Atlassian Community Nairobi

##


AI can now turn a rough business priority into a structured goal in seconds. It can suggest objectives, generate measurable results, review weak drafts, summarize progress, and adapt information for different teams. But creating a better goal is only the beginning.
The greater challenge is connecting those goals to real work, live organizational data, changing operational conditions, and the decisions required to produce measurable outcomes.
From Goals to Action explores how AI is evolving from a tool that generates information into a system that can understand context, interact with enterprise data, support decisions, and participate in governed workflows.
The event brings together two complementary sessions across the Atlassian ecosystem.
Part 1: Engineering the AI Goal-to-Execution Lifecycle in the Atlassian Ecosystem explores how Rovo agents, AI assistants, MCP-based systems, and Oboard's MCP server can help teams create, review, retrieve, connect, and update goal information.
Part 2: Assets to Action: Agentic Workflows That Understand Your Data moves from strategic intent into the enterprise context required for execution. Atlassian Assets enables teams to work with connected information such as software, devices, services, locations, policies, access, security, and other organizational objects. The source material presents a progression from natural language access to Assets, to Assets-aware agents, and finally to agentic automation.
Together, the sessions explore a connected execution model:
Goal → Context → Enterprise Data → Agent → Workflow → Action → Outcome
The central question is simple:
How do we design AI-enabled systems that understand what an organization wants to achieve, understand the operational reality around that goal, and help teams move from intention to measurable execution?


## Event Objectives:

• Explore how AI is changing the complete lifecycle from goal setting to execution.
• Show how goals can evolve from static documents into connected data that AI systems can retrieve, review, interpret, and update.
• Examine how Rovo agents, AI assistants, MCP-based integrations, Atlassian Assets, and automation can work together.
• Demonstrate how natural language interaction can simplify access to enterprise information.
• Show how domain-aware agents can use organizational data to analyse situations and provide relevant context.
• Explore how agentic automation can connect AI reasoning with operational workflows and predefined actions.
• Clarify where AI can assist, where automation may operate within defined controls, and where human judgment and accountability remain essential.
• Give attendees a practical framework for identifying valuable agentic workflow opportunities in their own organizations.


## What We Will Explore

### Part 1: Engineering the AI Goal-to-Execution Lifecycle

AI can already help teams formulate stronger goals. The greater opportunity lies in what happens after those goals are created.
This session explores AI-assisted goal creation and review, measurable outcomes, goal adaptation across teams, dynamic information retrieval, progress updates, and the connection between goals and ongoing work.
Attendees will also examine how MCP-based architectures can make structured goal information accessible to AI assistants and agents.
Practical examples, including Oboard's MCP server, will demonstrate what becomes possible when goals are treated as live, connected organizational data rather than static planning documents.
The session will also examine where AI can accelerate execution and where prioritization, interpretation, accountability, and strategic judgment must remain human-led.


### Part 2: Assets to Action: Agentic Workflows That Understand Your Data

Goals alone do not provide enough context for execution.
An objective such as "Reduce employee downtime caused by hardware incidents" may require a system to understand which devices exist, who owns them, where they are located, which are under repair, what services depend on them, and who owns the next action.
This session explores how Atlassian Assets can provide that contextual layer through three practical patterns.

1. Ask, Don't Query
   Users can ask questions about Assets using everyday language instead of manually constructing queries. The source presentation demonstrates this through an IT scenario involving locating laptops, identifying devices with repair status, and using the information in reporting.
2. Build Assets-Aware Agents
   Teams can create a Rovo agent, define clear instructions, add the Search Assets capability, and scope the agent to the organizational data relevant to the use case.
3. Move Toward Agentic Automation
   Agents can then be connected to automation so that current enterprise information becomes part of repeatable workflows, including actions such as creating work items and sending notifications.

This creates a progression from: "Search → Interpret → Manual Action to: Ask → Understand → Reason → Act"

### Agenda

4:00 – 4:15 PM: Check-in, introductions, and networking
4:15 – 5:15 PM: Engineering the AI Goal-to-Execution Lifecycle in the Atlassian Ecosystem
5:15 – 6:00 PM: Assets to Action: Agentic Workflows That Understand Your Data
6:00 – 6:15 PM: Q&A and open discussion: AI agents, MCP, enterprise context, automation, governance, and human oversight
6:15 – 7:00 PM: Food, networking, photos, community conversations, and closing announcements


## Expected Outcomes

By the end of the event, you should be able to:
• Explain the difference between AI-generated content, AI assistants, agents, and agentic workflows.
• Understand why AI becomes more valuable when it can work with live organizational context.
• Recognize how goals can function as connected operational data.
• Understand how MCP can connect AI systems with external tools and structured information.
• Understand how Atlassian Assets can provide enterprise context to Rovo agents and automation.
• Identify where natural language interaction can simplify enterprise information retrieval.
• Distinguish when AI should retrieve information, recommend an action, execute an approved workflow, or defer to a human decision-maker.
• Identify at least one real workflow that could be redesigned using the Goal → Context → Agent → Action → Outcome model.


## Key Takeaways

1. Goal-to-Execution Framework
   A practical model for connecting organizational intent to enterprise context, AI reasoning, workflows, actions, and measurable outcomes.
2. Enterprise AI Architecture
   A clearer understanding of how Rovo, MCP, Atlassian Assets, automation, and ecosystem applications can work together.
3. Practical Agentic Workflow Patterns
   Examples of moving from natural language information retrieval to Assets-aware agents and agent-supported automation.
4. Human and AI Responsibility
   A clearer distinction between what AI should assist with, what may be automated within policy, and where human accountability must remain explicit.
5. Path to Implementation
   A framework for evaluating internal workflows and identifying where connected AI can create practical operational value.


## Target Audience

• Software engineers
• Solution and enterprise architects
• AI and automation engineers
• Engineering managers and technology leaders
• Product, program, and delivery managers
• Jira, Confluence, Jira Service Management, and Assets practitioners
• IT operations and ITSM professionals
• Platform and developer experience teams
• Digital transformation leaders
• Developers building Atlassian integrations and Marketplace applications
• Atlassian partners and consultants
• Technical decision-makers evaluating Rovo, MCP, enterprise AI, and agentic workflows

The event is particularly relevant for teams asking: Where should AI sit within our systems, data, workflows, and decision-making processes to produce measurable organizational value?


## Like an attendee at one of our events, you can expect to:

• Share and learn Atlassian product knowledge, best practices, and case studies.
• Provide valuable user input to Atlassian so that they can keep making great products for us to use.
• Network and build a support system with fellow Atlassian product users.

Atlassian Community Nairobi is an award-winning community:

• SET THE STANDARD 2022
• SET THE STANDARD 2018
• MOST ACTIVE in EMEA 2017
• Global MASTER OF CONVERSION WINNER 2017

HELP US PLAN OUR NEXT EVENT

• Do you want to suggest a topic for us to focus on?
• Do you have a presentation proposal?
• Does your company want to host an event?
• Does your company want to sponsor an event?

Check out our community on our socials: