AI-driven lab design is fundamentally changing how research facilities allocate space, deploy infrastructure, and plan future growth. As artificial intelligence becomes embedded across discovery, analytics, and automation workflows, laboratories are achieving higher output with smaller physical footprints.
According to JLL’s latest analysis, AI in life sciences is not only accelerating research timelines but also forcing organizations to reassess long-standing assumptions about laboratory space utilization and facility requirements.
AI-driven lab design changes laboratory space utilization
Laboratories adopting AI-driven workflows are using significantly less space per employee than traditional research organizations. AI-first life sciences companies now operate with approximately 25 percent less space per full-time employee, driven by increased computational work, automation, and reduced reliance on large wet lab footprints.
This shift alters laboratory space utilization patterns, increasing demand for dry lab environments that support data science, computational biology, and AI model development, while reducing reliance on traditional bench-intensive layouts.
Infrastructure demands rise as AI in life sciences expands
While overall space needs decline, infrastructure demands are increasing. AI in life sciences requires robust power capacity, enhanced cooling, high-performance computing environments, and data-intensive connectivity. Robotics and automated laboratory systems further increase floor load and power requirements.
For lab managers, this means balancing space efficiency with infrastructure resilience. Facilities designed without adequate electrical, computational, and environmental capacity risk limiting future AI deployment.
Planning implications for laboratory managers
AI-driven lab design requires a proactive approach to facility planning. Laboratory managers should:
- Reevaluate wet lab versus dry lab ratios
- Plan for scalable power and data infrastructure
- Align space design with automation and AI-enabled workflows
As AI in life sciences continues to mature, laboratories that adapt their space strategies early will be better positioned to support evolving research models and funding expectations.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
