Design Platform Achieves 65% Daily AI Adoption with Claude Across 5,000 Employees

Canva, a software leader in design and collaboration, faced the challenge of rapidly scaling AI capabilities across its 5,000-person organization while maintaining flexibility for different team needs. Rather than adopting a single AI vendor, the company deliberately created a multi-vendor AI ecosystem to enable experimentation. Within this environment, Claude emerged as the standout choice. As Samantha Garrett, head of Canva's internal AI Solutions team, explained: "We knew it was up to us to give our team the space and time to test, learn and discover what good prompting looks like." Canva implemented Claude for Work to provide enterprise-grade AI capabilities with the governance, security, and reliability their workforce required.

Claude achieved immediate traction, with demand quickly exceeding supply as employees across Canva's teams discovered new use cases. Garrett recalled that employees were literally "begging for Claude accounts." The platform's approachable interface, warm communication style, and enterprise-first architecture differentiated it from alternatives. Engineers provided "overwhelming feedback" about Claude's capabilities in code generation and optimization, while product managers used Claude to create interactive prototypes without specialized design tools. Designers leveraged Claude to simulate user testing of templates, and teams used the Projects feature to maintain design knowledge repositories and ensure brand consistency—enabling non-specialists to express concepts in entirely new ways.

The organizational impact has been substantial. 65% of Canva's workforce now reports using AI daily or often to increase productivity and effectiveness. Internal Slack channels became hubs for sharing AI success stories, with employees regularly posting videos demonstrating their Claude creations. Canva's internal education team, Canva University, developed dedicated "AI school" content to accelerate learning. This cultural shift—from traditional top-down technology rollout to collaborative, experimental adoption—reflects a new era in enterprise AI deployment. Garrett concluded: "It's key to encourage teams to experiment and learn as they go in a space that's changing so quickly. Rather than waiting for all the answers, giving people the space to test ideas and adapt helps us grow with the technology—and shape where it goes next, together."