Case Study

From Groovy to AI-Assisted Automation: How Solarity Health Modernized Its Atlassian Cloud

How Empyra used AI-assisted engineering to rebuild complex Jira workflows without ScriptRunner Cloud, modernize enterprise automation, and establish a scalable foundation for intelligent teamwork.

Data Center Groovy scripts · ScriptRunner
AI-assisted engineering
Atlassian Cloud
Jira Automation JMWE JMCF Native Jira

Customer

Solarity Health

Industry

Healthcare Technology

Technology Partner

Empyra — Atlassian Platinum Solution Partner

Solutions

JiraJira Service ManagementConfluenceJira AutomationJira Misc Workflow Extensions (JMWE)Jira Misc Custom Fields (JMCF)Atlassian Cloud

Transformation

AI-Assisted EngineeringCloud MigrationWorkflow ModernizationEnterprise Automation
602,772Jira issues
598,316attachments
45active workflows
147ScriptRunner-affected transitions

01

Executive Overview

Artificial intelligence is changing how enterprises build software, automate workflows, and modernize technology. But some of the most valuable AI applications are not customer-facing chatbots or autonomous agents. They are practical engineering solutions to problems that would otherwise require extensive manual development.

Solarity Health's Atlassian Cloud migration presented exactly such an opportunity.

As an AI-enabled healthcare technology company, Solarity relies on sophisticated internal systems to coordinate software development, service operations, customer support, and enterprise knowledge.

Its existing Jira, Jira Service Management, and Confluence Data Center environment had evolved over years of customization. Business-critical operations depended on complex workflows, Groovy scripts, application-specific fields, scheduled jobs, listeners, and Marketplace extensions.

The challenge intensified when Solarity decided not to purchase ScriptRunner for Jira Cloud.

Many existing automations could no longer be transferred or executed in their original form. Empyra needed to preserve the required business functionality while replacing legacy scripting dependencies with supported Cloud capabilities.

Rather than reproducing the entire legacy scripting environment, Empyra used AI-assisted coding alongside experienced Atlassian engineers to accelerate the reconstruction of workflows and automation using Jira's native functionality, JMWE, and JMCF.

The engagement offers a compelling example of how AI can support enterprise modernization—not by replacing engineering judgment, but by helping skilled teams translate legacy technical complexity into maintainable, Cloud-compatible solutions.

02

The Challenge: When Years of Automation Become a Cloud Migration Barrier

Atlassian had become an important part of Solarity's internal operational environment.

Jira

Jira supported software engineering, development coordination, and work tracking.

Jira Service Management

Jira Service Management supported customer and internal service operations.

Confluence

Confluence provided documentation, knowledge sharing, and collaboration.

Over time, Solarity had extended these applications through extensive scripting and workflow customization.

ScriptRunner functionality was embedded in business processes that:

  • automatically routed requests
  • updated information
  • enforced approval requirements
  • calculated field values
  • and triggered subsequent workflow activities

These were not isolated technical conveniences. Many directly supported everyday operating processes.

The migration assessment documented:

602,772

602,772 Jira issues and 598,316 attachments in the source inventory.

29 · 121

29 Jira projects and 121 workflows across the assessed Data Center environment.

45

45 active workflows identified in the detailed ScriptRunner workflow inventory.

79 condition-related, 15 validator-related, and 134 post-function-related transition configurations in that inventory.

condition-relatedvalidator-relatedpost-function-related
147

Approximately 32 automation rules and 147 ScriptRunner-affected transitions identified by Empyra's delivery team for reconstruction and remediation.

Additional dependencies involving Dynamic Forms, Tempo, JMWE, JSU, and Confluence Marketplace applications.

The scale made the migration challenging, but the architectural differences between Data Center and Cloud created the more difficult problem.

A critical decision

No ScriptRunner Cloud

Solarity elected not to continue with ScriptRunner for Jira Cloud.

That decision removed the option of maintaining ScriptRunner as a like-for-like dependency for the existing scripted functionality.

Groovy-based conditions, validators, post-functions, scripted fields, and related automation logic needed to be assessed individually.

1

Some functions could be replaced with native Jira capabilities.

2

Others required JMWE or JMCF.

3

Some needed new automation rules, revised expressions, or different workflow designs.

?

The team also had to determine whether individual legacy functions were still required.

A conventional approach would have involved manually reviewing, rewriting, configuring, and testing a large volume of business logic under a constrained migration schedule.

Empyra saw an opportunity to use AI to support that engineering effort.

03

The Solution: AI-Assisted Workflow Reconstruction

Empyra adopted a targeted modernization approach built around three principles:

01

Preserve business behavior.

Identify what each legacy script or workflow function was intended to accomplish.

02

Use Cloud-supported alternatives.

Replace Data Center-specific implementations with Jira Automation, native workflow capabilities, JMWE, or JMCF where appropriate.

03

Use AI to accelerate coding, not replace technical judgment.

Apply AI assistance during the code translation and reconstruction process while retaining engineering ownership of configuration decisions and functional validation.

This distinction was central to the delivery approach.

AI was not deployed as an autonomous migration agent with unrestricted control over Solarity's production environment.

Instead, it served as an engineering accelerator, helping the team reconstruct the coding and logic portions of existing functions in forms suitable for Atlassian Cloud.

1

Step 1

Understanding what the legacy scripts actually did

Empyra's engineers reviewed existing workflow conditions, validators, post-functions, scripted fields, scheduled jobs, and listeners.

Rather than treating every customization as something that must be migrated unchanged, the team considered its business purpose.

This functional assessment was essential because Cloud automation could often achieve the same outcome through a different technical mechanism.

It also helped identify obsolete or unnecessary logic that did not need to be carried forward.

  • ?Was it enforcing an approval rule?
  • ?Was it updating a field during a transition?
  • ?Was it calculating a value from another field?
  • ?Was it determining which user could perform an action?
  • ?Was it initiating another transition automatically?
2

Step 2

Reconstructing workflow logic with AI-assisted coding

With the functional requirements identified, the team used AI assistance during development of the replacement logic.

The replacement implementations used different approaches depending on the requirement.

Rebuilding automated workflow transitions

Data Center

In the Data Center environment, ScriptRunner fast-track transitions could initiate additional workflow actions automatically.

For example, an asset-management workflow used ScriptRunner to trigger an additional transition when an asset moved out of service.

Cloud

Empyra replaced that dependency with a JMWE transition function in Cloud, preserving the intended sequence of workflow actions without requiring ScriptRunner Cloud.

Recreating dynamic field updates

Data Center

A legacy workflow used a ScriptRunner script to construct an issue summary from asset attributes, including manufacturer and model information.

Cloud

The Cloud implementation used JMWE's Set Issue Fields capability and Nunjucks expressions to reproduce the required field-selection and summary-update behavior.

This required translating not only syntax but the original business intent into the Cloud application's execution model.

Preserving approval and validation logic

Data Center

Several expense and approval workflows used scripted conditions and calculated values to determine whether particular transitions were permitted.

The documented rules included cost-based checks and routing decisions involving approval thresholds.

Cloud

Empyra reconstructed these behaviors with JMWE workflow functions and Jira Automation, enabling the required approval logic to be expressed through Cloud-compatible mechanisms.

Rebuilding conditional approver assignment

Data Center

Certain workflows determined approvers based on the reporter's group membership.

Cloud

The replacement configuration used Jira Automation conditions to evaluate the relevant group and populate the appropriate approver information.

This reduced reliance on legacy ScriptRunner code for a business function that could be supported by native Cloud automation.

Across these examples, the engineering team combined AI-assisted code development with knowledge of Jira workflow behavior, field models, permissions, and supported Marketplace capabilities.

The goal was not to reproduce Groovy syntax line for line. It was to deliver the required operational behavior through supported Cloud implementations.

3

Step 3

Recreating scripted fields with JMCF and native automation

Workflow transitions were only one component of the challenge.

Solarity also used ScriptRunner scripted fields to calculate or display operational information.

These included business priority, issue category, severity, status duration, estimated work, and other values used by service and engineering teams.

Empyra assessed the fields according to their actual business requirements.

JMCF

Dynamic calculated fields were reconstructed using JMCF functionality where suitable.

Native Jira fields

Some historical fields were converted to native Jira fields to preserve existing values without retaining unnecessary runtime scripting.

Jira Automation

Other field behavior was implemented using Jira Automation.

The September 2026 technical review confirmed that scripted-field reconstruction had progressed using JMCF and automation, with additional recalculation and validation activities required for some fields.

This approach illustrates an important modernization principle: the value lies in retaining meaningful business information, not necessarily retaining the original scripting application.

4

Step 4

Validating the reconstructed workflows

Rewriting code is only part of workflow migration.

Differences between Data Center and Cloud can affect transition execution, user permissions, field availability, event ordering, and Marketplace application behavior.

Empyra therefore incorporated the reconstructed logic into

  1. migration testing
  2. Cloud configuration
  3. user acceptance activities
  4. and post-cutover remediation

The team's documentation tracked individual workflow functions and their replacement configurations.

Issues identified during validation were addressed through configuration changes, workflow corrections, and, where necessary, coordination with Atlassian or Marketplace vendors.

This engineering oversight was particularly important because AI-generated or AI-assisted code still needs to be evaluated against the customer's actual business requirements and the constraints of the destination platform.

The advantage of AI-assisted development is not that every generated solution is automatically correct.

It is that engineers can accelerate repetitive translation work while concentrating their expertise on architecture, validation, and exceptions.

04

Beyond Automation: Managing a Complex Enterprise Cloud Migration

The AI-assisted workflow reconstruction was a significant part of the engagement, but it was delivered within a broader Jira, Jira Service Management, and Confluence Cloud migration.

Empyra worked with Solarity to

  1. 1assess application dependencies
  2. 2prepare source data
  3. 3conduct sandbox migrations
  4. 4document compatibility issues
  5. 5and coordinate production cutover activities
14

Dynamic Forms custom fields that were not directly supported in Jira Cloud

Preserving application-specific data

The team identified 14 Dynamic Forms custom fields that were not directly supported in Jira Cloud.

The migration approach involved converting those fields into native Jira field types before migration so their existing values could be preserved.

Associated workflow conditions and validators also needed review because they referenced the original application-specific fields.

68

Confluence spaces tested during the August 2026 dry run

64 spaces transferred4 failures

Handling Confluence knowledge complexity

Solarity's Confluence environment contained content dependent on Marketplace macros, specialized formatting, templates, and application-specific functionality.

During the August 2026 dry run, Empyra tested migration of 68 Confluence spaces.

The initial space migration successfully transferred 64 spaces and identified four failures requiring further remediation. Separate attachment testing also exposed partial failures and service-communication issues.

These results were used to guide further investigation and production preparation.

10,000

worklog threshold exceeded by some source issues

Working through large application-data dependencies

The Jira dry run identified a Tempo migration challenge involving unusually large worklog histories.

Some source issues exceeded a 10,000-worklog threshold, requiring an Atlassian issue-transformation process before the affected Tempo migration activities could continue.

Empyra coordinated with Atlassian and the application vendor to investigate and address the dependency.

These situations highlighted the importance of technical expertise beyond automated migration tooling.

05

A Practical Reporting Proof of Concept

Solarity's reporting environment presented another challenge.

Data Center

Existing engineering analytics and financial reporting processes relied on direct database access to Jira Data Center.

Cloud

The transition to Cloud required a different approach because Jira Cloud does not expose the same database-access model.

Empyra01

Empyra participated in technical discovery and developed a reporting proof of concept to help evaluate how the necessary information could be obtained using Cloud-compatible methods.

Solarity02

After the proof of concept, Solarity elected to develop and own the reporting solution internally.

This represented a practical collaboration model.

Empyra helped explore the technical approach and reduce architectural uncertainty,

while Solarity retained control of its downstream reporting implementation.

The outcome demonstrates how a services engagement can support customer capability-building without requiring every future component to be developed and operated by the implementation partner.

06

Production Cutover and Stabilization

The production Cloud transition was scheduled around the October 16–19, 2026 window.

Empyra and Solarity coordinated detailed runbooks covering

  1. 1source preparation
  2. 2migration sequencing
  3. 3application readiness
  4. 4attachment transfers
  5. 5verification
  6. 6and post-cutover activities

Following the production transition, the teams used a dedicated Jira stabilization board to track operational issues affecting workflows, user permissions, service-management configurations, Marketplace applications, knowledge rendering, and integrations.

Some issues were resolved during the initial stabilization period,

while others required further investigation or vendor involvement.

This reinforced another lesson from the engagement:

complex migrations require continuing attention to the way employees and customers actually use the platform after go-live.

07

The Results: AI as an Engineering Accelerator

Solarity's migration demonstrates several practical outcomes.

Reduced reliance on legacy scripting

By reconstructing required ScriptRunner functionality using supported Cloud capabilities, Empyra helped Solarity proceed without purchasing ScriptRunner Cloud as part of its target automation architecture.

The replacement approach used native Jira functionality, Jira Automation, JMWE, and JMCF according to the needs of each workflow or field.

AI-assisted reconstruction at meaningful scale

The delivery team applied AI-assisted coding to a migration workstream spanning 45 active workflows, approximately 32 automation rules, and roughly 147 ScriptRunner-affected transition touchpoints.

Rather than using AI as a demonstration disconnected from business operations, the team applied it to a real engineering challenge involving production-oriented workflow functionality and a constrained migration window.

A more supportable Cloud architecture

The migration moved required logic away from certain Data Center-specific scripting mechanisms and into supported Cloud functionality.

This created opportunities to

simplify future administration,
reduce unnecessary dependencies,
and make business rules easier to manage within the target platform.

A foundation for continued modernization

The production Cloud transition established the basis for further improvements to

knowledge management,
workflow governance,
service delivery,
integrations,
and the possible adoption of Atlassian's AI capabilities.

These are demonstrated architectural and delivery outcomes. The project has not established a verified monetary savings figure or measured AI-development productivity improvement, so neither is claimed here.

— 08 —

The Bigger Lesson:

AI Doesn't Replace Engineering Judgment.It Expands What Engineers Can Accomplish.

Enterprise AI conversations often focus on

chatbots, knowledge assistants, and autonomous agents.
Solarity's experience illustrates another important application:

using AI inside the engineering process itself.

Legacy workflow modernization requires developers to

  1. understand business behavior,
  2. interpret existing code,
  3. select compatible APIs and application functions,
  4. and verify that the final implementation behaves correctly.
AI

AI can support the translation and reconstruction work.

Engineering expertise

But engineering expertise remains essential for deciding

  • what should be preserved,
  • how it should be implemented,
  • which permissions apply,
  • and when a change is safe to release.

That combination of AI-assisted development and human engineering judgment is what made the approach relevant to Solarity's migration.

09

Looking Ahead: From AI-Assisted Engineering to AI-Enabled Teamwork

With Jira, Jira Service Management, and Confluence operating in a Cloud-based environment, Solarity can evaluate opportunities to extend intelligent capabilities into its internal collaboration and service workflows.

Potential areas include

Rovo-assisted knowledge discovery,
service-management support,
engineering context retrieval,
and more intelligent automation.

Those capabilities represent future opportunities rather than production Rovo outcomes delivered under this migration.

For Solarity, a company whose core business already combines AI and human expertise, the next stage is an opportunity to bring the same thoughtful balance into internal ways of working.

For other enterprises considering similar migrations, the message is practical:

Modernization does not always require carrying forward every legacy application.

With the right architectural decisions, AI-assisted engineering, and human validation, organizations can rebuild the capabilities they need for a more sustainable Cloud future.

10

About Empyra

Empyra is an Atlassian Platinum Solution Partner with expertise in

  • Cloud migration,
  • IT service management,
  • software development,
  • enterprise integration,
  • workflow automation,
  • AI enablement,
  • and managed services.

Empyra helps organizations modernize complex Atlassian environments by combining technical engineering capabilities with practical business-focused delivery.

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