The AI-Driven Shift in Lab Design
The landscape of scientific research is undergoing a significant transformation. The rise of artificial intelligence (AI) is shortening discovery timelines, disrupting traditional workflows, and changing the expectations of what laboratories must deliver. Lab design can no longer be reactive or incremental; it must be visionary, flexible, and deeply integrated with the tools of the future.
Historically, laboratory environments were often isolated and designed solely for human and manual testing. These spaces were often functional but uninspired, resembling basement-like setups. Today, laboratories are evolving into dynamic ecosystems where AI, robotics, and human researchers collaborate in real time. While human interaction remains essential for transformative creativity and collaboration, these environments also demand features like natural light, ventilation, and easy access to amenities such as cleaners, cafes, medical offices, gyms, and more.
Moreover, AI-driven research requires specific facilities, including computing labs, automation systems, robotic arms, and other advanced technologies. These changes necessitate a fundamental rethinking of lab planning and infrastructure—not just to meet today’s demands, but to accommodate technologies that have yet to emerge.
AI is the fastest-growing technology in human history. Our scientists have embraced the effectiveness of research and data analytics, finding ways to implement these tools in their work for groundbreaking scientific breakthroughs. This significantly accelerates the time to market for new discoveries. To keep pace with these advancements, lab design and operations must incorporate AI tools, ensuring that laboratories can adapt, renew, and implement new technologies effectively.
Modern laboratories require:
Flexibility for future utility reconfigurations from slab to slab
Dedicated mechanical, electrical, and plumbing (MEP) corridors and raised floors for real-time infrastructure access
Cleanroom-capable shell space for modular technology insertion
A real-life example is the Broad Institute in Cambridge, MA, which incorporated interstitial mechanical floors and flexible lab bays. This design allows laboratory groups to expand or contract without the need to redesign core systems.
“Across nearly two decades, Broad Institute has been a space that brings together the most creative scientists of this generation to collaborate and spark new ideas that drive discovery. With this additional space, our scientists will have the flexibility to take advantage of bold new opportunities and continue to work with our hospital and university partners to transform human health,” says Todd Golub, director of the Broad Institute and a founding core institute member.
Labs are evolving into living platforms, making the use of digital twins for predictive operations and spatial optimization a necessary requirement for future expansion and changes. For instance, the Thermo Fisher Scientific Center of Excellence in South San Francisco was one of the first labs to integrate real-time HVAC and robotic monitoring into their digital twins, enabling predictive maintenance and ultra-low downtime.
The industry is not only shifting to embrace the changes brought about by AI but is also celebrating achievements with awards. AstraZeneca’s Gaithersburg facility was recognized by the US Department of Energy in 2021 for its recertification to in the Superior Energy Performance 50001 (SEP 50001) program and additional Gold-level achievement. The campus was recognized for its efforts toward reducing energy consumption and greenhouse gas emissions by utilizing advanced analytics and machine learning capabilities for continuous HVAC controls and monitoring; implementing optimal start controls on all air handler units (AHUs), resulting in significant energy and cost savings; training facility maintenance personnel on the self-learning optimal start controls, reducing engineering time and labor; and potentially saving over 6,900,000 kWh of electricity and 170,000 therms of natural gas annually through these efforts, equating to around $851,000 per year.
Plan for what’s next, not just what’s now
As OpenAI’s Sam Altman emphasizes, organizations must design for AI’s future impact—not merely its current capabilities. This creates the new requirement for design—flexibility is the new GOLD standard.
How do you anticipate your future space needs, and what does it mean to design for what’s next? Do you have a crystal ball to guide you in making decisions and achieving your goals? While we can’t predict the exact future or identify our specific space requirements, we can incorporate flexibility into our design concepts to allow the space to adapt as needed. Here are a few simple, low-cost practices to ensure some level of agility for future needs:
Universal grid systems that accommodate shifting layouts to allow larger pieces of equipment, robotics and uncommon lab bench sizes and grids
Modular casework and demountable partitions for rapid reconfiguration
Zones for computational infrastructure with scalable cooling, power, and data capacity
Spaces for human-machine interaction, bridging AI interfaces and wet lab workflows
Dual-purpose rooms that support both simulation/modeling and hands-on research
Structural integrity and load bearing for heavy equipment, quantum computing labs, and equipment corridors scientific agility, which is essential in AI-accelerated discovery
These flexible environments reduce the cost of change while supporting pipelines. The supplier market must adapt to accommodate potential changes, allowing for different bench sizes that meet AI and robotics needs. Autonomous planning tools will need to be employed to create flexible workflows and life sciences vendors in the ecosystem need to respond to changes by integrating autonomous design systems into their laboratories and equipment. For example, Opentrons Labworks has developed the Flex Prep robot, which requires no coding skills to operate. Additionally, Genie Scientific produces flexible benches that incorporate robotics into their workflows. There are many more examples of robots, automation, and smart lab technologies becoming available for our scientists.
Infrastructure demands are shifting rapidly
Infrastructure is now a strategic asset for life sciences facilities. The integration of AI, high-performance computing (HPC), and emerging quantum platforms is radically increasing lab infrastructure requirements. Standard HVAC, electrical, and IT systems are no longer sufficient. Labs must be built with a digital-first infrastructure mindset.
When considering your next lab design, keep in mind several important factors: the structural framework of the building, its infrastructure, and overall integrity. Assess the fire hazard and occupancy ratings, the incoming service lines, and the available space for additional equipment and fiber lines. These elements are crucial for a successful lab setup:
Redundant power systems with two to five times the traditional capacity, plus microgrids or backup generators
Advanced cooling systems engineered for data-dense zones
Software-defined networks with 100+ Gbps data handling
Modular data centers and interstitial space for future upgrades
Smart building technologies to optimize energy and mechanical performance
When designing and building the “lab of the future,” which incorporates AI, robotics, quantum computing, digital twins, and smart infrastructure, how can we structure the technical foresight with phased development for optimal financial control over your CAPEX and OPEX investments? How do you build tomorrow's labs today with a strategic blueprint for ROI-driven innovation?
2N redundant power: Implement full UPS systems and backup generators, with isolated power branches for sensitive equipment
Liquid or directed air cooling: Plan for hot aisle/cold aisle separation in labs with embedded computing
Smart grid interface: For high-power facilities, especially those involving quantum or AI computing, integrate with utility demand response systems
Zone power redundancy to avoid overspending in low-risk areas
Strategic equipment selection: Prioritize equipment that is scalable, compatible with Open AI/AI, and energy-efficient
Simulation of lab occupancy: Utilize digital twins to simulate equipment cycles, which will help plan for adequate power loads and optimize energy use
Sustainable energy technologies: Use renewable energy technologies, such as geothermal and solar panels, to maximize power redundancy without overloading the power grid
The race to build next-generation laboratory facilities is intensifying, but the difference between breakthrough success and costly failure lies in strategic implementation rather than technological sophistication. Smart organizations are discovering that building tomorrow's labs requires today's financial discipline through a phased methodology that prioritizes sustainable value creation. The most successful transformations begin with foundational smart infrastructure and energy management systems—unsexy but critical investments that deliver immediate, measurable returns while creating the platform for future innovation. One leading biotechnology company achieved a 32 percent reduction in energy costs within the first year of their smart infrastructure deployment, funding subsequent technology phases while proving the business case for further investment.
This disciplined approach extends beyond technology to encompass capability development, strategic vendor partnerships, formal governance frameworks, and modular system design that accommodates future evolution. Organizations must build internal competencies alongside technology deployment, establish outcome-based vendor relationships, create cross-functional decision-making structures, and design flexible platforms that can adapt to changing requirements. Each phase should deliver measurable value that validates business cases before larger commitments, creating a virtuous cycle where early successes fund future innovation while building organizational expertise. The labs of the future won't be built by those who deploy the most advanced technology first, but by those who deploy the right technology systematically, balancing innovation with execution to create sustainable value while remaining adaptable to whatever the future may bring.
How is automation reshaping space and layout?
The age of isolated benchtop instruments is giving way to integrated robotic workflows, mobile automation units, and intelligent material handling systems.
The modern lab design and lab layouts must support the choreography of humans and machines. The spatial requirements, cooling, lighting, and power infrastructure are very different for the changing user types in the R&D.
While reviewing and planning for an effective automation integration requires some of the following principles:
Dedicated robotics bays with reinforced flooring, utility feeds, and safety zoning—review the load-bearing areas on the floor plates while placing the robotics areas
Ceiling-mounted services to free up floor area for robotic movement
Optimized flow paths for samples, reagents, and people
Strategically centralized equipment zones to improve utilization and return on investment
Ceiling heights and slab strength that anticipate overhead automation or gantry systems
Designing for automation is not about replacing people—it’s about reconfiguring space so both humans and machines can work together more efficiently and safely. Also, automation in an R&D facility today goes far beyond robotic arms and conveyor belts. It integrates AI, robotics, data analytics, machine learning, and IoT (Internet of Things) to accelerate research, reduce errors, improve reproducibility, and free up scientists for high-value tasks.
Here are some activities that are part of the automation process in an R&D Lab:
Automated sample handling: Integration with LIMS (Laboratory Information Management Systems) and ELNs (Electronic Lab Notebooks)
Automated analytical testing
Smart environmental controls-sensors and automated HVAC and lighting systems for optimal temperature, humidity, and air quality (critical in chemistry, biotech, or cleanroom R&D)—integration with building management systems (BMS) for energy optimization and sustainability
AI-driven experimentation-closed-loop systems that use AI to design and execute experiments autonomously; integration of digital twins for scenario modeling and predictive experimentation
Automated compound storage and retrieval: Robotic freezers or storage units that store, retrieve, and track samples using barcodes or RFID; minimizes human error and reduces contamination risk
Digital twins and virtual labs: Simulated versions of experiments, lab systems, or even full facilities—helps optimize operations before physical implementation
Several award-winning software platforms and applications have been recognized for improving researchers' lives, including LabTwin, Thermo Fisher’s Momentum Workflow Software, Hamilton Robotics Star Systems, Strateos SmartLab Platform, and more.
Why are early engagement and scenario planning critical?
Early engagement and scenario planning are essential for the successful outcome of any project. The design and construction industry is highly complex, involving numerous suppliers, vendors, and subject matter experts who provide a variety of advice to their clients. As an end user or project owner, even with extensive experience, it can be overwhelming to navigate all the conflicting or similar guidance received. It's crucial to understand your program, capacity, product, and research.
For many, a successful project means staying on budget and on schedule. However, success means that end users, researchers, and the manufacturing process are efficient and collaborative. The daily users should find the facility easy to navigate, conducive to creativity, and enjoyable to spend time in, still meeting the budget and schedule. Creating spaces that promote efficiency while addressing human needs can be more complex than it appears. When you incorporate robotics, automation, and future technology into the equation, leveraging these tools for decision-making becomes vital to ensuring a successful project outcome.
As laboratory environments become more intricate, front-end planning must become increasingly intelligent. New digital tools are enabling simulation-driven design decisions well before construction begins.
Key design implications include early and iterative user engagement and simulation processes. Leading-edge planning approaches now encompass:
Digital twins—to model real-time operations, test stress points, and optimize workflow
Generative design—to explore thousands of layout options based on AI-generated parameters
AR/VR visualization—to allow researchers to experience their future spaces before construction starts
Workflow simulations—using tools like Simio or AnyLogic for logistics modeling
Real-time programming tools—to optimize adjacencies and stacking (such as TestFit and Autodesk Forma)
This scenario-driven approach mitigates costly change orders while maximizing operational value from day one. These powerful tools not only help manage costs and reduce schedules but also create satisfied and accountable stakeholders who can make informed decisions throughout the process. Ultimately, this leads to happier end users who have made the right decisions regarding flow and equipment in real time, rather than reactively.
How can we leverage cross-sectors to accelerate progress and innovation?
The most forward-looking labs are borrowing from healthcare, high-tech manufacturing, and workplace design to leapfrog outdated norms.
The lab of the future is a hybrid—purpose-built for convergence.
Best practices from adjacent sectors include:
Healthcare principles: Biophilic lighting, infection control airflows, and evidence-based design to support cognition and performance
High-tech methods: Cleanroom logic, automation handoffs, and process repeatability from fabs and electronics lines
Workplace innovations: Zones for heads-down focus, cross-team collaboration, and UX-informed flow
Convergent design thinking: Blending user personas and processes across digital and physical R&D
Organizations that think horizontally—across sectors—will be first to market with labs that truly support 21st-century science.
Build for discovery at machine speed
AI has already demonstrated its disruptive potential. In 2023, Insilico Medicine used AI to develop a novel DDR1 kinase inhibitor in just 21 days—compressing a years-long process into weeks.
This is not an anomaly. It is a signal. The real estate response must be equally ambitious.
To avoid falling behind, research organizations must treat lab space as a strategic platform, not a sunk cost. The most successful teams will follow three guiding principles:
Benchmark with intent—know what good looks like across sectors
Design for change—build flexibility into every component
Make AI and automation central to your design criteria—not afterthoughts
As labs shift from static infrastructure to dynamic innovation engines, those who invest early will gain the clearest advantage in talent attraction, R&D acceleration, and competitive edge.
