Smarter Labs: AI, Compliance, and Energy in Sync
Laboratory facilities are among the most complex and energy-intensive building types, requiring strict environmental control to ensure safety, regulatory compliance, and research integrity. Many labs still rely on building management approaches that limit visibility into system performance, making it difficult to detect inefficiencies or compliance risks until they become significant problems. Advances in independent data layers, continuous monitoring platforms, and artificial intelligence (AI) analytics are changing that paradigm—providing facility operators, design teams, and researchers with actionable insights that support both safety and sustainability goals.
Greater access to building performance data can break down traditional silos between facilities teams and other stakeholders, Dan Diehl, CEO of Thrive Buildings, tells Lab Design News. “Too often, BMS (building management systems) data is locked down and only accessible and/or understandable by facilities personnel,” he says. “The benefit of an IDL (independent data layer) is having the ability at very low-cost points, via APIs (application programming interfaces), to distribute data and resulting analytics to a much wider constituent base and in ways that they can digest and utilize.”
This expanded transparency is critical in laboratory environments, where ventilation rates, pressurization, and containment systems must be precisely controlled. When only a small subset of personnel has access to operational data, subtle deviations can go unnoticed, leading to safety risks, compliance failures, or unnecessary energy consumption.
Detecting operational drift before it becomes a problem
Even well-designed laboratory systems can gradually drift away from their intended performance. This drift may result from routine operational adjustments, equipment wear, or evolving research needs. “As you might expect, it can be a wide variety, stemming from human error, human intervention with control parameters, broken equipment, and/or deferred maintenance,” says Diehl. He categorizes this drift into three primary types:
Behavioral (manual setpoint overrides that become permanent)
Mechanical (failed dampers, drifting airflow sensors, degraded VAV controls)
Algorithmic (control sequences that no longer reflect current lab use)
Each type of drift can affect both compliance and energy performance. For example, a laboratory ventilation system operating above the required air change rates may still maintain safe conditions but consume far more energy than necessary. Conversely, drift in pressurization or airflow control can quietly compromise containment and regulatory compliance.
Continuous fault detection and diagnostics (FDD) tools address these challenges by constantly analyzing system performance against expected baselines. Diehl notes, “Fault detection and diagnostics (FDD) tools continuously compare live performance to design intent and historical baselines. They identify subtle deviations like declining airflow response time or chronic pressurization instability before they escalate into compliance issues or research disruptions.”
By identifying problems early, facility teams can intervene before issues compromise safety, disrupt experiments, or increase operational costs.
AI-driven analytics for more efficient optimization
AI and advanced analytics are increasingly playing a role in optimizing laboratory performance while maintaining strict safety requirements. These tools can analyze vast amounts of building performance data, identifying patterns and anomalies that would be difficult for human operators to detect manually.
“AI or Advanced Analytics quickly helps highlight where anomalies exist and then can provide ‘likely’ causes and even suggest fixes,” says Diehl. Rather than relying on trial-and-error adjustments, facility managers can simulate potential changes and evaluate their impact before implementation, reducing risk while improving efficiency.
However, the effectiveness of AI depends on access to comprehensive and usable data. “The key is really to create data lakes that can then leverage the power of these analytics/rules, etc.,” Diehl says. When building data is fragmented or inaccessible, opportunities for optimization are lost. Open data architectures and independent data layers allow organizations to unlock the full potential of advanced analytics.
This capability is particularly important as laboratories pursue aggressive decarbonization and energy-reduction targets. Ventilation systems, which account for a significant portion of laboratory energy use, must maintain precise environmental conditions. AI-driven monitoring helps ensure that efficiency improvements do not compromise safety or compliance.
Extending commissioning across the entire lifecycle
Traditional commissioning verifies system performance at project completion, but it does not ensure sustained performance over time. Continuous monitoring platforms extend this concept throughout the building lifecycle, providing ongoing verification and optimization.
“Commissioning hopefully happens initially to help ensure the project is completed to design intent,” Diehl says. “However, once that is done, it is critical to leverage connected/continuous commissioning to sustain performance.”
Without ongoing monitoring, even well-commissioned systems can degrade over time. Continuous commissioning allows facility teams to identify performance drift, address issues proactively, and maintain optimal system operation. According to Diehl, the benefits extend beyond safety and compliance: “These platforms pay for themselves annually in both operational and energy savings; and with costs continuing to improve, in my opinion, they are a ‘must have,’ not a ‘nice to have’ anymore.”
This shift reflects a broader trend toward lifecycle-based laboratory design, where performance verification continues long after occupancy.
Informing future design through real-world performance data
Continuous monitoring does more than optimize existing facilities—it also provides valuable insights for future laboratory design and renovation projects. Historically, many laboratory systems were oversized based on conservative assumptions about heat loads and equipment demands. However, real-world operational data is challenging those assumptions.
“Typically, owners and designers assumed really high heat loads, which then cause oversizing of systems,” Diehl says. “When real-world data was analyzed, it was shown that most labs in use didn’t operate at even half of these assumed heat loads.”
Oversizing increases construction costs, energy consumption, and system complexity. By incorporating operational data from existing facilities, designers can right-size systems more accurately, improving both performance and efficiency.
Diehl emphasizes the importance of integrating real performance data into future planning: “In this sense, availability of real operating data from buildings should be the first input in the design of new ones.”
A data-driven future for laboratory performance
As laboratories become more technologically advanced and sustainability goals become more ambitious, continuous monitoring and AI-driven analytics are emerging as essential tools. Independent data layers, fault-detection systems, and continuous commissioning platforms provide the visibility needed to maintain compliance, ensure safety, and optimize energy performance throughout the entire building lifecycle.
By shifting from reactive maintenance to proactive, data-driven management, laboratory operators and design teams can create facilities that are not only safer and more compliant but also more efficient and adaptable to future research needs.
