Earlier this month at Microsoft Build 2026, Microsoft announced the general availability of Microsoft Discovery. The platform is designed to support the iterative testing cycles common in research and discovery workflows by enabling scientists to build and govern agentic AI workflows across scientific disciplines. Now, in the life sciences sector, Microsoft has announced a collaboration with Ginkgo Bioworks that will allow researchers to design experiments in Microsoft Discovery and execute them directly through Ginkgo Cloud Lab.
Microsoft said Discovery is designed to work within existing research and development environments rather than replace them. By connecting digital research tools with remote laboratory automation, the collaboration aims to make advanced experimental workflows more accessible to organizations without extensive in-house automated infrastructure.
Understanding lab-in-the-loop biological research
The collaboration centers on a concept known as lab-in-the-loop biology, which connects computational models with physical laboratory experimentation. In this approach, researchers use software tools to generate hypotheses, design experiments, and analyze results, while automated laboratory systems execute experimental workflows and return data for further analysis.
Microsoft Discovery and Ginkgo Cloud Lab are intended to create a more integrated workflow by connecting experiment planning with remote laboratory execution. Researchers can design experiments within Microsoft Discovery and submit them directly to Ginkgo's automated laboratory platform, reducing the need to manually transfer information between separate systems.
According to Microsoft and Ginkgo, the integration is designed to help researchers move more efficiently between experiment design, execution, and analysis while maintaining access to existing laboratory and data management workflows.
Expanding access to laboratory automation
For many organizations, building and maintaining automated laboratory infrastructure can require significant investments in equipment, facility space, and specialized expertise. Through the collaboration, researchers may be able to access automated experimental capabilities through Ginkgo Cloud Lab without developing comparable systems internally.
The cloud-based model also offers flexibility for organizations whose experimental needs fluctuate over time. Rather than scaling physical infrastructure, researchers can access laboratory automation services as needed through the platform.
While Microsoft and Ginkgo did not provide performance data related to the partnership, automated laboratory systems are often used to support standardized experimental execution and data collection, particularly in high-throughput research environments.
Connecting AI workflows with laboratory execution
The integration reflects a broader industry trend toward linking AI-driven research tools with laboratory automation. As scientific organizations increasingly adopt AI for experiment design and data analysis, connecting those tools directly to laboratory execution systems may help reduce administrative complexity and streamline research workflows.
For laboratory leaders, the collaboration highlights how cloud laboratory infrastructure and AI-enabled research platforms are continuing to converge. Rather than investing in new automation systems, organizations may gain access to advanced experimental capabilities through partnerships that combine digital research environments with remote laboratory operations.
Microsoft and Ginkgo plan to demonstrate how Microsoft Discovery and Ginkgo Cloud Lab can work together to support lab-in-the-loop biological research, providing scientists with a connected pathway from experimental design to physical execution.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
