At Merapar, we continuously embrace technologies that enable us to work smarter and innovate faster. Artificial Intelligence is playing an increasingly pivotal role in enhancing agile processes, streamlining workflows, and strengthening team collaboration. But what does this look like in practice? In this blog, our colleague Kelvin shares a personal story about how AI is transforming his work as a scrum master. He explains how he leverages AI tools to tackle complex challenges, save time, and better support his team. It’s an inspiring example of how technology and human intuition can work together to deliver outstanding results.

As scrum masters and agility consultants, we’re no strangers to the challenges of balancing team support, stakeholder communication, and process optimisation. Over the past few months, I’ve found myself increasingly relying on AI to tackle these challenges, and it’s been nothing short of transformative. From streamlining reporting to enabling data-driven decision-making, AI is reshaping how I approach my role. 

My challenge: SIT at scale

Recently, I supported our client’s project team with a complex joint System Integration Testing (SIT) phase, with their customer. The project involved over 120 test cases, multiple stakeholder groups, and a tight deadline. The team was spread across various time zones, and progress updates needed to be shared daily with senior leadership. We faced several common challenges:

1. Tracking progress across test cases in real-time, which became increasingly difficult as the number of failed or "on hold" cases grew.
2. Forecasting whether we could meet deadlines, given varying rates of completion.
3. Communicating insights quickly to leadership in a way that didn’t overwhelm them with unnecessary detail.

The usual approach, manually updating trackers, compiling reports, and crunching numbers in spreadsheets, was simply too time-consuming. I asked myself, “how can I efficiently automate these repeatable tasks?"

Solution: Turning data into actionable insights

This is where AI stepped in. Using AI tools, I was able to:

1. Automate Progress Tracking:
   AI-powered tools helped me pull data directly from our test case management system to create real-time dashboards. These dashboards automatically calculated metrics like test completion rates, the number of failed cases, and the percentage of "ready for SIT" cases. Instead of spending hours compiling this information, I had an instant snapshot of where we stood.
2. Forecast Deadlines with Precision:
   By analysing daily test execution rates, AI provided precise forecasts on whether we could complete the remaining test cases within the scheduled timeframe. It instantly calculated the exact increase needed in our daily completion rate, highlighting a goal that was achievable with focused effort but still challenging.
3. Streamline Stakeholder Communication
   Leadership wanted concise updates that captured risks and progress without technical jargon. AI helped me generate polished, executive-level summaries in minutes. This meant I could spend more time strategising with the team instead of drafting reports.
   
   

My personal lesson: when technology enhances, not replaces

One moment stands out during this project: halfway through SIT, we hit a bottleneck with a high-priority blocker. Normally, this would have meant hours spent manually investigating how it impacted our timeline. Instead, I used AI to simulate different scenarios: what if we resolved the blocker tomorrow? What if it took three days? The insights allowed us to inspect and adapt our focus and minimise delays. What I learned is that AI doesn’t replace the human element of Scrum, it enhances it. It gave me the clarity and bandwidth to focus on what truly matters: supporting the team, facilitating collaboration, and solving problems creatively.

The broader implications

This experience prompted me to reflect on how AI has the potential to reshape agile practices and redefine the very essence of the Scrum Master role. Here are some initial reflections:

1. Smarter Retrospectives: Imagine retrospectives where AI identifies recurring sprint patterns or root causes of delays, giving teams deeper insights into their performance.
2. Enhanced Velocity Tracking: By analysing historical sprint data, AI could provide more accurate velocity forecasts, helping teams plan more effectively.
3. Evolving the Scrum Master Role: As AI takes on more of the administrative and analytical tasks, the Scrum Master role may shift further toward being a coach, strategist, and change agent.

While these changes are exciting, they also raise questions: How do we balance AI's analytical power with the need for human intuition? How do we ensure it doesn’t unintentionally stifle creativity or collaboration?

Balancing AI’s analytical power with human intuition requires a deliberate approach that views AI as an enabler rather than a replacement. AI excels at processing data and identifying trends, but human intuition is essential for understanding team dynamics, fostering creativity, and making nuanced decisions that go beyond numbers. To avoid stifling creativity or collaboration, organisations must use AI as a tool to enhance, not dictate, team interactions, leveraging it for insights while ensuring space for open discussions, brainstorming, and empathetic leadership. 

Embracing the potential of AI

AI has already become an invaluable partner in my work as a scrum master. It helps me work smarter, and allows my teams to focus on what they do best: building great products. For those of you in agile or Scrum roles, I encourage you to explore how AI can complement your work. Start small, whether it’s automating reports or experimenting with forecasting tools. The potential is enormous, and the journey is just beginning.