Laboratories are undergoing some big changes thanks to a common disruptor: artificial intelligence.

Advanced mathematical modelling, computational data analysis and generative design are boosting demand for dry labs, which, as the name implies, differ from so-called wet laboratories, with their use of liquids, chemicals and biological samples.

While dry labs don’t require the same design or infrastructure as wet labs, there are other considerations, such as the need for robust power and HVAC systems to support a higher density of hi-tech equipment.

It means that while repurposing stranded assets to dry laboratories is one possibility, not all buildings are suitable for adaptive reuse.

Dusi highlights quantum computing labs as one example.

“It's one of the most complicated buildings to construct because it demands an almost astronautical like environment with no atmospheric pressure, created by tanks of nitrogen and argon gas,” she explains.

Aside from power requirements, Dusi adds that dry labs may still need substantial load bearing for large or heavy equipment, have deck to ceiling height requirements or vibration considerations.

Project management professionals are now using digital tools and AI to create time and quality efficiencies for more strategic and cost-effective construction of life sciences projects.

AI’s ability to collect, organize and interpret large volumes of information to extract useful insights can help with everything from procurement planning and program scheduling, to monitoring site safety, or improving sustainability.

Cairnes explains how building information modelling (BIM) helps create digital twins for visualization and better planning. “For example, it can detect potential clashes between pipes, ductwork or electrics and structural elements such as beams, which could cause expensive problems further down the line,” he says.

For Dusi, AI’s potential to enhance the overall experience and wellbeing of people working in life sciences laboratories is what’s most exciting. She sees huge potential for AI to simulate various scenarios and create evidence-based design for greater productivity and efficiency.

“By looking at the path of access for the scientists, how many steps it takes between various bits of equipment, how they interact with their colleagues in both wet and dry labs, as well as things like air quality, daylight, we can design and build labs that help researchers achieve key breakthroughs faster,” she says.