How Is AI Shaping the Future of Automated Labs?

Artificial intelligence (AI) is no longer just a tool for data analysis – it’s rapidly becoming the central nervous system of modern research. Across the life sciences, AI is transforming how experiments are designed, executed and interpreted, driving a shift toward smarter, more adaptive laboratories.

Over the next decade, AI is expected to coordinate entire experimental ecosystems – connecting instruments, optimizing workflows and uncovering hidden insights in vast datasets. It could redefine the lab as a self-learning environment that accelerates discovery and enables truly personalized science. Yet, as experts emphasize, realizing this vision will depend on responsible implementation – ensuring strong data governance, transparent model validation and the continued human ability to ask the right questions.

In response to the question, “Looking ahead, what role do you see AI and machine learning (ML) playing in the evolution of automated labs over the next 5–10 years?”, experts shared their insights with Technology Networks, offering a glimpse into a future where automation and intelligence work hand in hand.

Johan Junker, PhD

AI is already facilitating materials optimization. We can use AI to assist in the development of better biomaterials and to improve experimental conditions.

Sergej Ostojic, PhD

AI can greatly support the design of more efficient and feasible experiments, as well as improve how we interpret data. This is already evident in big data analyses and epidemiological research in the health sciences, where the speed and scale of analysis far exceed human capacity. Looking ahead, I envision automated AI-driven laboratories with genuine decision-making capabilities, trained on high-quality datasets. In the ideal scenario, humans would focus on generating highly relevant, original, and disruptive hypotheses, while AI-powered labs would carry out the work efficiently and in a timely manner.

Michael Head, PhD

There is huge potential in AI to be supportive for delivering health services. We are starting to consider the use of ML approaches along with large-language models to identify barriers in access to healthcare. We also used large language models in the development of our cancer research network analyses, turning a process that normally takes years into just a few weeks.

Gene Mack

I think the near-term role for AI will be in the early integration and screening of compounds. That’s where it can have an immediate impact. Looking ahead, if technology continues to improve, AI could also help us better identify and enrich patient populations for clinical trials. That would mean sponsors could more precisely match a drug to the patients most likely to benefit, rather than seeing positive effects diluted across a broader population. For small patient groups who respond well to a therapy, this kind of enrichment could be the difference between a signal getting lost and a trial succeeding.

Falk Schlaudraff, PhD

Over the next 5–10 years, AI and ML will play a transformative role in the automation of labs. One of the biggest opportunities lies in integrating data from various disciplines, such as imaging, next-generation sequencing and mass spectrometry, to optimize and streamline workflows.

AI could reduce the need for expensive and time-consuming fluorescence labeling by learning to identify cell types and structures directly from unstained or minimally stained images, significantly lowering costs and improving scalability. This would make advanced approaches like deep visual proteomics more accessible, paving the way for the broader implementation of personalized medicine.

Denise Teber, PhD

Connecting various laboratory instruments, often from different companies, is required for a fully automated workflow.  Connecting these instruments in a reliable workflow can be supported by AI. Furthermore, data generated by scientists is becoming increasingly bigger and more complex. Analysis of these data will be supported more and more by AI over the next 10 years.

Ellie Juarez, PhD

AI and ML will be central to scalable, automated interpretation of genomic and multiomic data. From real-time variant calling to predictive risk modeling, AI has the potential to not only streamline lab operations but also transform how results are contextualized for clinicians and patients. The integration of AI-driven analytics with automated sequencing workflows could redefine the standard for precision medicine.

Carol Houts

AI will move us from dashboards to decisions. It will help detect deviations before they occur, recommend corrective actions, and even generate validated methods and schedules automatically. This will cut review cycles and free up human expertise for higher-value work. The key will be strong governance, traceable data, version control for models and keeping humans in the loop for final release decisions.

Cesar Canales, PhD

AI and ML will become central to hypothesis generation and experimental design, especially in multiomic approaches and high-throughput imaging-based fields. Labs that are integrating AI will be able to test, refine, and scale up experiments more efficiently, and in ways that reduce human bias. I think our role as scientists will evolve from operator to more of a curator and interpreter, focusing on designing meaningful questions and validating computational predictions.

Ruizhi Wang, PhD

In the next 5–10 years, I see AI and ML becoming the coordinating layer that drives the evolution of automated labs. Today, automation speeds up individual steps, but labs still operate in a fragmented way. AI has the potential to integrate instruments, schedule and adjust workflows and analyze results as they are generated. That shift will move labs from running fixed protocols to running adaptive, data-driven workflows that improve with every cycle.

One emerging example is the idea of self-driving labs, where AI dynamically designs and refines experiments, closing the loop between setup, execution and analysis. Another is the use of digital twins – AI models of processes that allow teams to simulate changes before running them in the lab. Both trends point to a future where AI helps labs not only work faster but also learn continuously from their own data.

The role of AI will also be to make sense of the large and complex datasets that automated labs naturally produce. By detecting patterns, predicting outcomes and flagging issues early, AI can help ensure that automation is not just faster, but also more reliable and more informative. In this way, AI and ML will turn automated labs into connected, self-optimizing environments that support better science and faster development.