
Miro is an AI-powered visual workspace that helps teams collaborate, develop strategies, design products, and manage processes throughout the innovation lifecycle. Founded in 2011, Miro has grown into a global technology company with 1,600+ employees across 13 regional hubs. Today, more than 100 million users across 250,000 organizations use Miro to collaborate and bring ideas to life.
With employees and teams distributed around the world, Miro needed a learning environment capable of supporting a growing global workforce while providing consistent access to employee learning information. As part of its learning technology modernization, Miro transitioned historical learning and user data from its legacy learning environment into Docebo; however, moving the data was only part of the opportunity, Miro also needed a scalable way to connect its learning environment with the processes and systems surrounding it.
Moving from a legacy learning management system to Docebo meant transferring valuable historical information accumulated within the previous platform. User information, learning histories, enrollment data, and completion records needed to remain accessible after the transition. For an organization with more than 1,600 employees distributed across 13 global hubs, manually rebuilding that information would introduce unnecessary administrative work and migration risk.
The transition presented several challenges:
Simply implementing a new LMS wouldn't solve those challenges. Miro needed an approach that combined data migration, system integration, and workflow automation to create a learning environment capable of supporting continued growth.
Quandary helped establish a centralized learning environment in Docebo while using Workato-powered automation capabilities to create a more connected and scalable learning ecosystem, the project combined structured legacy-data migration with automation designed to reduce the manual processes surrounding learning administration.
The first step was identifying the user and learning information that needed to move from Miro's legacy learning environment. Existing records were assessed and mapped to the corresponding structures within Docebo. Historical learning information could then be transformed and standardized before being migrated into the new platform. This approach helped preserve relationships between learners and their historical training activity rather than requiring administrators to manually recreate records.
Workato provided the integration and automation layer supporting the Docebo environment. Using Workato's workflow automation capabilities, processes could be triggered by events and data changes rather than relying exclusively on administrators to move information between systems manually. This created the foundation for a more connected learning ecosystem where information could flow between Docebo and the applications surrounding it.
Instead of treating every LMS process as an isolated administrative task, automation could orchestrate repeatable workflows. Workato recipes could monitor for events, trigger downstream actions, transform information, and update connected applications automatically. That architecture helped shift learning administration away from repetitive manual processes and toward event-driven workflows capable of operating consistently at scale.
The automation layer also gave Miro greater flexibility as its learning ecosystem evolved. Workato supports pre-built and universal connectors, including HTTP-based connections, providing a path for connecting additional enterprise applications and APIs as requirements change. Rather than creating isolated point-to-point integrations for every new requirement, the organization could build on a reusable automation framework surrounding Docebo.
Miro established a centralized Docebo environment capable of supporting learning across a global workforce of more than: 1,600 Employees. Historical learning information could move forward with the organization instead of being left behind in legacy technology.
The modernized environment provides a scalable foundation for an organization operating across: 13 Global Hubs. Centralizing learning information helps create greater consistency and accessibility for a workforce distributed across countries and time zones.
Combining Docebo with Workato-powered automation created opportunities to replace repetitive administrative processes with automated workflows and instead of requiring employees to manually coordinate every step between systems, events within the learning ecosystem could automatically trigger the appropriate actions and data movement.
The project established more than a destination for historical LMS data. It created an architecture capable of supporting continued automation and integration as Miro's learning technology requirements evolve. New workflows, applications, APIs, and data sources can be connected through the automation layer rather than requiring administrators to manage disconnected processes manually.
LMS modernization isn't simply about replacing one learning platform with another. For a global technology company like Miro, the larger opportunity is creating a learning ecosystem that can scale alongside the organization. By migrating historical learning information into Docebo and incorporating Workato-powered automation, Miro established a foundation for more connected, automated learning operations.
Instead of relying on disconnected systems and repetitive administrative processes, Miro gained a learning environment designed around centralized data and scalable automation and the result is a foundation capable of supporting the organization's learning needs today while providing the flexibility to integrate and automate additional processes as those needs evolve.
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