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In the fast-paced world of enterprise asset management is a critical differentiator. Join this dynamic panel featuring leaders from Atlassian customers as they share their journeys in leveraging ITSM asset management solutions to gain visibility, control, and agility over their hardware and software assets. Discover how these organizations have transformed their asset management practices to support innovation, ensure compliance, and maximize operational efficiency.
Administer Rovo in your organization
Explore how to govern Rovo in Atlassian Administration, including where it is available, what it can search, how external AI access is managed, and how adoption and audit activity are monitored. This session is for org admins who need to set up global Rovo parameters and stakeholders who want to understand how Rovo works across a site.
In this session, you’ll learn how to:
- Enable and block access to Rovo for specific apps
- Enable web search in Rovo and understand its governance implications
- Add, monitor, and troubleshoot Rovo connectors
- Explain what the Rovo MCP server is and what governance problem it solves
- Understand user behavior with Rovo Insights
- Monitor Rovo and Rovo Dev credits and events
Is your AI hallucinating or helping? The difference is your Teamwork Graph. We’re moving beyond documentation as a chore and treating it as a high-leverage business asset. Learn how to structure your Atlassian stack so LLMs can amplify your team's best work, keep context intact, and bridge the gap between messy internal workflows and polished business outputs.
Matt Reiner (K15t)
Ready to roll up your Assets sleeves? In this hands-on lab, you’ll run the full asset-management lifecycle - from first data ingestion to a working Hardware Asset Management environment. No slides or lectures - just a live instance and guided steps.
Here’s what we’ll build together:
- Connect and ingest data — Hook an external source in the Data Manager NEW UI, map the schema, and import raw asset records.
- Cleanse and reconcile — Use Data Manager workflows to deduplicate, normalize, and validate data before it reaches production.
- Feed the Common Data Model (CDM) — Map cleaned data into Atlassian’s CDM, an opinionated schema that gives a structured foundation without building from scratch.
- Explore Hardware Asset Management — With the CDM populated, see hardware views, lifecycle tracking, and workflows that simplify physical asset management.
- Sneak peek: Software Asset Management (EAP) — A 5-minute preview of upcoming Software Asset Management in Early Access.
Key takeaways:
- Faster asset setup — The CDM provides a ready structure so you can get Assets running quickly.
- Trusted asset data — Data Manager’s ingestion and reconciliation cleans data before it hits your schema.
- Ready to go Hardware management — The Hardware Asset Management module supplies purpose-built views and workflows.
By session end, you’ll have hands-on experience and a working asset-management environment available for a month to play with.
Rick Lefort (Atlassian), Silvia Davis (Atlassian)
Your team already works in Claude, ChatGPT, and Gemini. The friction comes when those tools have no connection to the actual work: the Jira tickets, the Confluence docs, the decisions your team made last Tuesday. Without that context, AI assistants give generic answers, and your people end up doing the manual work of bridging the gap themselves.
In this session, you will see live demonstrations across Claude, OpenAI, and Gemini, connected to the Atlassian Teamwork Graph in real time. Using the Model Context Protocol, your existing AI tools can search and read your Jira and Confluence knowledge base without duplicating data or requiring anyone to switch windows. You will also see how Rovo connects to external agents like Gemini through Agent-to-Agent communication, so AI tools can trigger real actions across your Atlassian work and hand results back and forth. Every connection runs from wherever your team already operates, including desktop apps, IDEs, and developer interfaces.
This session is designed for engineering leads, IT admins, and team champions who want to extend AI tools they already trust into a connected, governed Atlassian workflow. You will leave with a clear picture of which connection method fits which use case and how to make the case for your team.
Key Takeaways
- Discover how the Model Context Protocol lets Claude, ChatGPT, and Gemini search and read your Atlassian knowledge base in real time, with no data duplication and no manual copy-paste required.
- Learn how Rovo's Agent-to-Agent communication enables external AI tools to trigger updates across Jira and Confluence, so your AI workflows produce actual team outcomes rather than just answers.
- Build a practical framework for choosing between MCP, Agent-to-Agent, and direct integrations based on your team's tools, workflows, and governance requirements.
Jemma Swaak (Atlassian)
Building a Rovo agent is one thing. Getting it to run reliably, handle complex multi-step work, and operate without someone watching over it is another challenge entirely. Most teams hit a ceiling when their agents are doing simple tasks well but cannot yet be trusted with the real, high-stakes workflows that would actually save hours of human effort.
This live demo takes you past the basics. You will see how to break complex processes into coordinated subagents, each handling a focused task, with an orchestration layer connecting them into a coherent workflow. You will also see how evaluations work to test agent reliability before you put them into production, how agent accounts give autonomous agents their own governed identity within your Atlassian environment, and what it looks like when agents run end-to-end without requiring human interaction at every step.
This session is designed for attendees who have foundational experience building Rovo agents and are ready to see what operationalizing them in real-world environments actually looks like. If you are responsible for scaling AI workflows across a team or organization, this is the session to attend.
Key Takeaways
- Learn how to decompose complex Rovo workflows into coordinated subagents so each task is handled with precision and the full process runs reliably end to end.
- Discover how agent evaluations let you validate reliability and catch failure points before deploying autonomous agents into production environments.
- Understand how agent accounts and autonomous execution work together to give your agents a governed, independent identity so they can complete real work without constant human supervision.
Building better AI agents is a skill your team can learn, no code required. Join Atlassian as they share the steps for creating effective AI agents for everyday workflows, from identifying automation opportunities to designing high-impact agent experiences. You’ll learn how to write clear instructions and examples (including what good and bad look like) and how to leverage existing knowledge to improve agent performance. Hear industry best practices, practical tips, and real-world use cases for Rovo Studio, and explore how Rovo Agents can extend to multiple surfaces beyond Atlassian.
Key takeaways:
- Learn how to bring Rovo Agents into everyday workflows.
- Explore Rovo Studio to build, test, and deploy your own agents.
- Discover practical ways your team can collaborate effectively with AI agents.
Sushant Koshy (Atlassian), Shihab Hamid (Atlassian)
Predicting where AI goes next is nearly impossible. What you can control is how ready your organization is when it gets there.
Agents are already a way of life for enterprise teams — automating workflows, making decisions, and accessing sensitive data across your organization. Governing that is table stakes. But technology keeps evolving, and the organizations that will win with AI aren't the ones who predicted every change. They're the ones who built a foundation flexible enough to handle whatever comes next.
In this session, we will share how leading enterprises are shifting from reactive AI governance to a proactive foundation of context-aware protections that give teams the confidence to move fast. We'll cover what it looks like in practice to stop chasing AI and start setting the conditions for it to flourish across teams, regions, and sensitive workflows.
You'll leave with a concrete framework for building a governance foundation that doesn't slow innovation down. It's what makes innovation possible.
This session is designed for IT leaders, security teams, and compliance professionals responsible for scaling AI adoption across regulated environments.
Ashwini Rattihalli (Atlassian), Nisha Narasimhan (Atlassian)
Every team has a wishlist for how Jira and Confluence could work better for them. A workflow that fits the way your team actually operates so AI can build exactly what you want in fewer cycles. Until now, building those customizations meant waiting on a developer, and hoping the result matched what you described.
In this hands-on session, bring your laptop and follow along step-by-step as you use Rovo Studio to describe what you need in plain language and generate a working Jira or Confluence extension, secured by Forge and running entirely within your Atlassian environment. You will learn how to move from idea to iteration efficiently, including how to use image uploads and annotations to give Rovo precise context, when to redirect early rather than undo later, and how to recognize when an extension is ready for your team. No coding or development experience is required at any point.
This session is designed for Jira and Confluence users of any technical background who want to solve real team problems without waiting on a development resource. You will leave with a clear, repeatable path for getting started in Studio, a finished extension you built yourself, and the confidence to tackle your team's next request on your own.
Key Takeaways
- Learn the step-by-step Studio workflow for describing what your team needs, generating a custom Jira or Confluence app, and iterating until the result feels exactly right.
- Discover how to combine image uploads, annotations, and well-scoped descriptions to give Rovo the context it needs so each iteration moves you forward rather than sideways.
- Walk away with a completed, Forge-secured extension built during the session and a practical framework for applying the same process to your team's next feature request.
Caterina Curti (Atlassian)
Learn how to use forms in a company-managed Jira space to collect structured information from any team or stakeholder. Forms are a flexible feature you can add to your Jira space to gather input right where you track work.
In this session, you’ll learn how to:
- Create and customize a form to collect the information you need
- Configure fields based on your audience’s context
- Add conditions to a form to control how users can submit information
- Determine whether you should use a public form or restrict access
EU-based customers on Atlassian Cloud or who are considering the move need to know: how does Atlassian meet my data privacy and compliance requirements? From DORA to NIS2 to BSI C5 and more, we're actively addressing regional and industry-based compliance needs.
In this session, we'll map Atlassian Cloud directly against the EU frameworks that matter most - so you can see exactly what’s available and where we’re closing any remaining gaps. Walk away with a clear picture of how you can address your privacy and compliance requirements in cloud and how to make the case internally for the path forward.
Key takeaways:
- Atlassian's compliance coverage across DORA, NIS2, BSI C5, GDPR, and EUCS
- What's available today and what else is shipping this year
- Three actions you can take this quarter
Configure queues for agent triage
Learn how to configure Jira Service Management queues that help agents triage and prioritize incoming work, from defining conditions and priority groups to organizing queues for your team’s process. Queues are a core part of service spaces that support how your team intakes, triages, and fulfills requests.
In this session, you’ll learn how to:
- Configure agent-specific and shared queues for triage
- Use JQL queue conditions
- Sort and reorder queues by priority
- Follow queue configuration best practices
- Create queue priority groups
- Recognize ways agents can personalize their view
When a single missed handoff in steam turbine field service can cost six figures, paper forms and email chains aren't good enough. In this session, a global energy leader and its implementation partner share how they reimagined field service operations inside Jira Service Management — cutting lead time from 60 days to 21 across ~45,000 assets. You'll hear what drove the change, how the platform was architected for scale, and how Rovo and automation are now layered on top to build a system that doesn't just work, but learns.
Key takeaways:
- How to map a compliance-heavy industrial field service process onto Jira Service Management — without losing the operational nuance that keeps turbines running
- What drove a 3× reduction in lead time (60 → 21 days) and what almost prevented it
- Practical patterns for layering AI augmentation onto a stabilised service workflow, with real examples from production
Johannes Tegethoff (HiQ GmbH)
Incoming requests pile up, sit in queues, get routed to the wrong team, and arrive without enough context for anyone to act on them quickly. For many support and operations teams, triage is the work before the work: a manual, time-consuming process that delays resolution and drains the people best equipped to solve the actual problem.
In this hands-on lab, bring your laptop and follow along step-by-step as you build a Rovo triage agent from the ground up, in Studio. You will see how to identify an agent use case, translate real-world support workflows into clear agent requirements, connect the right knowledge sources and actions, and write instructions that produce consistent results. The session also covers how to define an evaluation approach so your agent can be piloted in a controlled, measurable way before moving it to production.
This session is built for team leads, operations managers, and Atlassian practitioners who are ready to move from curiosity about AI agents to actually shipping one. No prior agent-building experience is required. You will leave with a repeatable framework you can apply to your own workflows the same week.
Key Takeaways
- Learn a practical framework for identifying which team workflows are genuinely agent-ready so you invest your time in problems where an agent will deliver real, measurable value.
- Follow along on your laptop through the live, step-by-step construction of a triage agent in Rovo Agent Studio, including how to write clear instructions, connect knowledge sources, and wire up actions that produce reliable outcomes.
- Discover how to design an evaluation approach that lets you test agent performance in a controlled environment and build the team trust needed for a successful, lasting rollout.
Paper-based strategic plans look impressive in the boardroom, but they're usually stale by the time you they’re created. Most organizations know their strategy should be a living system, but don’t know where to start to move from a static document to something dynamic.
In this hands-on workshop, you'll learn how to incrementally evolve a paper-based strategic plan into a living strategy model using Strategy Collection.
Customer service is often where the richest signals about product quality, incidents, friction, and unmet needs first appear — yet those insights can get trapped in support tools and handoffs. In this 15-minute theatre session, we’ll show how Atlassian Customer Service Management brings customer service into the Atlassian System of Work.
Attendees will see how CSM, powered by AI and the Teamwork Graph, gives support teams the context to deliver faster, more personalised service while staying connected to software, IT, and product teams. Instead of re-explaining issues across systems, teams can collaborate from a shared view of the customer, related incidents, feature requests, knowledge, and work already happening across Atlassian tools; all whilst customers benefit from faster resolutions, and more personalised service.
Dorothea Linneweber (Atlassian)
What if Rovo could answer a question no single person, or tool, can answer on their own? This session is built around two live demos that showcase how Rovo works across the tools your team already uses (Loom, Confluence, and Jira) to turn scattered information into structured, actionable outcomes.
In this 30-minute live demo session, you'll see how Rovo connects the dots between a messy Loom brain dump, a months-old Confluence page, and an open Jira epic, delivering answers and outputs that would otherwise take hours to pull together. You'll leave with real workflows you can put into practice immediately.
Key takeaways:
- Turn raw recordings into ready-to-share assets: watch Rovo transform a stream-of-consciousness Loom recording into a structured Confluence summary, complete with decisions, open questions, and Jira follow-up tasks
- Get answers no single person or tool can give you: see how Rovo surfaces cross-tool intelligence by pulling together context from Jira, Confluence, and Loom in seconds, then traces every answer back to its source
Every coding agent knows your code. Almost none of them know your work, the Jira issue that kicked it off, the Confluence doc that explains the trade-offs, the decisions made last sprint, or the team that owns the outcome. The result: agents that sound confident and build the wrong thing.
We're going to show you the difference. Live. Side-by-side.
Same task, same agent. Once flying blind, once powered by Jira, Teamwork Collections, and the Teamwork Graph. We'll run it across RovoDev, Claude, Codex, Gemini, and Cursor so you can see exactly where context changes everything. No slides. No hand-waving. Just the delta, in real time.
We'll also pull back the curtain on Agentic Jira, agents that can be assigned, @mentioned, and wired into your workflows right now, and make the case for why context, not the model, is the real moat in the agentic age.
Key takeaways:
- See the gap: a live, side-by-side demo of the same agent with and without organisational context. The difference is undeniable.
- Set it up yourself: concrete patterns to connect agents to Jira, Confluence, Bitbucket, and third-party sources via Teamwork Collections.
- Why Atlassian wins here: the Teamwork Graph is a structural advantage that composes with any coding agent your team already uses.
Gonçalo Cardoso (Atlassian), Ho Kim (Atlassian)
Great JSM agents don't start with AI
AI agents rarely fail because the model is weak – they fail because the organisation underneath them isn't ready. It's not a technology gap, but an architecture and leadership gap: knowledge nobody maintains, processes that only live in people's heads, no clear line between what a machine may decide and what stays with a human. And it's not just an ITSM problem – it's the same pattern we see across technology, organisation, and people whenever transformation stalls.
In this session, using Jira Service Management (JSM) as the proving ground, we show how to assess how ready your service organisation actually is. We then follow one ordinary ticket end to end – routed, picked up by the right specialist, resolved across the tools involved, every step documented – to make concrete what "AI-ready" looks like before you call anything agentic.
Getting there takes clear analysis, solid architecture, and leadership willing to commit – and staying there is a continuous maturity process, not a one-off fix. This is exactly the kind of transformation we help organisations navigate: in practice, with experience, and to good effect.
This is a sponsored session. Badge scanning is optional. By having your conference badge scanned, you are opting in to be contacted by the sponsor. You will be subject to the sponsor’s communications and privacy policies and must contact them directly if you later wish to change your preferences.
Stefan Hagen (Seibert Solutions GmbH)
What happens when enterprises move beyond AI experiments and become an AI-native organization? Join some of the fastest-growing teams as they share how Rovo is becoming part of everyday work for their teams. Hear how they are using Atlassian Rovo to acoomplish AI agents across their entire business to reduce operational overhead, saving hours, improving cycle times, and accelerating time to value across.
Key takeaways:
- See real customer use cases for agentic workflows across Jira, Confluence, and connected tools
- Take away practical patterns for scaling agentic AI responsibly and predictably
- Learn practical strategies for implementing agentic workflows and accelerating teams with AI-powered tools.
Sara Beyer (Ilkari), Razvan Nechifor (Edenred SE), Natalia Lezhai (Personio)
VodafoneZiggo’s AI transformation started with an onsite workshop to identify the highest-impact AI use cases for the business. From there, VodafoneZiggo and Atlassian collaborated to design and deploy custom Rovo agents. Basak Erdogan (Technical Platform Lead) partnered with Atlassian’s AI specialist to build an end-to-end agentic workflow, from Loom-captured planning sessions through structured Jira Epics to automated sprint rituals with zero manual reporting. In five months, agent interactions grew 20x and monthly active users scaled 5x across Marketing, tech teams, and beyond.Basak will share the honest story of going from a single workshop to organisation-wide AI adoption at one of Europe’s largest telcos, with active POCs of more use cases still to come.
Key Takeaways
- Start with the business, not the technology — how an onsite workshop with the CMO and 7 marketing leaders identified the use cases with the highest measurable impact, ensuring AI adoption was anchored to real business priorities from day one.
- From ideation to deployment through partnership — how VodafoneZiggo collaborated with Atlassian's AI specialist to design and deploy Rovo agents that drove 20x growth in agent interactions and eliminated manual quarterly-planning overhead for Marketing squads.
- Value that scales beyond Marketing — how the same patterns expanded into tech teams, proving that a focused starting point (one workshop, one function) can become a cross-functional accelerator across the enterprise.
Basak Erdogan (VodafoneZiggo)
Explore the anatomy of the Teamwork Graph and see how its connected data model links people, work, goals, and knowledge to create richer context across Atlassian. Learn how to nurture high-quality graph data to power the outcomes you want, then see real examples from multiple collections that demonstrate how the graph delivers value in practice.
Artificial Intelligence is only as smart as the context it receives. Without a unified, context-aware foundation, enterprise AI risks delivering fragmented insights and low return on investment.
In this session, learn how the Teamwork Graph serves as the ultimate context layer for your organization: bridging data across workflows, teams, and tools to power smarter, more intuitive AI experiences like Atlassian Rovo. Discover how shifting to a proactive, context-aware architecture allows you to maximize the ROI of your AI investments while maintaining enterprise governance and trust.
Key Takeaways
- Understand how connected organizational context dramatically improves AI accuracy, relevance, and value across everyday workflows
- Learn how a grounded context layer shifts your enterprise strategy from reactive AI governance to a proactive, secure foundation
- See how the Teamwork Graph enables enterprise-grade scaling, measurement, and data privacy without sacrificing performance or context depth
Sanja Samirana Panda (Atlassian), Hersh Iyer
Most teams reach a natural plateau with AI agents. A single agent handling a focused task works well in isolation, but the workflows that would unlock real organizational value are rarely that simple. They involve multiple steps, handoffs between systems, and a need for consistent, trustworthy performance before anyone is willing to let them run without supervision.
In this hands-on workshop, bring your laptop and follow along step-by-step as we give you the practical skills to move past that plateau. You will learn how to design subagent architectures that break complex processes into coordinated, focused tasks, and how to orchestrate those subagents into reliable end-to-end workflows. The session also covers how to use evaluations to test whether your agents are actually performing the way you expect, how to set up agent accounts that give autonomous agents a governed identity in your Atlassian environment, and how to build automations that run without requiring human intervention at every step.
This session is designed for practitioners with foundational Rovo agent-building experience who are ready to move from experimentation into real-world deployment. If you are responsible for scaling AI workflows across a team or organization, this workshop will give you the architecture and the confidence to do it.
Key Takeaways
- Learn how to design and orchestrate subagent systems that divide sophisticated workflows into coordinated, reliable components that work together without manual handholding.
- Build a working evaluation approach that lets you test agent reliability, surface failure modes early, and establish the evidence base your team needs to trust autonomous agents in production.
- Understand how agent accounts and autonomous execution models give your agents a clear, governed identity so they can run end-to-end workflows independently while remaining auditable and controlled.
Sven Peters (Atlassian)
In this session, learn how to replace a fragmented toolset with Atlassian Strategy Collection, creating full traceability from corporate strategy through portfolio planning to team-level delivery. We’ll walk through how to structure the rollout, get buy-in from leaders, and get deeper visibility from strategy to execution.
For organizations juggling multiple planning tools with no clear line of sight between strategy and execution, this session offers a practical blueprint for consolidation.
Rethink collaboration: Make Microsoft Teams part of the Jira workflow
Jira tracks work, but the conversations that move it forward happen in Microsoft Teams and Outlook. Updates are shared in channels, decisions get made in chats, and meetings happen in calendars, while the Jira work item only captures the final outcome.
We'll show how to connect Microsoft 365 collaboration directly to Jira work items, so updates and discussions stay tied to the work automatically. This creates the context foundation AI needs to understand the work, its history, and the decisions behind it.
Key takeaways
- How to keep collaboration structured by default, so Jira reflects what actually happened
- How structured updates improve handoffs, visibility, and decision traceability
- How the same setup supports both Jira Software and Jira Service Management workflows
This is a sponsored session. Badge scanning is optional. By having your conference badge scanned, you are opting in to be contacted by the sponsor. You will be subject to the sponsor’s communications and privacy policies and must contact them directly if you later wish to change your preferences.
Andreas Schmidt (yasoon)
You already use Rovo. But are you getting the most out of it? Platform Skills are pre-built, best-practice instructions that take the guesswork out of prompting - delivering reliable, consistent outputs for common team tasks. Instead of crafting the right prompt every time, you use a Platform Skill and get the right result every time.
In this 30-minute live demo session, you'll see how Platform Skills turn everyday teamwork - writing updates, summarizing pages, breaking down work - into fast, repeatable actions. New to Platform Skills? You'll leave with AI shortcuts you can put to work immediately.
Key takeaways:
- Discover pre-built Skills: browse and use ready-made Skills for common tasks like drafting status updates, generating action items, and obtaining data insights
- Work faster with more consistency: reduce time spent on repetitive tasks while getting reliable, high-quality outputs every time
Work does not happen in one place. Every day, critical context moves across meetings, pull requests, Jira tickets, Confluence pages, Slack threads, and Looms. A spec gets buried in a recording, a blocker hides in a thread, and a follow-up task simply goes uncaptured. By the time someone notices the gap, momentum is already lost.
In this live demo, see how Rovo keeps work moving by surfacing the right context before you have to go looking for it. Watch Rovo help teams understand what changed overnight, prepare them for upcoming meetings with relevant background already assembled, capture follow-ups that would otherwise fall through the cracks, and hand tasks to agents with the full context attached. Every insight appears right where you are working, across Home, the Rovo Button, mobile, Jira, and Chat, so nothing requires a context switch to act on.
This session is ideal for team leads, project managers, and individual contributors who want to spend less time hunting for information and more time doing the work that actually moves projects forward.
Key Takeaways:
- See how Rovo monitors work happening across your tools and surfaces the exact insights your team needs, without waiting to be asked.
- Understand how upcoming meetings, overnight changes, and uncaptured follow-ups are automatically surfaced and ready to act on.
- See how Home, the Rovo Button, mobile, Jira, and Chat work together to keep critical context and next actions within reach at all times.
Brian Feldman (Atlassian)
Scaling AI across a large organisation is less about turning features on and more about building the confidence to expand safely. Admins, AI champions, and central teams need practical controls, clear visibility into what is working, and a repeatable playbook for onboarding new teams without creating risk or confusion. Without those foundations, adoption stalls or sprawls in ways that are hard to unwind.
In this live demo, we walk through exactly how to govern and grow Rovo at scale. Attendees will see how to manage users and permissions, set guardrails that reflect real organisational policy, monitor agent usage across teams, evaluate ROI with meaningful metrics, and connect third-party tools so the platform grows alongside the business. Every step is grounded in what central AI teams and champions actually need to run a credible, measurable rollout.
This session is built for IT admins, AI programme leads, and internal champions who are moving beyond pilot programmes and need a structured, auditable approach to enterprise-wide Rovo adoption.
Key Takeaways:
- Learn how to configure user access, set practical guardrails, and establish clear policies that keep Rovo adoption safe and auditable as your organisation grows.
- Discover how to monitor agent usage, measure adoption trends, and evaluate ROI using the metrics that resonate with both technical and business stakeholders.
- See how to integrate third-party tools and support structured rollout plans so each new team joins a consistent, well-governed Rovo ecosystem.
Ashwini Rattihalli (Atlassian)
Getting an AI agent to write decent code is no longer the hard part -- keeping your team aligned when planning, execution, and review all move at different speeds is. In this session, engineering teams will see a complete, realistic AI-native delivery loop in Jira: turning scattered context into agent-ready plans, delegating work to humans and AI, and reviewing outcomes with intent and decisions baked in.
- Turn scattered product, engineering, and customer context into agent-ready Jira plans with clear ownership
- Decide what to delegate to agents vs. keep with humans, and where to add guardrails and approval checkpoints
- Review and iterate agent output with rationale and learning loops connected back to Jira as your system of record
Chelsea Bullock (Atlassian), Matthew Canham (Atlassian)
Last year, Atlassian’s first State of Product report revealed a paradox: product teams were adopting AI faster than ever, yet felt more disconnected from strategy than before. Burnout was up. Confidence was down. And the relentless push toward profitability was squeezing out the work PMs actually signed up to do.
So what’s changed? In this panel, Axel Sooriah (Atlassian) sits down with product leaders to unpack the findings from Atlassian’s second annual State of Product report. Drawing on survey data from 1,000+ product professionals, the conversation goes beyond the numbers — into what’s actually working inside high-performing product teams, where AI adoption is paying off (and where it’s still falling short), and what the best product orgs are doing differently to close the gap between speed and strategy.
Key takeaways:
• How the most effective product teams are using AI to reclaim strategic time — not just automate tasks
• Why the gap between product teams that experiment and those that don’t is widening, and what that means for outcomes
• What product leaders are doing to rebuild confidence and focus amid growing pressure to deliver business impact
Axel Sooriah (Atlassian)
Stop Adding AI to Broken Workflows — How to transform AI into value for your teams
AI adoption is everywhere. AI-native operations are not. While organisations are rapidly introducing chatbots and isolated agents, many still struggle to translate experimentation into measurable business value. The problem is rarely access to technology. The problem is that AI is being added to existing work without fundamentally redesigning how that work flows. That creates a hidden cost: individual, inefficient prompting can not only consume credits, but also time and attention without producing reusable capability or measurable outcomes. AI-native workflows are not free either, but they make AI spend more deliberate, governable and optimisable by reusing context, choosing the right level of automation and measuring cost against business value. In this session, we will explore the growing gap between AI adoption and operational transformation, define what AI-native actually means, introduce a practical maturity model and explain why the workflow—not the individual prompt—is the true unit of AI transformation on the Atlassian Platform. You will discover what organisations need to connect people, agents and trusted organisational context responsibly, and why platform expertise, process understanding, transformation capability and people enablement must come together. Bring us one workflow your team complains about every week. At our booth, we will demonstrate a live agentic workflow and help you identify where AI could create lasting value in your organisation.
This is a sponsored session. Badge scanning is optional. By having your conference badge scanned, you are opting in to be contacted by the sponsor. You will be subject to the sponsor’s communications and privacy policies and must contact them directly if you later wish to change your preferences.
Marvin Kotlo (Communardo Group GmbH)
Real delivery is a team sport — so this advanced, hands-on lab makes you run one, with AI agents as teammates at every step. Working in small teams, you'll start from a goal, brainstorm your own app ideas on a Confluence whiteboard, and use AI to shape them into a working PRD.
From there, you'll move into Jira, where each team member owns a piece of the build. Agents help you break down the work and generate your first version live. Here's what sets this lab apart: instead of typing your revisions, you'll record them with Loom's AI capture feature, turning spoken feedback into actionable change requests the agents implement. Together, you'll test, fix, and refine — taking your app through a complete agentic delivery lifecycle across the Teamwork Collection. In just 90 minutes, you'll go from a blank canvas to a fully functional, working app.
In this hands-on lab, developer and engineering teams work in small groups to go from a blank Confluence whiteboard to a fully functional app, in 60 minutes, using AI agents as teammates at every step.
You'll brainstorm, shape a PRD with AI, break down the build in Jira with each member owning a piece, and do something no typing prompt can: record your feedback in Loom and watch agents turn your spoken revisions into implemented changes in real time.
- Ideate and spec: brainstorm app ideas on a Confluence whiteboard and use Rovo to shape them into a working PRD without writing a line from scratch
- Build and delegate: break down work in Jira with AI-generated task breakdown, assign ownership across your team, and generate your first working version live
- Revise with your voice: record feedback in Loom's AI capture feature and watch agents implement your spoken change requests across a complete agentic delivery lifecycle
Thomas Hardin (Atlassian), Jovana Dunisijevic (Atlassian)
Industry research shows AI productivity gains have plateaued at 10–15% - because today's tools only optimize the 20% of a developer's day spent writing code. The real bottlenecks are left and right of code: planning, orchestration, review, and operations.
Join Atlassian’s Jovana Dunisijevic, for a hands-on deep dive into the AI-native SDLC. In this workshop, we’ll move past single-player copilots and show you how Atlassian is turning Jira into an AI-native orchestration layer for the entire software development lifecycle.
You'll work with Jira AI Planner - an always-on AI technical architect that helps you rapidly vet ideas, generate estimates, and create work breakdowns - and the Jira Coding Agent, which tackles coding tasks directly from your board without ever switching to an IDE. Then we'll go further: you'll learn how to build custom automations that chain these capabilities together, transforming your Jira board into an agentic software factory where humans set intent and agents execute. We'll also explore the value of AI-powered code reviews - from establishing code standards that AI can seamlessly enforce, to triggering agentic pipelines that autonomously fix issues.
You'll walk away with a working understanding of how to move from single-player copilots to multiplayer AI workflows - where the coordination layer becomes the leverage point.
What you'll learn:
- How AI Planner turns rough ideas into well-scoped, agent-ready work breakdowns
- How to trigger the Jira Coding Agent - development directly from your backlog
- How to build automations that orchestrate human-agent collaboration
- Why intent and context (not code generation) are the moat in an AI-native SDLC
Jovana Dunisijevic (Atlassian), Warren Marusiak (Atlassian)
Operating environments are accelerating, but most leadership practices are still running on industrial-age logic: siloed thinking, static annual plans, and disconnected tools. The result is a growing gap between how fast the world moves and how fast leaders can respond.
This session challenges conventional leadership models and introduces a new framework for leading in complexity. You'll explore how Strategy Collection helps leaders move from reactive firefighting to proactive, connected decision-making with real-time visibility into priorities, dependencies, and the health of their portfolio. Whether you're a CIO, VP of Strategy, or transformation leader, you'll leave with a different lens on your own operating model and practical steps to close the leadership lag.
When a legacy ITSM platform becomes too expensive, too slow, and too rigid, something has to give. In this session, you'll hear how one team migrated 4,000+ users to JSM in just 4.5 months — and what they learned when they realized staying close to old processes wasn't enough. From that foundation, JSM expanded beyond IT into HR, Procurement, Facilities, and subsidiaries across Europe, evolving into a full Service Engineering Platform where CMDB schemas, SLAs, and routing rules are versioned in Git and deployed through CI/CD. This is an honest look at a transformation still in progress — covering real decisions, real tradeoffs, and a clear-eyed path toward AI agents and Infrastructure as Code.
Strategic alignment is only valuable when it drives better decisions. Learn how to leverage Atlassian Strategy Collection to connect strategic objectives, portfolio investments, value streams, delivery execution, and financial outcomes.
Key takeaways:
- Align strategy, funding, and execution across the enterprise
- Gain real-time visibility into value delivery and investment performance
- Empower leaders with actionable insights for portfolio prioritization and funding decisions
Your Agent Is Only as Good as Its Context
Would you let an AI agent act on your behalf if you weren't sure what it actually knows? That's the question every IT and Security leader is quietly sitting with right now, and it's the question this session answers.
We'll open with something most vendors won't show you on stage: the same request, run live, once through a Rovo agent and once through a general-purpose AI assistant, both pulling from the exact same asset data. One of them wins, clearly and immediately, and the reason why is the whole point of this talk. It comes down to what's underneath the agent, not what's inside the prompt. Specifically, whether that data is complete, consistent, and current, the standard Lansweeper was built to deliver and most environments quietly fail to meet.
This isn't a feature demo. It's a working case for a bigger idea: human-AI collaboration doesn't fail because the model isn't smart enough. It fails at the moment a person has to decide whether to trust what the agent just told them, and that decision is only ever as good as the context behind the answer.
If your organization is betting on Rovo, or evaluating whether to, you'll leave this session with a sharper question to ask about every AI initiative on your roadmap: not "what can this agent do," but "is the data behind it complete, consistent, and current enough to trust?" That question is the difference between an agent your team supervises and one your team works alongside.
Key Takeaways
- An AI agent is only as good as the context behind it: data that's complete, consistent, and current enough to actually mean something.
- A live, side-by-side comparison shows what that context standard buys you in speed and reliability, not just in theory.
- Attendees leave with a sharper evaluation question for their own AI initiatives: does this agent have the context to be trusted with the answer?
This is a sponsored session. Badge scanning is optional. By having your conference badge scanned, you are opting in to be contacted by the sponsor. You will be subject to the sponsor’s communications and privacy policies and must contact them directly if you later wish to change your preferences.
Gary Blower (Lansweeper)
What does it actually look like to deploy AI agents across a large-scale engineering organization — and what happens when it works? In this session, practitioners from a leading financial services organization in Eastern Europe pull back the curtain on how they're using Atlassian Rovo Agents to transform operations across Incident Management, Problem Management, SDLC, and Portfolio Management.
You'll walk away with concrete examples of how AI agents can help incident responders gather context faster, surface related changes, support root cause analysis, improve requirements quality, and deliver actionable portfolio insights — all without adding process overhead. More importantly, you'll hear the unfiltered story: the governance decisions they had to make, the adoption challenges they didn't expect, and the lessons that only come from running this at scale across 150+ agile teams and hundreds of business systems.
Giga Shubitidze (TBC Bank), Giorgi Tsitskishvili (TBC Bank), MaSonya Scott (Atlassian)
