The life sciences sector—once a beacon of stability in commercial real estate (CRE)—is now facing a new challenge: oversupply. After years of rapid expansion, a glut of lab and research space has left many properties sitting vacant. As landlords and developers reassess how to position these assets, life sciences companies are turning to artificial intelligence (AI) as a strategic lever to increase operational efficiency, streamline research, and enhance their value within the CRE landscape.
The Oversupply Challenge
Fueled by a surge in demand, life sciences real estate experienced a construction boom. Purpose-built lab spaces and research facilities popped up in biotech hubs across the U.S., from Boston and San Diego to the Research Triangle.
But as market dynamics shifted, supply began to outpace demand, leading to higher vacancy rates and downward pressure on rental growth.
According to a report by Cushman & Wakefield, this imbalance is reshaping how life sciences firms and CRE stakeholders approach space planning, investment, and leasing strategy. Tenants are becoming more selective, and owners must now compete not only on location but also on functionality, flexibility, and technological integration.
The result? Increased competition and a renewed urgency to differentiate through smarter, tech-enabled solutions.
AI: A Strategic Response
To adapt, many life sciences organizations are embracing AI to optimize operations and better position their spaces within a competitive CRE market. Here’s how:
- Optimized Space Utilization:
AI analyzes usage data to help repurpose or reconfigure lab spaces to better meet evolving R&D needs. This not only improves internal workflows, but also helps property owners tailor their offerings to what modern tenants are truly looking for—versatile, data-informed lab environments. - Accelerated Research and Development:
AI is reshaping drug discovery. Platforms powered by machine learning can predict protein structures and molecular interactions, dramatically reducing the time and cost of R&D.
➡️ For example, Alphabet’s Isomorphic Labs uses AI to model protein structures, unlocking faster drug development pipelines. - Enhanced Decision-Making:
From tenant targeting to capital planning, AI helps stakeholders—whether on the tenant or landlord side—make smarter, faster decisions based on real-time market data and predictive insights. This is particularly crucial in today’s oversupplied CRE environment, where data-driven agility can make the difference between filled and vacant space.
Real-World Applications
AI adoption in life sciences is far from theoretical—it’s already making a measurable impact across the U.S. and beyond:
- University of Pennsylvania
Penn secured a $7 million award from the Advanced Research Projects Agency for Health (ARPA-H) to develop AI systems aimed at improving treatment for diseases like breast cancer. This initiative brings together Penn Engineering and Penn Medicine to create transparent AI tools for personalized medical treatment, highlighting how advanced infrastructure and AI innovation can coexist within CRE environments built for cutting-edge research. - Omada Health
Based in the U.S., Omada Health is rolling out OmadaSpark, an AI-powered nutrition platform. Designed to educate and motivate patients through personalized “nutritional intelligence,” the tool is part of Omada’s broader strategy ahead of its IPO. Their growing footprint reflects how digital health companies may influence future CRE needs, seeking dynamic, hybrid-ready office and lab space optimized for both human and machine-driven workflows.
Looking Ahead
AI isn’t just a short-term solution to a real estate problem—it’s a long-term strategy for innovation and resilience. As CRE owners and life sciences tenants align more closely around flexible space, operational efficiency, and futureproofing, AI is emerging as a shared driver of value.
By integrating AI into core operations, life sciences firms are not only better positioned to weather CRE market fluctuations but also accelerating discovery, improving outcomes, and creating more agile, data-driven work environments.
The future of life sciences belongs to those who combine cutting-edge research with cutting-edge technology—and AI is leading the way.
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