it may not be helpful to only ask:

“What do you think the Sprint Goal is for?”

At some point, the Scrum Master may need to teach the purpose of a Sprint Goal, give examples, and then coach the team on how to create better ones.

1. Pure coaching is powerful when the person or team has enough awareness, knowledge, and ownership to discover their own solution

2. But when there is a knowledge gap, a misunderstanding of Scrum, or an organizational constraint, other stances may be more useful

What does “coaching” mean for a Scrum Master?

For a Scrum Master, coaching is the act of helping individuals, teams, and organizations improve their own capability to think, decide, collaborate, and adapt.

In short, they coach them to be Agile (a word no one likes to use anymore).

It is less about giving answers and more about helping people:

• see reality more clearly;

• inspect their assumptions;

• understand the impact of their behaviour;

• discover options;

• make decisions;

• take ownership;

• learn from outcomes;

• improve continuously

A Scrum Master coaches by creating awareness and enabling better choices.

For example, instead of saying:

“Your Daily Scrum is too much like a status meeting.”

They coach by saying:

“What is the team trying to achieve in the Daily Scrum?”

“Who is the conversation for?”

“How does the current conversation help the team inspect progress toward the Sprint Goal?”

The Scrum Master is not taking ownership away from the team. They are helping the team inspect and adapt.

Coaching is different from teaching, mentoring, and facilitating

One reason this becomes confusing is that Scrum Masters often use multiple stances on the same day.

#1 Coaching

Coaching helps people discover their own insights and solutions.

Important: Coaching is useful when the team has the “ability” to solve the problem but needs reflection, awareness, or courage. It is NOT useful when the team can’t solve the problem on their own.

#2: Teaching

Teaching is useful when there is a knowledge gap.

Important: Teaching is appropriate when people do not yet understand the framework, concepts, or principles.

#3: Mentoring

Mentoring is sharing experience to help someone grow.

Important: Mentoring is useful when the Scrum Master has relevant experience, AND the other person is open to learning from it.

#4: Facilitating

Facilitation helps a group have an effective conversation or decision-making process.

Important: Facilitation is essential to help teams navigate VUCA

#5: Advising or consulting

Sometimes the Scrum Master provides direct advice, especially to leaders or the organization.

Important: You gotta do what you gotta do. What you need here is COURAGE.

The Scrum Master as a coach to the team

Scrum Masters coach the Scrum team toward self-management and continuous improvement.

You are not the team’s manager. It means the Scrum Master helps the team own more of its work system.

A strong Scrum Master helps the team move from dependence to ownership.

The goal is for the team to become increasingly capable.

The Scrum Master as a coach to the Product Owner

Scrum Masters also coach Product Owners.

This is important because many Scrum challenges are, in fact, product ownership challenges.

For example, if the Product Owner comes to Sprint Planning with a list of unrelated user stories, the Scrum Master may coach with questions like:

“What is the product outcome we want this Sprint to move toward?”

“How do these items connect to the Product Goal?”

“What trade-off are we making if everything is priority one?”

“What feedback do we need from stakeholders before the next Sprint?”

Here, the Scrum Master is helping the Product Owner become more effective, not taking over product decisions.

The Scrum Master as a coach to the organization

This is often the most neglected part.

Scrum Masters do not only serve the Scrum Team. They also serve the larger organization.

They coach the organization to understand that Scrum is not just a team-level process. “It exposes” organizational problems.

A Scrum Master coaches leaders and stakeholders by making these issues visible and helping the organization inspect and adapt.

This may involve uncomfortable conversations.

For example:

“The team is being asked to commit to a Sprint Goal, but new urgent work is being inserted every few days. What do we want the team to optimize for: focus or responsiveness?

This is coaching at the system level.

Coaching does not mean being passive

One common anti-pattern is the passive Scrum Master.

They say:

“The team is self-managing, so I should not interfere.”

But self-management does not mean abandonment.

A Scrum Master should not control the team, but they should actively help the team see and improve its system.

Coaching is not silence, avoidance, or politeness.

Scrum Master must lovingly but firmly challenge the team.

The maturity of the team matters

The Scrum Master’s coaching stance should evolve with the team.

New team

A new team may need more teaching, structure, and facilitation.

The Scrum Master may explain Scrum events, clarify accountabilities, introduce working agreements, and help people understand the basics.

Growing team

A growing team may need more coaching and mentoring.

The Scrum Master can begin asking more reflective questions, helping the team own decisions and improve collaboration.

Mature team

A mature team may need more systemic coaching.

The Scrum Master may focus less on event mechanics and more on

• organizational impediments,

• product flow,

• stakeholder alignment, and

• continuous improvement

The same Scrum Master behaviour that helps a new team may limit a mature team.

For example, heavily facilitating every event may be useful early on, but over time, it may prevent the team from taking ownership.

To balance it all, you need Stance Agility

Effective Scrum Masters need stance agility.

They should be able to move between stances intentionally:

Choose the right stance for the moment.

My observation:

• Some Scrum Masters over-teach because coaching feels slow

• Some over-coach because direct teaching feels uncomfortable

• Some over-facilitate because conflict feels risky

• Some over-help because they want to be needed

What you need is to develop a “range.”

Example #1: Daily Scrum becomes a status meeting

A weak response:

A coaching response:

A teaching response may also be useful:

The Scrum Master may need both teaching and coaching.

Example #2: Product Owner brings too many priorities

A coaching response:

A mentoring response:

An advising response:

Example #3: Team does not improve after Retrospectives

A coaching response:

A facilitation response:

Example #4: Manager assigns work directly to team members

A coaching response to the manager:

A teaching/advising response:

A facilitative response:

Bottomline

So, do Scrum Masters coach?

Yes. Absolutely.

But the more honest answer is this:

Scrum Masters coach in service of self-management, empiricism, and continuous improvement, not as a performance of the role, but as one of several stances they use to help teams and organizations become more effective.

• A Scrum Master is not a meeting facilitator

• A Scrum Master is not a process police

• A Scrum Master is not only a professional coach

A Scrum Master is a change agent who uses coaching, teaching, mentoring, facilitation, and direct challenge in whatever combination the team and organization need to help people get consistently better at delivering value together.

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Further Reading

1. Agile Coaching Lifecycle

2. How to Measure the Effectiveness of Agile Coaching?

3. Team Trust Coaching Cards

Connect With Me

• Connect on LinkedIn (I publish every Monday, Tuesday, Wednesday, Thursday and Friday)

• Connect on Community Chat

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Winning Strategy provides insights from my experiences at Twitter, Amazon, and my current role as an Executive Product Coach at one of North America’s largest banks.

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Here’s how I used to run retrospectives when I was a Scrum Master.

I used to go around the room, one person at a time, thinking that equal airtime meant equal participation.

Round-robin format. Everyone gets a turn.

There were two people on the team who never had anything to share.

My question: “Priya, you’ve been quiet. What do you think?”

Their reply: “No, all good. I agree with what was said.”

Let me be clear.

Round-robins are good.

But!

They create compliance. And compliance is not the same as candour.

Like most facilitators, I believed that if everyone got a turn to speak, the retro was fair. After years of coaching teams and sitting in on hundreds of retrospectives, I learned that:

Quiet people don’t need more turns to speak. They need fewer reasons to stay silent.

In this post, I’ll show you how to run a retro where quiet people contribute without being put on the spot… without round-robins.

Let’s get started.

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I genuinely believed that showing executives I knew Agile would earn their respect.

Then, in a meeting after a quarterly review, one of the SVPs pulled me aside and asked:

“I still don’t know what you actually do.”

As a new Scrum Master, I was quite impulsive, so I replied:

“I need to explain the framework better. I need to help the team understand Agile.”

Now… I don’t think I need to tell you what happened next.

I didn’t get the next question. The executive dropped the conversation and walked away.

That’s why I cringe now when I see new Scrum Masters walking into executive meetings armed with Agile vocabulary and walking out wondering why no one takes them seriously.

Let me be clear.

Knowing your Scrum matters.

But!

Agile fluency is not the same as executive credibility. And confusing the two is one of the most expensive mistakes a new Scrum Master can make.

After years of working alongside executives across banking and tech, I learned that credibility in this (or “any”) role is not granted. It is built through a specific set of behaviours that have almost nothing to do with how well you know the Scrum Guide.

In this post, I’ll share 7 such behaviours.

Because if someone who once bored executives into silence can learn to become the person they actually rely on, so can you.

Let’s get started.

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I used to think the Daily Standup was the one thing that didn’t need to change when a team adopted AI.

I was so very WRONG!

When a Scrum team adopts AI in its workflow, the Daily Standup SHOULD ALSO evolve.

The purpose of the Daily Scrum remains the same:

To help the team inspect progress toward the Sprint Goal and adapt the plan for the next 24 hours.

But since AI changed, how work is done, where risks appear, and what the team needs to discuss, it has introduced some new risks, such as:

• low-quality generated output,

• hidden assumptions,

• security concerns,

• prompt dependency,

• duplicated work, and

• reduced shared understanding.

So…

When this is the situation… how do you guide your teams so the Daily Standup remains focused, transparent, and outcome-oriented?

Let’s find out.

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I’ve been on both sides of this conversation.

I’ve been the person trying to diplomatically explain that the whole system is broken. And I’ve been the person hearing that feedback, mentally preparing my defence before the other person finishes their sentence.

Believe me… neither side is fun.

As a Scrum Master, you have the exact same problem.

You are expected to “diagnose” but criticized for your “diagnosis.”

Stories are not ready. Sprint work spills over. Priorities change mid-sprint. Developers wait for answers that don’t come. Stakeholders bypass the Product Owner entirely.

From where you are sitting, the diagnosis of the above problems is obvious:

The system around product decision-making is broken.

But the moment you say “this is a systems problem” out loud, you risk damaging your relationship with the Product Owner if you’re not careful in your delivery.

So, how do we fix the delivery?

Let’s find out.

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“Not everything that counts can be counted, and not everything that can be counted counts.”
— Albert Einstein

Agile teams often have no shortage of metrics.

The problem is not the lack of data. Agie teams have a lot of it.

The problem is that they have too much disconnected data, which means they don’t have a shared understanding of what that data is supposed to help them decide.

This is the situation where metrics are discussed without changing anything. Everyone can point to numbers, but nobody is sure what they mean or whether they matter.

That is metrics overwhelm.

And it is dangerous because it makes a team “appear” data-informed without helping the team make better decisions.

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If you’re new, then you missed the posts below in the past few weeks

1. The Retrospective that never lead to action

2. How to deal with part time team members with split allocations

3. 11 things Atlassian Rovo AI can't do yet

4. How to define TRULY URGENT mid sprint interruptions?

5. 9 built in ROVO AI Agents for Scrum Masters

6. How would I help my team resolve impediments without depending on me?

Here’s what “metrics overwhelm” looks like

You may recognize metrics overwhelm when conversations sound like this:

“Our velocity is down, but our throughput is up. Is that good or bad?”

“The dashboard says we’re behind, but the team says things are fine.”

“Leadership wants more predictability, so we added more metrics.”

“We measure customer adoption, but nobody knows what adoption level would count as success.”

“We report sprint completion every two weeks, but the product decisions are still based on stakeholder pressure.”

“We have all the data in Jira, Productboard, GA, Amplitude, Power BI, and spreadsheets, but we still don’t know what to prioritize.”

The team collects metrics at three different levels, but mixes them together:

1. Delivery metrics: Are we delivering work smoothly?

2. Product metrics: Are users benefiting from what we deliver?

3. Business metrics: Is the organization getting the result it needs?

Each category is useful. But confusion begins when people expect one type of metric to answer another type of question.

Velocity cannot tell you whether customers value the feature. NPS cannot tell you whether the team has too much work in progress. Revenue cannot tell you whether your refinement process is healthy. Cycle time cannot tell you whether you are solving the right problem.

When the metric is used to answer the wrong question, the conversation becomes confusing.

Why does metric overwhelm happen?

The simple answer is UNCERTAINTY.

Metrics overwhelm usually happens because people are trying to reduce uncertainty.

Product work is uncertain. Agile delivery is uncertain. Stakeholder expectations are uncertain. Customer behaviour is uncertain. Technical complexity is uncertain.

So teams and leaders reach for measurement to build confidence.

The problem is that metrics are often added one by one, without removing old ones and without clarifying their purpose.

None of these is bad on its own. In fact, most are reasonable.

But together, without a decision framework, they become cognitive clutter.

The team ends up with a pile of signals but no way to separate what is important from what is merely available.

What’s a useful metric?

A useful metric should help someone make a better decision.

If a metric does not influence a decision or trigger a conversation, or guide an experiment, it is probably not a useful metric.

One of the questions you can ask your team is:

“What decision will this metric help us make?”

If nobody can answer, pause before adding it to the dashboard.

For example:

The metric itself is not the insight.

Insight comes from interpreting the metric in context and deciding what to do next.

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How Product Owners get overwhelmed by metrics

For Product Owners, metrics overwhelm often shows up as pressure to prove progress.

Stakeholders want to know:

In response, the Product Owner collects more and more data to defend decisions.

But a Product Owner’s job is not to produce enough metrics to satisfy everyone’s curiosity.

Their job is to create enough CLARITY to support better product decisions. That means the Product Owner needs to distinguish between:

The above metrics are not the same.

A roadmap confidence metric may help stakeholders understand uncertainty.

A feature usage metric may help the Product Owner decide whether to iterate.

A cost-of-delay discussion may help prioritize backlog items.

A cycle time metric may help the team improve flow.

A business outcome metric may help evaluate whether the product strategy is working.

When all of these are thrown into a single dashboard without a clear intent, the Product Owner becomes a curator of confusion.

How Scrum Masters get overwhelmed by metrics

For Scrum Masters, the danger is slightly different.

Scrum Masters often care deeply about team health, flow, continuous improvement, and transparency.

Metrics can be very helpful here. But they can also become a subtle form of micro management if not handled properly.

The above metrics can support excellent conversations.

But if the team feels the numbers are being used to judge them, they will adapt their behaviour to protect themselves.

Metrics should help the team see the system more clearly, not make individuals feel watched.

So how do we handle this?

The best way to escape Metrics Overwhelm is by clarifying the decisions first.

Start by asking:

“What decisions do we need to make on a regular basis?”

Then, for each decision, identify the smallest set of metrics that can inform it.

For example:

This approach prevents the collection of metrics that serve no purpose.

Use a “metrics menu,” not a metrics buffet

If you have ever had a buffet at a restaurant, you know that buffets encourage people to pile everything onto the plate.

A menu forces choice.

Agile teams benefit from a small, intentional set of active metrics, which may change depending on the team’s focus.

Below are a few examples of such a metric menu:

#1: If the team is struggling with the delivery flow

Those may still matter, but they are not the immediate requirement for improvement.

#2: If the team is validating a new product idea

The goal is learning, not maximizing output.

#3: If the team is improving quality

The goal is to restore confidence and reduce the demand for failure.

#4: If the team is scaling stakeholder trust

The goal is better alignment and expectation management.

Separate health metrics from target metrics

One distinction is between target metrics and health metrics.

Target metrics

These represent what you are actively trying to improve.

Examples:

• Increase activation

• Reduce onboarding drop-off

• Improve cycle time

• Reduce escaped defects

• Increase trial-to-paid conversion

• Reduce customer churn

• Improve forecast reliability

These metrics are central to business decisions.

Health metrics

These are things you monitor to make sure improvement in one area is not damaging another.

Examples:

• Team morale

• Defect rate

• Customer complaints

• System performance

• Support volume

• Work in progress

• Technical debt indicators

For instance, if the target is to increase feature delivery speed, quality and team health should become health metrics. Otherwise, the team may “succeed” by burning people out.

If the goal is to reduce defects, delivery throughput may become a health metric; otherwise, quality improvement could lead to paralysis.

This distinction helps reduce overwhelm because not every metric gets equal attention.

Some metrics are steering the current change.

Others are guardrails.

How to turn metric data into insight

Data becomes insight when it changes understanding.

Here’s how the progression happens:

1. Data: “Cycle time increased from 4 days to 9 days.”

2. Information: “The increase is mostly in review and testing.”

3. Insight: “Stories are reaching review before acceptance criteria are clear, causing rework.”

4. Action: “We will improve refinement and add earlier QA/product review.”

5. Learning: “If review wait time drops, we know the change helped.”

Many teams stop at step 1 or 2.

They display the data and assume the insight is obvious.

It usually is not.

Here, Scrum Masters can add huge value by facilitating the movement from number to meaning to action.

Qualitative insights

Metrics tell you what is happening.

They often cannot tell you why it is happening.

For instance, if activation is low, analytics may show where users drop off. But they may not explain whether users are confused, unconvinced, distracted, lack permissions, are using a workaround, or are solving a different problem entirely.

If the velocity drops, Jira may show fewer story points completed. But it may not explain whether the issue is unclear priorities, technical debt, dependencies, interruptions, illness, low morale, or overcommitment.

This is why metrics should be paired with qualitative learning:

You don’t have to choose between data and conversation. Use data to focus the conversation.

Metric Purpose Statement

For each metric you keep, define a short purpose statement.

Use this format:

We measure [metric] to understand [question] so that we can decide [decision/action].

Examples:

We measure cycle time to understand where work slows down so that we can improve flow and reduce unnecessary waiting.

We measure feature adoption to understand whether users are trying the new capability so that we can decide whether to improve, promote, or stop investing in it.

We measure escaped defects to understand where quality issues reach customers so that we can strengthen our feedback and testing practices.

We measure team sentiment to understand whether our way of working is sustainable so that we can address overload before it becomes burnout.

We measure activation rate to understand whether new users reach first value so that we can improve onboarding and early product experience.

If you cannot complete the statement, the metric probably does not belong in the active dashboard.

How to clean the current metrics you have

Conduct a workshop with the Product Owner, Scrum Master, Developers, and relevant stakeholders.

Step 1: List your current metrics

Collect every recurring metric the team reports, reviews, or tracks.

Do not debate yet. Just list them.

Step 2: Mark the audience

For each metric, identify who it is for:

• Team

• Product Owner

• Scrum Master

• Stakeholders

• Leadership

• Customers

• Operations

• Other teams

Step 3: Identify the decision

Ask:

“What decision does this metric support?”

If the answer is unclear, mark it.

Step 4: Identify the action threshold

Ask:

“When would this metric cause us to act?”

For example:

• If the cycle time increases for three consecutive weeks, we investigate blockers

• If adoption is below target after launch, we run customer interviews

• If the number of escaped defects increases, we review testing and release practices

• If WIP exceeds the agreed limit, we stop starting and start finishing

Step 5: Remove, archive, or downgrade

Not every metric needs to be deleted.

Some can be:

• If a metric helps us make a current decision, keep it active (Eg: Cycle Time)

• If it protects us from side effects, make it a guardrail (Eg. Escaped defects)

• If it helps us investigate a problem, keep it diagnostic (Eg. Time spent in each workflow status)

• If it has historical value, archive it (Eg. Adoption of last quarter’s reporting feature)

• If nobody uses it, remove it (Eg. Number of backlog items created)

This gives the team permission to stop carrying old measurement habits forever.

The honest truth about Metrics

Data doesn’t create clarity on its own. Clarity comes from a team that has agreed on what they’re trying to achieve, identified the signals that show whether they’re getting there, and built the habit of talking honestly about what those signals mean.

Metrics are a language.

And like any language, they only communicate something when both parties understand the words.

Further Reading

1. How to Discuss Metrics With Your Team?

2. Metrics for Project Based Scrum Teams

3. How Agile Metrics are Getting Misused - And How Can You Fix It?

Connect With Me

• Connect on LinkedIn (I publish every Monday, Tuesday, Wednesday, Thursday and Friday)

• Connect on Community Chat

• Connect on YouTube

Winning Strategy provides insights from my experiences at Twitter, Amazon, and my current role as an Executive Product Coach at one of North America’s largest banks.

Leave a comment

We gather around the whiteboard, write the same complaints on the sticky notes, nod, and then... go right back to work on Monday as if nothing happened.

It’s a tricky situation, but it is completely reversible.

Today, we are going to unpack exactly how my teams fixed it and how you can help your team fix it as well.

Let’s get started

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On paper, such team members are part of the team. But in reality, they exist in a kind of permanent limbo: half here, half somewhere else.

This can be a UX designer who supports three teams simultaneously. This can also be a senior developer who is officially 50% on your team.

It’s not their fault, though.

They're usually trying their hardest to serve multiple teams while knowing, deep down, that they can't fully satisfy any of them.

The fault, if we're assigning it, sits somewhere in the organizational structure: the hiring problems, the company’s budget just can’t be stretched problems, etc.

In this post, I want to walk you through:

• what's actually happening beneath the surface when you have split-allocation members on your team

• why the standard Agile instinct to complain won’t work, and

• what successful teams are doing to make it work in this situation

The goal of this post is to form a team that delivers with whatever setup it actually has.

Let’s get started.

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But if you’ve used AI for more than a week in the trenches, you know that the demos always look good, and they are nothing like reality. When AI meets a legacy Jira instance, things break.

Rovo is powerful, but it has distinct boundaries.

In this post, I discuss 11 such boundaries of Atlassian’s Rovo AI and how my teams have navigated around them.

Let’s get started.

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What kind of posts do you want to see on Winning Strategy?

In order to provide you with maximum value out of this publication, please use the above poll and send me your interest.

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I want to start with something I hear constantly when I talk to Scrum Masters and product teams about mid-sprint interruptions.

They describe the problem as a process problem by saying:

“We need a better intake system.”

“We need stakeholders to stop bypassing the backlog.”

“We need leadership to respect the sprint.”

Those things might be true but…

I’ve come to believe the deeper problem is almost always a failure to build shared understanding about the outcome the team is trying to protect and what it genuinely costs to interrupt the team.

When teams have that shared understanding (something that they can clearly show the stakeholders), urgency becomes a tractable question.

When they don’t, it becomes a power struggle. And power struggles are exhausting… believe me!

In this post, we will learn how YOU can help your team build that shared understanding.

Let’s get started.

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I’ve been collecting notes on those teams on what they actually use, the exact queries they type, and where they’ve hit roadblocks.

This post is that list.

Here’s what I am hoping you learn from this post:

1. What ROVO’s out-of-the-box agents can do for a Scrum Master

2. Examples of how people are using those agents

There’s a lot to cover, so let’s get started.

If you are new to Winning Strategy, here’s what you missed in the past few weeks

1. How to write Acceptance Criteria using AI

2. 13 AI prompts your Product Owner needs

3. How the POs in my teams use AI to turn vague complaints Into Backlog Items.

4. We created guardrails for AI assisted backlog refinement

5. AI Summaries for Leadership

6. How to prioritize defects when everything Is critical?

Got an urgent question?

Get a quick answer by joining the subscriber chat below.

Which ROVO Agents are Scrum Masters successfully using?

They are using ROVO to handle repetitive, administrative tasks. The list below is based on what's genuinely in use across the team.

These are out-of-the-box ROVO agents.

So if your team is on Atlassian Cloud with ROVO enabled, you have access to all of them today.

#1. Backlog Refinement: Theme Analyzer

This is being used to scan the backlog and then group issues into recurring themes.

You open it in Rovo Chat and describe what you want in plain language:

• point it at a project,

• paste in a search results page link, or

• give it a JQL query if you want to narrow the scope

The agent handles the rest.

The simplest way to start using it is with the built-in conversation starter prompt below:

“Help me group the issues in project X into themes.”

It will ask clarifying questions if it needs more context.

Scrum Masters are using this agent to map what stories have been piling up and have not been worked on.

One thing to keep in mind:

This agent was built primarily for analyzing customer feedback and support tickets at scale. Scrum Masters are applying this to backlog refinement, and it works well for that, but the output reflects what’s written in the tickets.

Incomplete descriptions and unclear titles lead to incorrect groupings.

#2. Backlog Refinement: Work Item Organizer

This out-of-the-box agent is being used to handle the backlog cleanup.

Things like

• moving items into sprints,

• linking loose tickets to epics,

• flagging old written issues that haven't been touched in months

It's typically run as a pre-refinement tidy-up.

Like the Theme Analyzer agent, you interact with it conversationally in Rovo Chat. You describe what you need, it proposes the actions, and you confirm your decision before anything changes.

Queries Scrum Masters are using:

• “Find all backlog items not linked to an epic and suggest which epics they belong to”

• “Identify issues in the backlog that haven’t been updated in over 90 days”

• “Move all story-type items estimated under 5 points into the next sprint”

Two things you must know before you use it:

First: bulk actions are capped at 20 work items at a time. If you ask it to move or update a large batch of issues, it will stop at 20. On an active backlog, this limit comes up quickly, so expect to run it in multiple passes rather than in a single sweep.

Second: the agent proposes actions and waits for your confirmation. It doesn’t make changes without your approval. That’s useful to know before you set expectations with the team about what it can do automatically.

#3. Sprint Planning: Readiness Checker

Once configured, the Readiness Checker runs automatically when a new ticket is created.

It checks whether the ticket has:

• acceptance criteria,

• a definition of done, and

• enough context to actually be planned

If something's missing, it adds a comment to the issue flagging what's needed before they reach planning.

You can use it either:

1. manually through Rovo Chat (you open the agent, point it at a ticket, and get feedback immediately) or

2. automatically via a Jira automation rule

The automation approach is more scalable, but Scrum Masters also use it manually to spot-check individual tickets before planning.

Note: We will discuss each agent in more detail in future posts.

#4. Sprint Planning: Breaking down work

ROVO chat

Through Rovo Chat, you can take a large Epic and ask ROVO to break it into smaller, more actionable User Stories.

It pulls context from linked Confluence pages, linked issues, and any attached documents to inform the breakdown. We have a Confluence page set up with WAHZUR and T-Split techniques that it usually refers to.

Queries Scrum Masters are typing:

• “Break this epic into individual user stories with acceptance criteria.”

• “This ticket is too large for one sprint. Suggest how to split it into smaller pieces.”

• “Draft a description and acceptance criteria for this ticket based on the Confluence spec linked to it.”

Work Item Planner

This out-of-the-box agent’s main use is to break large items into actionable steps.

Note: “Work item Planner” is separate from “Work item Organizer.”

The Organizer tidies up what already exists, the Planner creates what doesn't yet exist.

You can use “Work item Planner” in two ways:

The first is directly inside Jira, without going through chat at all.

1. Open an epic, go to the child work items area, select Add, then Suggest

2. Rovo generates a list of proposed child items based on the epic's description

3. You review them, remove what doesn't fit, and create the ones you want. This is the simpler starting point for most teams

The second is through the Work Item Planner agent in Rovo Chat. The agent's built-in conversation starters are:

• "Break down my epic into subtasks" and

• "Turn this page into a list of tasks."

One thing that applies to both methods: the quality of the output depends directly on what's written in the epic description.

Rovo only reads what you give it. If you want to use a Confluence page with instructions, you need to link that page in the epic description first. It doesn't automatically scan linked issues or attached documents.

#5. Retrospectives: Transcript Insights Reporter

The Transcript Insights Reporter is a general transcript analysis agent.

It was built primarily for customer interviews and user research transcripts. Scrum Masters are applying it to retrospective meetings, and it works for that purpose, but it is important to know that this agent wasn't purpose-built for retros.

Here’s the workflow:

1. After the retro, you open the agent in Rovo Chat, paste in the transcript (if you have one recorded), and ask it to identify themes and suggest next steps

2. It returns a structured text response (1) with themes from the discussion, observations, and (2) recommended actions

3. That output then needs to be reviewed and turned into Jira tickets manually, or via a follow-up prompt.

4. The agent surfaces the analysis; it doesn't create tickets on its own

The agent’s built-in conversation starter is:

“Extract insights from this transcript and turn them into interview notes.”

For retros, Scrum Masters use the following prompts:

• “Here is the transcript from our sprint retrospective. Identify the main themes and suggest action items for each”

• “What were the main concerns raised in this meeting, and what actions did the team agree to?”

One thing worth knowing:

There is a separate agent called the Meeting Insights Reporter, launched more recently.

That one doesn't work from a pasted transcript. It searches meeting notes and transcripts already saved in Confluence via Loom. If your team doesn't use Loom, this agent isn't available to you. The Transcript Insights Reporter is the one that works with a transcript you paste in directly.

Also…

The silent retro caveat applies here:

If your retrospective runs mostly on written stickies and dot voting without verbal discussion, there's no transcript to work with.

The Transcript Insights Reporter agent is useful for teams where the retro is conversation-based, and someone is capturing or recording it.

#6. Blocker Detection: Blocker Checker

Blocker Checker is an automation agent.

It is designed for use inside a Jira automation rule. It doesn't run on its own, and it's not something you interact with through chat.

Here's how they have it set up:

1. You build a Jira automation rule with three parts: a trigger, the Blocker Checker agent, and a follow-up action

2. The trigger watches for a condition, typically a comment added to an issue containing language that suggests the work is stuck

3. The rule calls the Blocker Checker, which reads the context and evaluates whether the issue is blocked

4. A second action in the rule then uses the agent's response to transition the issue status to Blocked

The agent signals; the automation executes.

This means the setup requires more than just enabling the agent. You need to:

1. configure the automation rule,

2. write the trigger conditions,

3. connect the agent, and

4. add the status-change action

Example of how it runs:

Let’s say a developer comments at 2 pm that they're waiting on sign-off from another team. The rule fires, and the issue status changes to Blocked the same afternoon. The Scrum Master sees it on the board without waiting for the next standup.

That difference in response time (same day versus next morning) is where this automation is useful, particularly for teams where impediments tend to surface late.

#7. Sprint Reporting

There is no dedicated out-of-the-box agent for sprint reporting. What Scrum Masters are doing here falls into two approaches.

1. First is using Rovo Chat directly for end-of-sprint summaries

You ask it to pull data from the completed sprint and return a structured summary:

• completed items,

• carryover,

• current blockers, etc.

For single-sprint summaries, this works reasonably well, though the quality varies depending on how consistently the team has been maintaining their Jira data.

Prompts that work:

• “Summarize Sprint 12 results: how many points were planned, how many were completed, and what carried over”

• “List all incomplete items from the last sprint and their current status”

• "Generate a sprint report for stakeholders covering blockers and carryover items"

Avoid this:

Avoid asking Rovo for velocity. Velocity requires comparing completed work across multiple sprints, and as covered in the limitations section below, cross-sprint reasoning is where Rovo produces unreliable results. Velocity is better pulled from Jira's built-in Velocity Report than asked of Rovo Chat.

2. Second approach

This is used by Scrum Masters who need consistent output every sprint. They are building a custom agent in Rovo Studio specifically for sprint reporting.

You define what the agent should pull, how to structure the output, and connect it to the relevant Jira project. It takes some setup, but once configured, it produces the same format each sprint without requiring a new prompt each time.

Note: We will learn how to build these in future posts.

#8. Finding issues without writing JQL

Scrum Masters who don't write JQL regularly use ROVO Chat to convert plain-language questions into Jira queries and run them.

Queries Scrum Masters are typing:

• “Show me all bugs assigned to the backend team that have been in progress for more than five days”

• “Find all tickets with no assignee that were moved into the current sprint”

• “List all issues flagged as high priority that haven’t been updated in the last week”

#9. Cross-Team Dependencies: Deep Research

In environments where multiple teams share dependencies, ROVO's Deep Research mode helps SMs identify cross-team dependencies.

Deep Research scans across:

• projects,

• Confluence pages and

• connected tools

and compiles a report on the current status and dependencies. Before using it, there are two things to know:

1. It takes up to 15 minutes. This is not a quick process

2. It will first ask you clarifying questions. The current version of Deep Research doesn't run immediately on submission. It first asks targeted questions to confirm the scope

Prompts used:

• “Research the current status of shared dependencies between Team A and Team B for the Q4 release”

• “Summarize progress on the platform migration across all teams involved”

• "What Jira issues are blocking work across more than one team right now?"

One limitation:

Deep Research only surfaces what was recorded.

If dependencies between teams exist but were never documented in Jira or Confluence, tracked informally, discussed in meetings that weren't captured, or managed through direct messages, they won't appear in the report. The output reflects the quality of what your teams have been writing down.

Bottom line on ROVO output quality

Before wrapping up, there is one thing that cuts across every agent on this list.

Rovo works with what’s in the system.

The Theme Analyzer groups what’s written in your tickets. The Readiness Checker evaluates what’s been filled in. Deep Research synthesizes what’s been documented. Every one of these agents is only as useful as the data behind it.

The Scrum Masters I work with who get consistent, reliable outputs from Rovo are the ones on teams where Jira is reasonably well-maintained, i.e. tickets have descriptions, epics are linked, and sprints are closed properly.

If your team is earlier in that journey, the agents still work, but you’ll notice the gaps quickly. In some ways, that’s useful information. Rovo makes bad data hygiene visible faster.

Next Post…

In the next post, we will explore what can't be done with ROVO yet and when it makes sense to stop waiting for Atlassian to close the gap and start building CUSTOM AGENTS of your own.

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This week’s recipe will help you put those prompts to work as a team.

You will use this recipe when a story is clearly too big, and you need it ready for the sprint before the next planning session.

Note: It won’t replace your regular backlog refinement.

Let’s get started.

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We have all manually split user stories.

A few years ago, I introduced WAHZUR and T-Split techniques to split large user stories into smaller ones. If you don’t know what they are, watch the video below, where I talk about these two techniques.

Now imagine doing all of that in a fraction of the time, right inside Jira, using Atlassian’s own ROVO AI.

What is ROVO?

ROVO is Atlassian's built-in AI assistant. You access it via the chat icon in the top-right corner of Jira. You can also access it by typing /rovo in any issue description or comment field.

This post walks you through the simplest way to split user stories with ROVO AI utilizing WAHZUR and T-Split techniques.

Let’s get started.

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It is one of those Product activities that everyone knows matters, yet it's routinely underdeveloped or written too late.

Your team has probably already used AI to write acceptance criteria.

You typed in a user story, got a list of conditions back, and pasted it straight into Jira. Fast. Easy. Done.

Here’s the thing, though. This speed comes at a cost.

I have seen that this acceptance criteria is usually vague, with missing edge cases and conditions that can’t actually be verified.

AI can write AC. That part is easy. Writing AC your team can trust is a different story.

Used without a clear process, AI gives you false confidence. The gaps are still there, they're just harder to spot.

In today’s post, I am going to give you a simple 3-phased process my teams use to write acceptance criteria every sprint.

Let’s get started.

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Last Thursday, we learned how the POs use AI to turn vague signals into refinement-ready backlog items.

When this is done right, your PO can start the refinement sessions with:

1. a categorized signal,

2. a one-paragraph problem statement, and

3. a selected user story

But what if it is NOT done right?

Then what?

If you are a Scrum Master, how would you help the PO with this process and ensure it happens consistently?

Before we get into the mechanics of this, let me clarify something:

Your job as a Scrum Master IS NOT to run the AI prompts. Your job is to ensure the prompts are run by the PO and the team on a regular basis.

And to do that, you need to know:

• exactly which prompts exist,

• what each prompt produces, and most importantly

• when to push the PO to use the ones they’re skipping

That’s what this post gives you.

There are 13 prompts in this AI discovery toolkit, organized across various groups.

For each prompt, I’ve added a single pre-refinement question that you can use as a trigger to nudge your PO in the right direction. You don’t need to become a prompt engineer. All you need is to know the right question to ask.

Let’s get started.

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Every refinement session has a tax.

The first 20-30 minutes where the team figures out what they’re actually building, why it matters, and whether it’s even the right thing.

That tax is paid because, usually, the PO walks into the Refinement sessions with a vague signal and not a prepared backlog item.

On Thursday, I showed you how some of the POs I work with have started using AI to turn such vague complaints/signals into refinement-ready backlog items in three steps, before the team ever gathers for the Refinement session.

Today, you get the template and facilitation guide to actually run it.

Let’s get started.

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Here’s a typical refinement session:

Someone complains: "Users are saying they can't find anything in the app. Sales is losing deals over it."

The team comes together, one team member reads the complaint (or a vague requirement, or an idea, etc.) aloud, and for the next 40 minutes, the team tries to figure out:

• WHAT the problem actually is,

• WHOSE problem is it, and

• whether this is even the RIGHT THING to build

The effort is usually parked or rushed because “the thing” is not yet ready for refinement, and other items are lined up in the queue.

This happens because that complaint or requirement is a vague signal, and the work of turning it into a clear backlog item usually falls onto the PO.

Most POs do this in their heads between meetings, which results in a vague complaint becoming a vague user story.

This is now being changed thanks to AI.

Some POs I work with are using AI to quickly and easily turn these kinds of vague ideas into refinement-ready Backlog Items.

Here’s how they are doing it.

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Welcome to the Sunday edition of the Winning Strategy Weekly Recipe.

This past Thursday, we discussed the risks my team encountered when using AI for backlog refinement.

Specifically, we looked at how AI behaves and how it confidently generates acceptance criteria, system behaviours, and trade-offs that look completely plausible but are often fabricated.

I shared the 7 essential guardrails my teams use to ensure the tool accelerates the work without taking decision-making ownership away from the team.

But understanding the guardrails and operationalizing them in an Agile environment are two different things.

To benefit from AI without the rework, we need a system that translates those principles into steps during our live sprint cadence.

This post bridges that gap.

You receive an artifact pack that you can add to your backlog workflow today, enabling your team to use AI for refinements with minimal risk.

Let’s get started.

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YES, we did.

And it went great… for about two weeks.

Then we spotted a problem.

The risk of using AI for refinement is that it sometimes produces bad output that is indistinguishable from good output.

I've spent a lot of time thinking about where AI helps in product discovery and delivery, and where it introduces risk that teams don't see until it's too late. In this post, I share 7 guardrails that are essential for any team using AI for backlog refinement.

I have conducted these tests across various refinement sessions, with multiple teams iteratively enhancing them.

Before I walk through each one, I want to name the one underlying principle that forms the basis of every guardrail.

Here it is. 👇

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