Atlassian Cloud Migration at Enterprise Scale

For many organizations, moving to Atlassian Cloud is no longer a question of if, but when. Cloud migration promises reduced infrastructure overhead, faster innovation, built‑in security, and access to modern capabilities like automation and AI. For small teams, the transition can feel relatively straightforward. For large teams and enterprises, however, Atlassian Cloud migration is a very different experience. It requires more than just data transfer.

Large organizations operate across departments, regions, time zones, and regulatory environments. They often carry years of configuration, custom workflows, and ingrained ways of working. When these environments move to the cloud, teams frequently discover that migration alone does not solve their underlying challenges.

This post walks through the most common problems large teams face during Atlassian Cloud migration, why these issues occur, and what organizations can do to avoid turning migration into a stalled transformation.

The Reality of Migrating Atlassian Cloud in Large Teams

Atlassian Cloud is designed to support enterprise scale, but scale amplifies complexity. Different teams migrate at different speeds. Some embrace new features immediately, while others attempt to replicate Data Center behaviors in the cloud.

As a result, organizations often end up with:

Inconsistent Jira project structures
Fragmented Confluence spaces
Confusing permission models
Limited visibility across teams
Frustration that “cloud didn’t fix anything”

These problems are not caused by Atlassian Cloud itself. They are caused by how migration is approached. They are also the most common friction points large teams encounter during Atlassian Cloud migration. Understanding them early helps organizations avoid rework, frustration, and stalled adoption. With this, they are in a much stronger position to realize the benefits of the cloud.

An illustration showing disconnected tools, with a team trying to piece it all back together

1. Treating Migration as the End Goal

One of the most common and costly mistakes large teams make is treating an Atlassian Cloud migration as a finish line rather than a starting point. When migration is approached as a one‑time IT project with the sole goal of “getting to the cloud”, organizations often miss the real opportunity for improvement.

This lift‑and‑shift mindset focuses on moving data as quickly as possible, not on improving how teams work. Once users can log in and systems are technically live, the project is declared complete. In reality, this is when the most important work should begin.

In lift‑and‑shift migrations, large teams often carry over:

Overly complex Jira workflows
Hundreds of unused or poorly defined custom fields
Outdated Confluence space structures.

When these configurations are moved unchanged, organizations simply recreate broken processes and messy data in a new environment. Instead of feeling faster and more intuitive, the cloud instance feels harder to manage, slower to navigate, and more frustrating for users.

Migration should be followed by simplification and redesign. Without intentional post‑migration cleanup and optimization, teams fail to take advantage of cloud‑native capabilities such as improved automation, simplified administration, and modern collaboration patterns.

The goal of an Atlassian Cloud migration is not just to reach the cloud. The real goal is to improve business agility, strengthen security, and enable teams to move faster with less friction. Migration is only the vehicle to get there, not the destination itself.

2. Lack of Standardization Across Teams

Atlassian Cloud gives teams flexibility by design. That flexibility is one of its biggest strengths, but without clear guardrails, it quickly turns into fragmentation in large organizations.

During cloud migration, Atlassian tools are often rolled out team by team. Each group configures projects, workflows, fields, and spaces to fit its immediate needs. Over time, this leads to:

Different ways of tracking the same kind of work
Inconsistent naming and field usage across teams
Conflicting workflow logic and approval steps
Dashboards and reports that cannot be compared

Teams struggle to collaborate because they no longer share a common structure or language. Leaders lose confidence in reporting because metrics mean different things in different contexts. Basic questions such as what’s blocked, what’s at risk, or how work aligns to priorities, become difficult to answer without manual effort.

This is a common failure point in large Atlassian Cloud migrations. Teams feel empowered locally, but the organization as a whole feels less aligned. Successful cloud migrations balance team autonomy with shared standards.

Standardization, such as common work hierarchies, shared reporting fields, and consistent space structures, creates a foundation that supports scale without slowing teams down. When standards are intentional and well‑communicated, teams move faster together instead of drifting apart.

An illustration representing overwhelm due to overly complicated processes.

3. Permission Sprawl and Governance Gaps

As more users and teams migrate to Atlassian Cloud, permissions become harder to manage.

Large teams frequently encounter:

Users with too much access
Teams blocked by missing permissions
Inconsistent access across projects
Increased security and compliance risk

In regulated environments, these issues are even more serious. Without a clear governance model, teams may work around restrictions, creating shadow processes and data exposure risks.

Atlassian Cloud provides strong governance tools, especially when combined with Atlassian Guard. However, these tools require clear ownership, documented access policies and ongoing review and adjustment.

Cloud migration without governance planning often results in either over‑restriction or uncontrolled access, both of which slow teams down.

4. Information Overload Without Context

Cloud migration increases the amount of visible work, but visible work without structure leads to overload.

After migration, large teams often generate:

More Jira issues
More Confluence pages
More comments and updates
More recorded meetings

Yet despite all this information, teams still struggle to find what they need. The issue isn’t missing information, it's disconnected information. Decisions live in one place, work happens in another, and updates are scattered elsewhere. Over time, documentation loses relevance and context gets rebuilt manually in meetings and messages.

Atlassian Cloud works best when information is deliberately connected. Work should link to the documents that explain it, and updates should live close to the work they describe. Without those connections, teams spend more time searching than executing.

An illustration depicting connection and collaboration among members in a team

5. Underusing Automation and AI

Atlassian Cloud introduces powerful automation and AI capabilities, but many large teams fail to use them effectively.

Common reasons include:

Fear of breaking workflows
Unclear use cases
Concerns about data security
Poor underlying data quality

As a result, teams continue to rely on manual effort for routine tasks like triaging work, writing repetitive updates, and searching for context across tools.

AI becomes useful only when strong foundations exist. When work and knowledge are centralized and governed properly, AI tools like Atlassian Rovo can dramatically reduce this overhead. Rovo can surface context across Jira, Confluence, and Loom, helping teams find answers faster and reduce rework.

AI adoption succeeds only when foundations are in place.

How Large Teams Can Avoid These Problems

Successful Atlassian Cloud migration for large teams requires intention beyond technical execution. Migration should be treated as a structured change in how teams work, not just where systems run.

Practical steps include:

Plan for post‑migration optimization, not just data transfer.
Migration should include time and ownership for simplifying workflows, removing unused configurations, and adapting to cloud‑native capabilities after go‑live.
Define shared standards while preserving team flexibility.
Establish common structures for tracking work, reporting, and documentation, while still allowing teams to tailor workflows to their needs.
Establish governance early, especially for permissions and security.
Clear ownership, access models, and security policies help prevent permission sprawl and reduce risk as more teams adopt the platform.
Connect work and knowledge, not just store it.
Link active work, decisions, and documentation so teams can easily find context instead of searching across disconnected tools.
Introduce automation and AI gradually, with clear goals.
Start with well‑defined use cases once data is clean and structured, then expand automation and AI adoption as teams gain confidence.

Cloud migration should be treated as an opportunity to simplify, align, and modernize how teams work.

How Atlas Bench Helps Large Teams Succeed in Atlassian Cloud

As an Atlassian Platinum Solution Partner with deep Cloud specialization, Atlas Bench helps organizations move beyond migration and fully transform how they work on Atlassian Cloud.

We support large teams by:

Simplifying complex Jira and Confluence environments
Establishing scalable standards and governance
Improving visibility and reporting across teams
Enabling automation and AI responsibly
Aligning Atlassian tools to real business workflows

If you’re preparing for migration, mid‑transition, or struggling after go‑live, we help teams turn Atlassian Cloud into a connected system of work.

Interested in learning more? Connect with Atlas Bench to book a complimentary consultation with our Certified Atlassian Experts.

Start transforming collaboration today with Atlassian Cloud. Sign up now to help your team work better together.

Atlas Bench is an Atlassian Platinum Solution Partner with deep Cloud Specialization, helping Data Center customers move beyond migration and fully transform on the Atlassian Cloud Platform. We deliver Atlassian’s System of Work end to end through four collections: Strategy, Teamwork, Service, and Software, connecting planning to execution, aligning teams around outcomes, modernizing service delivery, and accelerating software development.

We are also recognized as an Atlassian World Class Software Development Partner of the Year (Americas), and we bring AI leadership to every engagement. By leveraging Rovo for automation and purpose built agents, we reduce manual work, unlock knowledge across tools, and speed up decision making so organizations realize measurable value faster in cloud.

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