How Liferay built a unified, AI-ready service management ecosystem with META-INF

Liferay is a global provider of enterprise digital experience platform (DXP) solutions, helping organizations create connected experiences for customers, employees, and partners at scale. Operating across multiple regions and serving enterprise customers in industries such as financial services, manufacturing, healthcare, and technology, the company manages highly complex support and operational environments where service quality and operational efficiency directly influence customer satisfaction.

As the organization continued to grow globally, Liferay recognized that its existing support environment could no longer sustain the operational integration, scalability, and future innovation the business required. The company needed a modern service management ecosystem that could connect customer support, engineering, IT, HR, and internal operations within a unified platform while also preparing the organization for the next generation of AI-assisted service delivery.

To support this transformation, Liferay partnered with META-INF, an Atlassian Double Platinum Partner specialized in enterprise service management and complex Jira ecosystem implementations. Together, the two companies designed and implemented a centralized, AI-ready operational environment based on Jira Service Management.

The initiative was far more strategic than a standard platform migration. While the immediate objective was to replace the organization's existing customer support platform, the broader goal was to build a scalable operational foundation that would support future growth, improve cross-functional collaboration, increase visibility across global operations, and enable AI-driven process automation in the years ahead.

Before the transformation, customer support processes ran primarily on a standalone customer support platform, while engineering and internal operations teams worked in Jira-based environments. Although functional on a basic level, the separation between platforms increasingly created operational friction as the organization scaled. Support engineers frequently needed to move between systems during investigations, customer and subscription data existed across disconnected environments, and operational visibility remained fragmented across teams and business functions.

This fragmentation became particularly challenging as Liferay began evaluating the future role of AI within enterprise service management. The company quickly identified that the effectiveness of AI-driven operations depends not only on the capabilities of AI models themselves, but on the quality and accessibility of operational context surrounding tickets, customers, assets, workflows, and organizational knowledge.

During the evaluation process, Atlassian’s long-term product vision emerged as a critical differentiator. Compared to traditional service desk platforms, Jira Service Management offered significantly deeper integration with engineering operations, stronger automation capabilities, and a more mature roadmap for AI-assisted workflows powered by Atlassian Rovo. For Liferay leadership, the decision was ultimately about choosing a strategic operational platform that could support the company’s future direction rather than simply solving current ticketing requirements.

“When we compared AI roadmaps, Atlassian’s vision clearly outpaced our previous ITSM solution, making Jira Service Management the strategic choice for our future.”

META-INF played a central role throughout the transformation. Beyond the technical migration itself, the project required redesigning global support processes, standardizing workflows, implementing automation architecture, optimizing request management, integrating asset management capabilities, and aligning multiple operational teams around a shared service management model.

The migration to Jira Service Management was implemented in multiple phases to minimize disruption and ensure business continuity. One of the key operational improvements involved introducing a centralized “General Request” model that standardized request handling globally while supporting incidents, escalations, internal operational requests, and customer-facing support interactions within a single environment.

As implementation progressed, Jira Service Management evolved into a unified operational backbone supporting customer support, IT operations, HR workflows, engineering escalations, and internal service processes. This consolidation significantly improved collaboration between support and engineering teams, particularly because engineers no longer needed to operate across disconnected platforms during issue resolution.

The integration of Jira Service Management directly into the broader Atlassian ecosystem substantially reduced context switching and accelerated resolution workflows. Instead of moving between separate support and engineering tools, teams could collaborate within a single shared operational environment, improving efficiency and responsiveness.

This operational impact was immediately visible to the teams involved:

“In our previous setup within the previous support environment, engineers jumped between tools; with Jira Service Management integrated into Jira, they stay in one environment and resolve issues faster.”

At the same time, the implementation of Assets and centralized operational data management established a far more reliable source of truth behind customer interactions. Customer records, subscription information, service requests, and operational context became significantly more transparent and accessible across teams. This not only improved day-to-day support operations but also provided leadership with a clearer global overview of operational performance and service-related issues across the organization.

For Liferay, this operational transparency became one of the project’s most important business outcomes:

“By centralizing customer and subscription data in Jira Service Management and Assets, we finally have a reliable source of truth behind every support interaction.”

One of the most significant aspects of the project was the smoothness of the migration itself. Through careful planning, phased rollout strategies, process alignment, and close collaboration between Liferay and META-INF, the organization completed the transition with minimal operational disruption. The migration demonstrated the maturity of both the implementation approach and the underlying Atlassian platform, particularly in enterprise-scale environments where uninterrupted service continuity is critical.

The success of the rollout was reflected in the simplicity of the go-live experience itself:

“Our migration from our previous ITSM solution to Jira Service Management was so quiet that my phone never rang, which, as a leader, is the clearest sign it went right.”

Even during the early stages of adoption, Liferay began seeing measurable operational improvements. Engineering collaboration accelerated, operational transparency improved, and several repetitive support activities became candidates for automation. The organization also established a scalable foundation for future AI-assisted workflows, enabling routine ticket handling and repetitive operational tasks to be increasingly automated. At the same time, human expertise focuses on higher-value customer interactions.

This AI-readiness became one of the project’s most strategically important outcomes. Rather than approaching AI as a standalone technology initiative, Liferay viewed it as a capability that must be deeply integrated into operational processes, workflows, and organizational knowledge structures. By centralizing operational context within Jira Service Management, the company created the conditions necessary for AI to deliver meaningful business value in the future.

The organization is already seeing the potential of combining Jira Service Management with AI-driven operational support:

“Even in the early days of our implementation, we’re already seeing how Rovo and Jira Service Management can automate routine ticket work and free up our most experienced engineers to focus on complex, high-value customer problems.”

This long-term vision extends beyond automation itself. Liferay sees AI as a way to elevate customer-facing teams toward more strategic, consultative work while reducing the operational burden of repetitive service activities.

“Our long-term vision is Rovo and Jira Service Management working together so AI handles the basics and our agents can concentrate on strategic customer guidance.”

Jira Service Management underpins multiple business functions across Liferay, including external customer support, internal IT operations, HR services, and engineering collaboration. The organization now operates with significantly improved visibility, stronger process consistency, and a more scalable operational model that supports future growth and automation initiatives.

Today, the Liferay project serves as a strong reference deployment for organizations looking to modernize enterprise service management while preparing for AI-enabled operations. The collaboration between Liferay and META-INF demonstrates how Jira Service Management can successfully replace legacy support platforms while delivering measurable operational improvements and creating a scalable foundation for future growth.

The collaboration between Liferay, META-INF, and Atlassian highlights how organizations can successfully move beyond fragmented support environments toward a connected, future-ready service management model that delivers measurable operational improvements while establishing the foundation for long-term innovation and scalability.