The US Department of Energy's Oak Ridge National Laboratory now runs more than a dozen self-driving laboratories - placing it among the first research institutions worldwide to operate autonomous labs at scale - and relies on a 1,300-person Facilities and Operations workforce that few outside the lab ever see. These craft workers, skilled trades professionals, and facility technicians build and maintain the infrastructure that makes round-the-clock AI-driven science possible.
Autonomous labs run with little human intervention, using robotics, sensors, and automation to handle everything from pouring liquids to placing samples into ovens. Artificial intelligence makes at least one decision in each experimental process, and the systems learn and adapt as they go. But the experiments only work because of the power, networking, ventilation, and physical infrastructure that operations teams designed and installed before the first robot ever powered on.
"Embedding AI into our workflows and systems is completely changing how we do science," said Rob Moore, a distinguished staff scientist who leads autonomous science efforts at ORNL. "That transformation impacts not only the scientific process but also the entire operations enterprise that enables it."
The skilled trades behind autonomous labs
Bringing a single self-driving lab online requires a cross-section of trades that rarely gets acknowledged in published research. Craig Bridges, a chemist in ORNL's Autonomous Chemistry Laboratory, detailed the labor: hoisting and rigging professionals transported heavy robotic equipment and furnaces. Pipefitters connected up to eight different flowing gas streams at different locations. Instrument technicians diagnosed and installed wiring for specialized communication between equipment and control computers.
Sheet metal workers built custom ventilation enclosures to expand the chemistry that could be safely performed. Carpenters constructed equipment fixtures. Electricians provided power, ran physical networking lines for over 10 pieces of equipment, and set up a charging station for the lab's mobile robot. "This work is complex in and of itself, but they had so many safety and performance aspects to consider, since experiments run while most people are sleeping," Bridges said. "Thanks to the staff, I can now focus on delivering the science."
Infrastructure demands of continuous experimentation
Autonomous laboratories depend on a tightly integrated network of equipment, controls, and data systems. Instruments communicate in real time, and data flows directly into computational models that guide the next experimental step. Supporting that environment requires long-term planning and people who understand how every piece fits together, Moore said.
F&O oversees electrical distribution, utilities, high-capacity networking, and control systems that keep experiments stable and safe. Utility, security, and fire protection employees work around the clock. Autonomous labs add new operational demands: steady power, higher data rates, uninterrupted connectivity, and systems that can respond instantly to change. Operations teams must anticipate future requirements long before new instruments or AI systems arrive, a dynamic that has reshaped how AI for Science & Research infrastructure gets planned and built.
Moore said F&O personnel are equal partners in shaping ORNL's autonomous science strategy. "Operations has an equal role in figuring out how to do science in this new era," he said. "We're collaboratively defining what the future will look like."
From maintenance to innovation
Some operations staff are moving beyond support into direct innovation. Darren Loposser, an instrument technician who supports research facilities and fabricates control systems, saw an opportunity to transform an old lab space to meet a project's goals while also laying groundwork for future autonomous science. His upgrades include a system to connect multiple laboratories into a single control network that can operate and share information continuously, letting researchers monitor experiments and respond in real time.
The work creates structured data streams - dense collections of data points about an experiment or piece of equipment - that AI tools can use to analyze results, detect problems, and guide future experiments. It supports ORNL's "Labs of the Future" vision, developed through the Interconnected Science Ecosystem (INTERSECT) Initiative directed by Moore. "We're bringing the power of industrial automation to research," Loposser said.
Chris Brewer, a strategic program manager in ORNL's Computing and Computational Sciences Directorate, said the contributions of people like Loposser deserve wider recognition. "Darren and the diverse cast of F&O staffers won't win awards or receive patents for their work. They see it as simply doing their jobs and don't seek praise, but I think all of the lab, and the public, need to know how essential their work is to the mission of pushing discovery and innovation forward at ORNL."
Why this matters for science and research professionals
The ORNL model surfaces a practical reality for research institutions investing in autonomous labs: the facilities workforce cannot be an afterthought. Electrical capacity, networking throughput, physical safety systems, and real-time control infrastructure determine what experiments are possible. Research directors planning autonomous science initiatives should involve operations teams from the earliest design phase - not after instruments arrive. The scientists who treat F&O staff as co-investigators rather than service providers will bring labs online faster and run them with fewer disruptions.
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