Life science facilities are some of the toughest challenges in the race to decarbonize, given their high energy consumption. The good news is technology is here to help. Not only for decarbonization, but also for energy security and cost reduction.
In the most recent Siemens Infrastructure Transition Monitor, which brought together insights from 1,400 executive respondents across 19 countries, more than half of industrial leaders (63 percent), including pharmaceutical companies, view digitalization as a critical enabler of the transition toward net zero, with AI emerging as the most important technology to achieve it.
This is especially valuable for labs. AI can analyze vast streams of data to optimize operations, spot energy inefficiencies invisible to the human eye, and help facilities reduce emissions without putting product quality or compliance at risk.
However, it is also true that AI demand is generating more power consumption, which can impact overall decarbonization goals. Yet life science facilities are already proving AI’s value through measurable operational gains, with emerging autonomous capabilities demonstrating clear potential for transformative impact.
In fact, AI has already started to be embedded in products and platforms—for example, to help automate and control heating, ventilation, and air conditioning (HVAC). By using HVAC equipment data along with weather and, in some scenarios, occupancy data, it defines the optimal conditions in a room or zone of a building. This effectively creates a self-adjusting system that predicts the ideal conditions and then operates the HVAC more efficiently.
This isn’t theoretical, it’s happening right now in energy-intensive critical environments—labs, production facilities, and controlled storage—where precise environmental control is essential for both compliance and energy efficiency. The results are measurable. Pharma and life science facilities implementing intelligent HVAC controls in these critical spaces are achieving significant monthly energy savings.
Many pharma organizations are also looking further ahead. The shift from smart to autonomous technology is gaining momentum, with 60 percent of industrial leaders saying the benefits outweigh the costs. For labs, laying the foundations for this shift means thinking about getting your systems to work together and smart data platforms now, which will ultimately allow them to move faster and more effectively toward net zero.
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But let’s not get too far ahead of ourselves. My goal with this article is to get specific about how labs can start applying the digital tools that already exist—meeting today’s practical needs and then building on them gradually in a way that fits their operational model, whether they’re a global pharma giant or a smaller research lab.
Stage 1: See what’s happening
Think of it as giving your lab a nervous system.
By strategically placing sensors on critical equipment and systems—from cleanroom HVAC units and ultra-low temperature freezers to fume hoods, incubators, and autoclaves—you can see exactly how energy is being used, and where it’s being wasted.
The result is a real-time picture of energy consumption, equipment health, environmental conditions, and overall performance. This makes it far easier to target the areas that matter most; for example, a freezer running outside its optimal temperature band or a cleanroom airflow pattern consuming more air changes per hour than required by standards.
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Small-scale labs: Implementing just a handful of low-cost IoT sensors can deliver quick wins and tangible savings, without requiring a major IT investment.
Stage 2: Get your systems talking
Right now, lab systems often work in silos—HVAC doing one thing, freezers another. By connecting these into a single digital environment, you break down those silos and start to see the whole picture of how one system affects another.
The key here is that you need the solid foundations of a robust building management platform. These platforms handle the heavy lifting and act as a central hub that links different lab systems, analyzing thousands of data points in real time and creating the framework for AI to later act with the greatest impact. When AI has a complete operational picture, it can make intelligent decisions about anything from cleanroom airflow to freezers and security systems.
Small-scale labs: Even connecting just your HVAC and fume hoods to one shared dashboard can unlock valuable insights and savings.
Stage 3: Build a digital twin
This is where things start to accelerate.
A digital twin creates a virtual version of your lab so you can safely test changes, like adjusting airflow or optimizing equipment schedules, before doing anything in the real world. This isn't just a fancy 3D model; it's a working copy that shows you how all your lab systems interact in real-time.
Digital twins bring together dynamic and static data from multiple sources, delivering a real-time understanding that dramatically improves decision-making, advanced planning, and optimization potential. Digital twins also allow engineers to model different scenarios and assess outcomes before implementation. It can help labs move from reactive management to proactive design, showing managers where the biggest energy-saving opportunities lie without risking safety or compliance.
Small-scale labs: Many digital twin solutions are now cloud-based services, lowering the barrier to entry and enabling advanced optimization without huge CapEx.
Stage 4: Apply AI intelligence
For lab and facility managers, AI integration means moving beyond simple alerts to intelligent systems that continuously analyze energy demand and environmental conditions.
AI-powered systems can spot patterns you’d miss, predict equipment failures, and recommend adjustments that balance energy reduction with compliance and product quality.
A good example is the Life Science Factory in Göttingen, Germany—proof that AI isn’t just for pharmaceutical giants. This 3,300 m² shared facility with state-of-the-art labs, open offices, and a prototyping workshop for life science start-ups, uses intelligent ventilation and automated light and shade control via room automation to optimize energy use. Air volume flows are monitored with alerts if consumption is too high. Other systems track energy supply and highlight optimization potential. Together, these systems ensure the Life Science Factory operates at peak efficiency while maintaining the strict environmental conditions required for cutting-edge life science research.
Small-scale labs: Many AI platforms now come with user-friendly interfaces, so you don’t need a team of data scientists to benefit. In shared facilities like the Life Science Factory, start-ups and smaller companies can also access advanced technologies and scale them up as needed, without heavy upfront investment.
Stage 5: Rethink your energy supply
Cutting consumption is only half the story. How you generate and supply energy matters just as much. Ferring GmbH recognized this when building its new 15,000 m² facility for research, labs, and sterile cleanroom production in Germany. The company invested in a dedicated energy center with combined heat and power plants, boilers, chillers, and air compressors, all managed by advanced digital controls that optimize performance and enable preventative maintenance.
Supported by €1 million ($1.2 million) in government subsidies, the project now saves around €925,000 ($1.09 million) annually and cuts 2,400 tons of CO₂ emissions each year.
Small-scale labs: While not always feasible for a single lab, at campus- or portfolio-level, this approach is a powerful lever for both decarbonization and cost control.
Stage 6: Let the lab run itself (almost)
The end game is labs that self-optimize—environmental systems automatically adjust to occupancy and process demand, keeping energy use to a minimum while meeting regulatory standards. Facilities teams are freed from firefighting and can focus on long-term strategy and continuous improvement.
Going a step further, autonomous labs could eventually interact with the wider energy ecosystem. By shifting loads, storing surplus renewables, and providing flexibility services back to the grid, they would become active players in the clean energy transition.
Boehringer Ingelheim is already heading that way, as Jan Fassbender, head of global facilities and engineering, told us when interviewed for our ITM 2025 research, with AI helping to tailor the energy mix of each site, helping many to become carbon neutral.
“We are shaping each site’s future energy mix based on what's locally available now, and what will be in 2030 or 2035,” Fassbender explained. “These models rely on advanced analytics and AI to simulate the mix of electricity, storage, heat pumps, and more. These insights are invaluable when engaging suppliers and grid partners. Fourteen of our sites are already carbon neutral using this data-driven approach.”
Small-scale labs: Even partial automation, like predictive maintenance or automated freezer monitoring, delivers measurable savings and reduces operational risk.
A leadership opportunity for the whole industry
Net zero isn’t just for the largest players. Whether you’re running one research lab, working from a shared lab space, or operating a global R&D network, there’s a clear path forward: cutting emissions, saving costs, and showing regulators, investors, and employees that sustainability can be built into the science.
