Involve AI in Scrum? Positioning the Role of Smart Assistants in Agile Teams
You might have already got AI assisting with coding and file checks, but have you considered having it as part of your agile team?
In the Scrum development process, there are many repetitive tasks and information dissemination activities such as meeting documentation, requirement clarification, progress tracking, estimation of working hours... And these are actually things that AI is best suited for.
This article will explore how to incorporate AI into your sprint rhythm, transforming it into a 'recorder, analyser, and advisor', truly realizing intelligent team collaboration.
AI More Than Tools: Roles in the Process
Many companies introduce AI tools merely as individual assistance. However, agile teams emphasize teamwork.
Instead of having each person figure things out on their own, let AI be a shared process participant with the following functional roles:
- Scrum Recorder: Automatically records and summarizes daily standup meetings and retrospectives
- Requirements Assistant: Consolidates user stories and acceptance criteria based on user feedback
- Task Coordinator: Analyzes progress deviation, provides task allocation suggestions
- Knowledge Manager: Builds Sprint knowledge base, outputs version update records
These tasks don't replace the PO, Scrum Master, or Dev roles but complement and remind them, preventing information gaps and redundant work.
Practical Scenarios: How AI Engages in a Sprint?
Scenario 1: Daily Standup Meeting
AI generates an 'update summary' based on the day's progress, project files, and board records, then raises risk alerts. For example:
「Backend Task #52 has been delayed for two days; it depends on Frontend task #55. It is suggested to adjust the schedule earlier.」
After the meeting, AI automatically produces a record and drafts Jira updates, reducing Scrum Master's workload.
Scenario 2: Building and Decomposing User Stories
AI assists in extracting requirement descriptions and critical constraints through customer interactions or service ticket management. For example:
「As an order manager, I want to be able to filter refund requests from the Dashboard based on amount, time, and status.」
AI automatically transforms these into Story + Acceptance Criteria and provides prioritization suggestions.
Scenario 3: Sprint Review Generation and Version Recordation
When a sprint concludes, AI consolidates Git commits, merge requests, and issue updates to auto-generate version update summaries:
「V1.5.0 adds three features, fixes two bugs. Key updates: Adds an interface for setting API permissions, optimizes backend export report performance.」
This reduces document writing time, helping product and marketing teams quickly access information.
NT Tech's Entry Point: Assisting AI in Your Development Process
NT Tech assists enterprises to introduce 'process-oriented AI assistants', not just developing models and interfaces but truly integrating AI into agile processes.
We can help you:
- Design AI tools that integrate with your development tools (such as Jira, GitLab, Slack)
- Create 'task node prompt template' and memory modules
- Establish development knowledge bases and version update generation mechanisms
- Conduct department-level training to assist the development team in using AI assistants effectively
We believe that when AI isn't just an engineer's assistant but a part of your workflow, your development efficiency and knowledge accumulation will significantly improve.
