AI Builders Week

AI summary focused on the core points of the original article.

The core message running through the entire Atlassian AI Builders Week guide is to show that product managers (PMs) and designers are not simply becoming 'developers' who write code directly, but are becoming leaders (Steering) who use AI to maximize work efficiency and guide the entire team in the right direction.

1. "Don’t row; steer the ship"

Expanding roles: In the AI era, the roles of PMs, designers, and engineers are not being unified into one (`AI Builder`); rather, the Circle of each discipline is expanding further.
Solving bottlenecks: Thanks to AI, engineering productivity has increased explosively. If PMs and designers are only doing coding directly at this point, bottlenecks arise on the planning/design side.
Providing direction: The most important role of PMs and designers is not writing code directly, but setting direction and increasing decision-making speed so that the team can understand and execute the right product direction and context.

2. AI usage and practical guides for PMs and designers

💡 High-value use cases for PMs

Multi-step workflow automation: Beyond simple chatbot questions, it guides users to handle end-to-end workflows by connecting files, tools, and code.
Practical application examples:
Combine Jira, Confluence, and Loom data to write weekly work summaries
Use conversational prompts to set product strategy and roadmaps (using the RICE framework)
Automatically create Jira Product Discovery (JPD) tickets and manage the backlog
Create working prototypes based on backlog ideas

🎨 Use cases and capability building for designers

Democratizing design decisions: Excellent designers in the AI era do not stop at quickly generating designs with AI; they build guidelines and systems so the entire team can make good design decisions.
Strategy and product framing: Going beyond the existing design domain, they expand their capabilities so they can broadly participate in areas such as strategy formulation and writing product frameworks, even into PM territory.

3. Practical tips for AI agents and token optimization

Using Rovo CLI: Train teams to actively use a terminal-based autonomous agent that is not just an AI for answering questions, but one that can read local computer files, connect with Atlassian tools, and has persistent memory.
Token budget management: Tokens should be treated as a 'budget,' not an unlimited resource. Guide users to keep the context window at around 50–70% to prevent hallucinations or performance degradation in the agent.
Skill best practices: It is recommended to formalize recurring prompts (`skill.md`) and share them with the whole team and organization through a shared repository for reuse.

XPLORED View

This guide clearly shows that in the AI era, PMs and designers are expanding into people who design the team’s context, tools, and decision criteria, rather than people who “make more things directly.”

Good situations to read it

When you want to reorganize how the team operates in organizations where the boundaries between AI builders, PMs, designers, and engineers are blurring
When you want to create AI workflows by connecting practical work tools such as Jira, Confluence, Loom, and Rovo CLI

Key takeaways

As AI increases execution speed, organizational bottlenecks move from code production to direction setting, context sharing, and decision quality.
Capturing recurring prompts and work methods as skills and documents becomes an operating asset for the entire team, beyond just a personal productivity tip.

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