MRI Software’s Transformation: From ServiceNow to Jira Service Management
How they moved from ServiceNow to Jira Service Management, and gained data-backed benefits realised from day 1
Who are MRI Software?
MRI Software’sIT and engineering teams operate at global scale, supporting both internal operations and the continuous delivery of product innovation. However, with IT traditionally operating in ServiceNow and engineering teams working within the Atlassian ecosystem, this created fragmentation in tools, processes, and data—limiting collaboration and visibility across functions.
Requirements at a glance
Shift from tool replacement to operating model transformation
Simplify and standardise core ITSM processes leveraging ITIL best practices
Establish a scalable platform-first foundation
Enable automation and AI
The challenge
Operating across multiple platforms—including ServiceNow for IT and Atlassian tools for engineering—created fragmentation, siloed data, and inconsistent ways of working. This lack of connectivity limited collaboration and reduced operational efficiency.
At the same time, the organisation struggled to realise value from its existing investment. High licensing costs, limited scalability, and gaps in critical capabilities such as CMDB and AI meant the platform was no longer fit for purpose.
MRI recognised that simply migrating tools would not solve these issues. Instead, the organisation needed a full transformation of its service management operating model, placing equal emphasis on people, processes, and technology.
The solution
MRI Software partnered with Adaptavist to deliver a transformation from ServiceNow to Jira Service Management, powered by the broader Atlassian ecosystem.
Rather than a traditional “lift-and-shift” migration, the programme was designed as a clean-slate transformation, reimagining workflows, standardising practices, and embedding organisational change from the outset.
The goal was to create a modern, scalable, and AI-enabled service management capability, aligned to ITIL best practices and capable of supporting MRI’s global operations and future growth.
Phase 1: Onboarding and stakeholder alignment
The project began with identification of key stakeholders who would be impacted the most, and who could champion the transformation.
Executive leadership—including CTO, VP, and IT directors—defined the strategic vision and success criteria, ensuring a unified direction before broader engagement. This top-down alignment created a strong foundation for change.
Adaptavist facilitated steering groups with operational teams, focusing on pain points, expectations, and desired outcomes. Rather than simply gathering requirements, the sessions reframed thinking towards a future-state operating model, aligning stakeholders around a shared transformation vision.
Phase 2: Discovery and design
With clear alignment established, Adaptavist conducted structured discovery workshops and decision sprints to define requirements and the design of the new solution.
This phase prioritised:
- Mapping functional requirements to business needs
- Designing user-centric workflows
- Validating concepts early through UAT and UX mindset
By involving stakeholders throughout the design process, MRI Software avoided common pitfalls such as rework and misaligned expectations. The result was a validated, experience-led design that reflected both operational needs and user experience.
Phase 3: Implementation and validation
The implementation phase followed a value-first approach, delivering incremental functionality and validating it continuously with stakeholders.
A key strategic decision was made not to migrate legacy data from ServiceNow. Instead, MRI Software adopted a clean-slate approach, simplifying processes and avoiding the complexity of historical inefficiencies. Legacy data remained accessible for compliance, but the new platform was built for the future.
Regular feedback loops and UAT ensured the solution remained aligned to business goals, while flexibility allowed for evolving requirements during delivery.
Phase 4: Enablement and adoption
Recognising that the success of a transformation depends on people. A dedicated enablement phase prepared users for the new platform and ways of working.
Adaptavist delivered:
- Role-based and tool-based training tailored to specific user needs
- Online and asynchronous learning materials for a global workforce
- Supported communication plans and execution
- Solution management enablement for ongoing self-maintenance.
This approach enabled scalable, flexible learning, and ensured high levels of adoption across geographically distributed teams. It also embedded new ways of working, not just new tools.
Phase 5: Benefits realised, backed by data
he final phase focused on ongoing optimisation and benefit realisation.
MRI established a robust framework for continuous improvement, addressing cost efficiency by rethinking value and investment, while proactively managing adoption challenges through early stakeholder engagement, structured communication, and targeted user training. The approach also mitigated regulatory and operational risks through carefully designed functional and non-functional requirements, and enabled AI capabilities from day one by embedding integration and utilisation into the core platform design.
During the first week of operation:
50% expansion from 400 to 600 ITSM users at lower cost
50% forecasted reduction in platform costs despite increased users
4.9/ 5 CSAT score on the first week of operation
94.7% Time to resolution SLA met
Over 7735 of alerts obtained <10 min MTTA and 1 hour MTTR
AI enhanced service
AI alert grouping
MRI Software, as a Software development company with an extremely large infrastructure, has a legion of monitoring alerts. Today, they’re using AI alert grouping to improve visibility across the large number of monitoring alerts raised. This allows their NOC (Network Operations Centre) team to identify better underlying incidents from alerts, reducing the time to escalate alerts to actionable incidents.
Service AI agent on the front line
The service agent has been configured to use IT knowledge and optimised agent configuration to:
- Clarify the customer request and prompt them to provide the right information based on the historical data of resolved tickets
- Identify the user's sentiment and adapt behaviour accordingly based on the customer's tone
- Try to deflect the request by providing a knowledge base-based response to enable the customer to self-serve
- Raise a ticket on a customer's behalf as a fallback scenario or if the customer sentiment degrades
AI service agent results in the first week:
- Agents used by an average of 21 users per day
- 14% of 1,200 service requests (166 conversations) handled with 0 human intervention so far
This is an ongoing phase, ensuring the platform evolves alongside business needs and continues to deliver value over time.
Business impact
The transformation delivered immediate and measurable value. By consolidating onto Jira Service Management, MRI reduced costs while scaling its service management capability.
Teams now operate on a single platform, enabling seamless collaboration across IT, engineering, and business functions.
Crucially, the organisation achieved day-one value realisation, with productivity gains and user satisfaction evident immediately after go-live.
Future plans
Stabilise the platform and operating model over the next 6 months
Increase coverage from 80% to 100% enterprise-wide
Continue role-based enablement and training programmes
Expand and mature the CMDB for AI-driven insights
Explore Customer Service Management (CSM) capabilities with Atlassian’s Service Collection
Extend the platform to additional business functions
Ready to embark on your transformation?
Like MRI Software, your organisation deserves to reap the benefits of a successful transformation from ServiceNow to Jira Service Management. Get in touch with our experts today.