5 Practical Ways Generative AI can help Scrum Masters

As a Scrum Master, you wear many hats – coach, facilitator, impediment remover. But with competing priorities and limited time, some tasks can fall through the cracks.

What if there was a way to lighten your load and supercharge your effectiveness?

In this article, we’ll explore 5 practical ways Generative AI can help you deliver greater value as a Scrum Master.

1️⃣ Generate JQL Queries

As a scrum master, you often need to pull reports and metrics from Jira to track progress, identify blockers, and communicate status to stakeholders.

Writing effective JQL queries takes practice though, and can be time consuming for you to learn. This is where generative AI tools come in handy!

By providing these AI assistants with a few details about what data you need to extract from Jira such as the date range, teams involved, ticket types, statuses etc. They can automatically generate complex JQL queries without needing to understand the syntax details.

You simply provide the parameters in plain language, and the AI generates optimized queries saving you significant research and trial-and-error time.

This allows you to pull the reports you need faster, enabling quicker data-driven decisions and responses for you.

Freeing you up to focus more on facilitation versus losing time writing intricate search queries.

Generative AI is your new best friend as a scrum master when leveraging Jira!

2️⃣ Analyse Data from Scrum team

As a scrum master, analyzing data and metrics from your scrum team can inform many important decisions, but digging through data takes considerable time.

Generative AI tools provide you a faster option. Simply describe to the AI assistant what data you want to examine burndown trends, defect rates, team velocity over time, etc and parameters like date ranges to analyze.

Rather than compiling reports yourself, the AI will rapidly process raw data into digestible visualizations, projections, and insights to reveal team patterns.

You can ask follow up questions about the findings and the AI will explain key takeaways, without you needing data science skills. This allows you to spend more hours in facilitation, coaching, and communication roles to guide the team.

Having quick access to automatically generated data analysis from an AI tool helps you make timely, evidence-based decisions to keep your team running at peak efficiency and productivity.

3️⃣Summarise Meeting notes

As a busy scrum master, you likely spend hours each week in various meetings sprint planning, standups, retrospectives, reviews, etc.

Comprehensive meeting notes are vital for capturing key discussions, decisions, action items and more. However, summarizing pages of detailed notes into concise meeting summaries can devour your limited time.

This is where leveraging generative AI tools saves you major effort. Rather than poring over volumes of notes to draft summaries yourself, you can simply provide the raw meeting notes to your AI assistant.

In seconds, it can scan the content and auto-generate an executive summary highlighting the most critical details, conclusions, tasks and owner assignments. The summary it produces likely will be higher quality than what you’d write manually.

This allows you to distribute comprehensive recaps faster to stakeholders. Generative AI becomes your personal assistant to efficiently handle the time-intensive task of distilling meeting notes down to their essence.

4️⃣ Project capacity planning

Juggling project capacity and resourcing as a scrum master can be challenging. You must factor in team velocities, roadmaps, and changing priorities across multiple sprint cycles.

Rather than creating complex spreadsheets, you can leverage generative AI to automate capacity forecasts to plan better.

Simply provide details like the team size, their velocities per sprint, upcoming project requirements and timelines to the AI tool.

In seconds, it can simulate resource allocation scenarios across existing commitments and propose options to optimize staffing, predict delivery capacity per timeline, and model tradeoffs to guide pace-setting decisions.

The tool handles the variables and risk analysis automatically, without you needing advanced math skills.

This frees up your time for higher-value work. Letting AI rapidly process the complex models and assumptions, while you focus on guiding teams, coaching, and communication.

With AI-powered capacity planning, you can steer multiple agile teams more strategically and with clearer foresight into delivery tradeoffs.

5️⃣ Prioritise Backlog

As a scrum master, one of your most important and time-consuming jobs is helping product owners effectively prioritize the backlog to ensure work is aligned to business goals.

Traditionally, this requires you to facilitate intensive sessions to manually estimate and compare large numbers of complex features and enhancement requests based on vague criteria. Leveraging generative AI radically simplifies and accelerates this task for you.

Instead of endless meetings, you can describe the existing backlog items and strategic priorities to your AI assistant.

It will instantly process all the variables and combinations using advanced decision analytics to provide you with an optimized prioritized backlog order complete with quantitative scoring rationale. The AI can re-prioritize on-demand when new inputs change as well.

This allows you to bypass the tedious manual work of re-assessing the priority sequence each sprint. With AI, you can focus more energy on higher judgement tasks while enabling data-driven backlog prioritization.


Scrum masters wear many hats, but time-consuming manual work like data analysis and documentation leave less bandwidth for strategic priorities.

Fortunately, advanced generative AI tools now offer scrum masters a collaborator to offload tedious tasks.

By translating verbal requests into complex JQL queries, processing raw data into visual insights, distilling meeting notes into summaries, forecasting capacity allocation tradeoffs, and scientifically prioritizing backlogs, AI frees up hours otherwise wasted on overhead.

Empowered with faster access to essential information and rapid scenario modeling powered by AI, scrum masters expand capacity to guide agile teams through higher-value coaching, collaboration facilitation, stakeholder communication, and project governance oversight.

Rather than compete with technology, excellent scrum masters utilize AI to amplify their uniquely human strengths. The future of product delivery leverages this harmonious balance.

Patrick G Avatar

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