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January 08.2025
2 Minutes Read
Atlassian's Future: Connect's Sunset and the Rise of Forge for Developers
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02.23.2026
Building Trust in AI Adoption: Rachel Shepard's Insights from Atlassian
Update Understanding Trust in AI Adoption In the rapidly evolving landscape of artificial intelligence (AI), trust is emerging as a critical factor that influences user adoption. Rachel Shepard, an AI design leader at Atlassian, emphasized this point during her presentation at the World Summit AI. With the increasing prevalence of AI tools like Rovo, understanding how to build trust with users becomes paramount. Breaking Down Complex Systems The challenge lies in effectively introducing AI agents without overwhelming users. Shepard led a design sprint at Atlassian aimed at addressing this issue. She questioned whether the AI features being created truly aligned with users' expectations and mental models. Rachel's experienced insight suggests that when AI tools are perceived as overly complex or inconsistent, they foster skepticism, deterring potential adoption. Trust-Driven Design Principles Shepard highlighted several key design principles that underpin successful AI integration. First and foremost, meeting users where they are is vital. This means designing AI systems that resonate with user experiences, thereby reducing cognitive load. In replacing traditional personified agents with simpler "Skills," the Atlassian team created a user-focused approach where capabilities are seamlessly incorporated into daily workflows. This shift not only simplified the user experience but also reduced anxiety around using AI tools. Beyond Agents: A Skill-Based Approach By dissolving the concept of agents into manageable Skills, users can now access functionalities that cater to their immediate needs without the clutter of unnecessary choices. This results in an increase in feature utilization, highlighting the efficiency of presenting AI capabilities in a direct and intuitive manner. Shared Knowledge Drives Success The establishment of a shared skills registry allowed different teams within Atlassian to effectively utilize and share these Skills. This framework not only organized resources but also facilitated easier access to various AI capabilities, further removing barriers to user trust. As Rachel Shepard noted, creating a less rigid and more accessible AI environment invites greater user engagement. The Importance of Transparency Complementing Shepard’s insights, principles from the broader dialogue surrounding responsible AI systems emphasize transparency. Many industry leaders advocate for clear visibility into AI decision-making processes, allowing users to understand how AI-generated recommendations come to be. Such clarity reinforces trust and aids in overcoming adoption hurdles. Conclusion: The Path to Trustworthy AI As organizations look to integrate AI, the lessons discussed by Rachel Shepard and supported by industry-wide frameworks stress the importance of trust. By aligning AI features with user expectations, deploying transparent practices, and focusing on making AI accessible, companies can foster an environment where AI becomes a reliable partner in productivity. Cultivating trust is not just about avoiding risks but about setting the stage for successful AI adoption across diverse user bases.
02.22.2026
Catch Performance Regressions Early in Jira Cloud: Enhance User Experience
Update Unraveling Performance Regressions in Jira Cloud In the dynamic environment of Jira Cloud, performance regressions can often go unnoticed until they impact user experience. A minor change made by developers can have varying effects across the platform, especially with millions of active tenants utilizing unique configurations and datasets. This makes catching regressions—a scenario where the performance of an application deteriorates compared to its historical levels—an intricate endeavor. The Challenge of Multi-Tenancy Jira Cloud caters to a diverse range of users, each with distinct traffic patterns, data structures, and integrations. This variability significantly influences how performance regressions manifest. For example, a harmless update might disrupt service for 0.01% of tenants, leading to severe latency issues for a handful of users while remaining undetected in broader performance metrics. Why Conventional Metrics Fall Short Many performance monitoring tools rely on aggregate data, assessing metrics like Service Level Objectives (SLOs) at a high-level overview. This approach grossly oversimplifies the problem, masking regressions that could materially affect large enterprise customers. Hence, Jira’s engineering team has developed a system that focuses on per-tenant, per-endpoint metrics. This allows teams to receive alerts tailored to specific regressions affecting targeted user groups. Leveraging Advanced Analytics for Detection The innovative system in place utilizes statistical process control techniques to monitor each endpoint with precision. Instead of relying solely on global alerts, the framework examines individual performance histories. This method has proven effective—recent months have seen prompt identification and resolution of multiple production regressions. All this is supported by a robust data analytics engine that dives deep into operational metrics. Automated Root Cause Analysis: The Game-Changer To further streamline the mitigation process, Atlassian has integrated AI-driven root cause analysis (RCA) through their Rovo Dev CLI tool. This cutting-edge technology autonomously queries performance data and identifies changes in the codebase causing regressions, significantly reducing the time engineers spend diagnosing issues. In a recent alert concerning latency spikes due to a feature flag rollout, the RCA agent was able to pinpoint the cause in no time, allowing for swift action before the problem escalated to customer complaints. The Future of Performance Management As Jira continues to scale and evolve, the monitoring and management of performance regressions will increasingly rely on automation and sophisticated analytics. By leveraging a combination of tenant-specific monitoring, automated RCA, and refined alert systems, Atlassian aims to enhance the user experience across all levels of their platform, ensuring any arising issues are swiftly addressed. In an era where agile development is paramount, maintaining the balance between rapid iterations and stable performance is crucial. With these innovative measures in place, Jira not only addresses past challenges but is also well-equipped for future scalability.
02.20.2026
How Datasite Achieved Agile Collaboration by Cutting Meetings with Loom
Update Redefining Workplace CollaborationIn the modern workplace, the challenge of maintaining productivity amidst a flurry of meetings is a familiar battle, and Datasite has discovered an innovative solution. By integrating video communications with project management tools, Datasite has managed to cut more than 4,000 meetings in just five months, achieving significant time savings and enhancing workplace culture.At Datasite, the implementation of tools such as Loom, Jira, and Confluence has created a new paradigm for communication. Traditionally, the company's reliance on meetings blurred the lines between productivity and time-consuming discussions, leading to overlapping schedules and diminished focus. However, with the introduction of asynchronous video updates, employees were empowered to share project updates and collaborate without the constraints of scheduled meetings.Embracing Time EmpathyOne of the remarkable shifts at Datasite is the cultural transformation towards 'time empathy', as described by JR Harrell, EVP of Product Operations and Enablement. This cultural ethos encourages teams to prioritize asynchronous communication, allowing them to communicate effectively while preserving time for deep work. This shift not only alleviates the clutter of meetings but also promotes a healthier work-life balance.The benefits are tangible; with over $500,000 reclaimed in lost meeting time, employees can now engage in more meaningful and impactful work, fostering both innovation and efficiency.The Power of IntegrationThe seamless integration of Loom with Jira further enhances project clarity and collaboration. Teams can now provide context through video explanations directly within Jira tasks, allowing colleagues to grasp complex concepts without unnecessary delays. Asynchronous tools like Loom have also shown to expedite bug resolutions and project updates by adding rich, visual information that enhances understanding in a fraction of the time a traditional meeting would take.As organizations continue to adapt to hybrid work environments, leveraging tools that reduce meeting fatigue and enhance clarity will be essential. The successful transition at Datasite serves as a compelling case for others grappling with similar issues in maintaining productivity across dispersed teams.
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