Laboratories are among the most energy-intensive workplaces, with critical equipment like ultra-low temperature (ULT) freezers consuming as much power as a household. That makes equipment performance a natural target for sustainability efforts—and one where artificial intelligence (AI) is beginning to change the game.
“Energy savings are dollars hiding in plain sight,” said Rick Kriss, CEO of KLATU Networks, during his talk at the My Green Lab Summit 2025. “The problem is not for lack of intent in terms of achieving sustainable outcomes, reducing energy cost, and HVAC loads. It's more about a lack of a repeatable process to identify and correct underperformance.”
The “bad boys” of energy use
Kriss pointed to ULT freezers, fume hoods, and autoclaves as some of the most resource-intensive instruments in the lab. These units often operate around the clock, and even when they appear to function normally, they may be operating under mechanical stress that quietly increases energy consumption.
Predicting failures before they happen
AI and machine learning tools are now helping labs identify and address those hidden inefficiencies. By analyzing performance patterns, predictive analytics can identify when equipment is straining long before a breakdown occurs. Detecting mechanical stress early allows managers to take corrective action, reducing wasted energy and extending the lifespan of costly assets.
“There's a near-perfect correlation between mechanical stress in a ULT [freezer] and wasted energy,” Kriss explained. By finding stress sooner, labs can cut waste and extend equipment life.
Bringing objectivity to equipment decisions
One model Kriss described is TRAXX Score, a system that assigns each instrument a benchmark rating, similar to a credit score. Instead of relying on intuition or scattered service logs, managers receive a standardized measure that reflects energy use, utilization, and mechanical health.
“The beauty of a score is objectivity,” he said. “It’s measurable, repeatable, and practical.”
Why benchmarking matters
Objective scoring gives lab managers a clearer picture of where to focus repair budgets and efficiency initiatives. Instead of spreading resources thinly, managers can target the equipment that consumes the most energy or shows early signs of stress.
Benchmarking can also unlock financial benefits. Many utilities offer rebate programs for organizations that can demonstrate measurable energy savings. By documenting performance before and after with objective scores, labs are better positioned to qualify for these incentives. Utilities often require proof that savings persist over time, making ongoing monitoring essential.
Verification is critical because repair work does not always deliver long-term improvements. Continuous monitoring ensures that interventions truly restore performance and prevent inefficiencies from reemerging.
Toward asset-level certification
Looking ahead, Kriss suggested that asset-level scoring could complement broader programs such as My Green Lab’s certification framework. Being able to demonstrate measurable efficiency at the level of individual instruments, he argued, would help labs show credible progress toward sustainability goals.
What it means for lab managers
For lab leaders tasked with balancing budgets and sustainability commitments, AI-driven benchmarking provides a means to transition from best efforts to measurable results. By turning hidden inefficiencies into quantifiable data, managers can prioritize interventions, reduce energy waste, and make a stronger case for investments in sustainable operations. This shift from guesswork to evidence-based scoring positions labs to cut costs, shrink emissions, and build resilience.
