Considering AI Utilization by Scrum Masters - Introduction

This is Day 4’s article of the Mamezou Developer Site Advent Calendar 2025.

I am Ishida from the Agile Group.

In recent years, the evolution of generative AI has been remarkable and is set to greatly change the way we work. On the Mamezou Developer Site, many articles have been posted about utilizing generative AI in various phases of system development such as design, implementation, and testing. (If you’re interested, please also check the #生成AI or #AIエージェント tags.)

On the other hand, the theme of how Scrum Masters themselves can leverage AI in the Scrum process is still evolving, and there are few concrete examples of practical application.

Therefore, in this series spanning three parts, I will explore hints on how Scrum Masters can engage with AI and support their teams, interweaving my own experiences.

The structure of the series is as follows:

• Part 1: Introduction (this article)
• Part 2: Transparency
• Part 3: Inspection & Adaptation

In Part 1 (this article), based on the concept of “Artificial Intelligence” mentioned in the Scrum Guide Expansion Pack, I will introduce the overall picture and basic ideas for utilizing AI in Scrum.

Scrum Guide Expansion Pack was created as a comprehensive companion to the 2020 Scrum Guide and was published in June 2025. It has also been translated into Japanese by volunteers.

In this Expansion Pack, to respond to ever-changing markets and technologies, Artificial Intelligence (AI) is defined as one of the “Scrum roles in the Expansion Pack.” This indicates that AI is recognized not just as a technical trend, but as an important element that strengthens the practice of Scrum.

The Expansion Pack introduces the following ways in which AI can enhance Scrum:

AI can enhance Scrum through the following:

• Empirical Process Control: AI-driven analytics improve transparency, inspection, and adaptation.
• Cognitive Augmentation: AI enables human Scrum team members to focus on strategic, creative, and ethical considerations.
• Continuous Value Adaptation: AI can update and reprioritize product backlog items based on real-time user feedback and trends.
• System Insights: AI can identify hidden interdependencies and improve data-driven decision-making.

In this series, we will focus particularly on the first item, which is closely related to the role of the Scrum Master.

Empirical Process Control: AI-driven analytics improve transparency, inspection, and adaptation.
(Original: Empirical Process Control: AI-driven analytics improve transparency, inspection, and adaptation.)

A key responsibility of the Scrum Master is to support the team in practicing Scrum’s three pillars of transparency, inspection, and adaptation. AI holds the potential to enhance this empirical process control to an unprecedented level.

Within the roles defined in the Scrum Guide Expansion Pack, the “Scrum Master” role includes the following unique sentence:

The Scrum Master must be human.
(Original: The Scrum Master must be human.)

This suggests the importance of roles that only humans can fulfill, such as collaborating with the team and navigating complex human relationships. AI remains a “powerful partner” that extends the capabilities of the Scrum Master, allowing us humans to focus on more essential challenges.

While you might be able to receive Scrum-related advice from an AI Scrum Master based on the spirit of the Scrum Guide, it should never replace the work of the Scrum Master; rather, it should serve as a partner that strengthens that role and maximizes the team’s capabilities.

In this article, as the first step in introducing AI to Scrum, I presented its overall picture and potential.

Next time, with the theme of “transparency,” one of Scrum’s three pillars, we will explore how to leverage this new tool called AI to visualize the project, interspersed with concrete practical examples.