Thermo Fisher Scientific has announced a strategic collaboration with NVIDIA to embed artificial intelligence directly into scientific instrumentation and laboratory infrastructure. The partnership brings together Thermo Fisher’s portfolio of instruments and laboratory software with NVIDIA’s AI computing platforms to accelerate automation, improve accuracy, and connect laboratory data to scalable AI systems.

The announcement reflects a broader shift toward laboratory AI automation as instrument vendors move beyond standalone hardware toward integrated, AI-enabled environments. By reducing manual steps across experiment design, instrument operation, and data analysis, the collaboration positions AI as a foundational component of next-generation scientific workflows rather than an add-on analytics tool.

Thermo Fisher Scientific–NVIDIA AI collaboration and laboratory AI automation

In many laboratories, scientists still perform a wide range of tasks manually, including experiment design, instrument setup, sample preparation, and result interpretation. These steps can slow workflows and introduce variability, particularly in data-intensive life sciences research.

The Thermo Fisher–NVIDIA collaboration focuses on modernizing these workflows by pairing Thermo Fisher’s scientific instrumentation and laboratory software with NVIDIA’s AI infrastructure. By integrating AI directly into instruments and laboratory systems, the companies aim to reduce manual steps and enable faster, more consistent experimentation.

“Artificial intelligence coupled with laboratory automation will transform how scientific work is performed,” said Gianluca Pettiti, executive vice president at Thermo Fisher Scientific.

AI infrastructure supporting scientific instrumentation

At the technology level, the collaboration leverages NVIDIA’s AI infrastructure, including NVIDIA DGX Spark™, a desktop-scale AI supercomputer, and foundation models such as NVIDIA NeMo™ and NVIDIA BioNeMo™. These tools support advanced data processing, model training, and AI inference directly connected to laboratory instruments.

For laboratories, this enables capabilities such as real-time data analysis during instrument runs, AI-assisted experimental design, and more intuitive user interfaces. Rather than treating data analysis as a downstream step, AI-powered scientific instrumentation can respond dynamically as experiments unfold.

Enabling “lab-in-the-loop” science

NVIDIA characterizes this approach as “lab-in-the-loop” science, in which AI agents, instruments, and scientists operate in continuous feedback cycles. In this model, AI systems help guide experiments as they run, not just analyze results afterward.

“We are entering the era of ‘lab-in-the-loop’ science where the trinity of AI, agents, and instruments will be able to scale scientific discovery at an industrial pace,” said Kimberly Powell, vice president of healthcare at NVIDIA.

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For laboratories seeking higher throughput and reproducibility, this approach supports faster iteration and more standardized outcomes, particularly in environments with long or resource-intensive experimental cycles.

Implications for laboratory managers

For laboratory managers, the move toward laboratory AI automation has operational implications beyond individual instruments. AI-enabled systems depend on well-integrated data infrastructure, consistent workflows, and staff readiness to work alongside AI-assisted tools.

As AI becomes embedded across instruments, software, and data systems, lab leaders may need to evaluate data governance practices, cross-platform integration, and training strategies. Thermo Fisher’s broader ecosystem, which includes instruments, consumables, services, and digital platforms, positions AI as a unifying layer across laboratory operations.

By connecting data, instruments, software, and scientists in a single environment, AI-powered scientific instrumentation can improve laboratory automation and performance without requiring proportional increases in staffing. Collaborations such as this one highlight how instrument manufacturers and technology providers are shaping the next phase of AI-driven laboratory operations.

This article was created with the assistance of Generative AI and has undergone editorial review before publishing.