Accelerating Lab Experiments 10-30x using an AI –first Automation Approach

Laboratory automation in life sciences has traditionally focused on hardware integration and physical workflows. However, the true value of automation emerges when we consider the entire experimental life cycle: hypothesize, design, run, analyze, repeat. This cycle depends critically on the bidirectional flow of contextual information between the physical lab environment and computational analysis. Without context, measurements lose relevance, and analytical outputs cannot effectively guide future experiments.

Our analysis identifies three complementary methods that enable context-rich lab automation: model-quality data, FAIR data principles, and tacit knowledge. LLMs have become increasingly valuable tools for scientific data management, particularly in making data FAIR and improving data quality for modeling. Going a step further, using LLM agents can better hypothesize de novo experimentation using robust contextual information from unrelated results. These agents can also be used during experimentation to monitor progress and guide downstream processes or abort entirely if warranted.

Andrew Doran

Andrew.Doran@20visioneer15.com

Raminderpal Singh, PhD

1. Key components

1.1 Supporting FAIR Principles

LLMs improve findability by automatically generating rich, standardized metadata from raw datasets and linking them to domain-specific ontologies, enabling more effective contextual search beyond simple keyword matching. For accessibility, they can create comprehensive documentation, generate data dictionaries, and translate or simplify technical content to reach broader audiences. With interoperability, advanced schema mapping, translating between file formats while preserving semantic meaning, and standardized variable names and values according to community norms. Finally, LLMs support reusability by assessing data quality, identifying inconsistencies, tracking provenance through clear lineage descriptions, and providing usage examples, including sample code and scenarios to guide future applications.

1.2 The Transformation into Model-Quality Data

AI can significantly enhance automated data cleaning pipelines for model quality data by leveraging structured representations of domain knowledge that define key concepts, relationships, and constraints relevant to model performance and evaluation. This ontology can include standardized definitions for metrics like accuracy, precision, recall, fairness, robustness, and explainability, as well as metadata about datasets, models, and validation procedures. By referencing this ontology, LLMs with ML can identify anomalies, suggest context-aware corrections, and document each cleaning step with semantic clarity. They can recommend data transformations that align not only with statistical properties but also with the conceptual structure of model quality assessment that ensure preprocessing supports are valid and interpretable evaluations. LLMs also help detect potential sources of bias by using this ontology to flag imbalances in performance metrics across subgroups or inconsistencies in evaluation protocols. They standardize terminology, normalize variable names and values according to ontological definitions, and intelligently handle missing or inconsistent data through imputation or flagging. This ontology-driven approach ensures that data cleaning aligns with domain-specific standards, enhances transparency, and supports trustworthy model evaluation.

1.3 Leveraging Tacit Knowledge

LLMs can play a transformative role in laboratory automation by capturing and simulating aspects of tacit knowledge, the kind of intuitive, experience-based understanding that is often difficult to formalize or document. By analyzing historical data and scientific literature, LLMs can interpret experimental results contextually, suggest nuanced protocol adjustments, and translate informal lab instructions into precise automation commands. They also support training

by offering active guidance, anticipating errors through pattern recognition, and continuously improving through feedback loops. This integration helps bridge the gap between human intuition and machine precision, making lab workflows more adaptive and intelligent.

1.4 Limitations

While AI offers powerful capabilities for processing and transforming scientific data, they can also introduce their own biases, particularly when trained on general-purpose datasets that may not reflect the nuances of specific scientific domains. These biases can manifest in subtle ways, such as favoring certain interpretations, overlooking edge cases, or misapplying transformations. This makes domain expertise essential for validating AI-assisted data processing, ensuring that outputs align with established scientific standards. Additionally, specialized terminology and niche subject areas can challenge even advanced LLMs, potentially leading to misinterpretations or oversimplifications. Careful oversight and human-in-the-loop validation are critical to maintaining scientific integrity.

2. Implementation Framework: Integrating AIs into Lab Workflows

As AI (Machine Learning and Large Language Models) become more deeply embedded in scientific research, they are transforming how laboratories operate. These systems offer powerful capabilities for interpreting data, generating insights, and supporting decision-making across the experimental lifecycle. To fully realize their potential, laboratories must adopt a thoughtful approach that integrates technical infrastructure, robust oversight, and cultural readiness.

Well-designed architecture enables seamless interaction between ML, LLMs, and laboratory systems, from ingesting structured and unstructured data to delivering context-aware recommendations through intuitive interfaces. Equally important is a strong governance framework that ensures transparency, human oversight, and accountability in how LLMs influence scientific outcomes. At the same time, fostering a collaborative mindset and equipping researchers with the skills to engage critically with AI tools are essential for building trust and maximizing impact.

2.1 Experiment Enhancement Inside and Out

Traditional laboratory automation often struggles with making dynamic adjustments during experiments, especially when unexpected conditions arise. In contrast, experienced scientists excel at on-the-fly decision-making, using intuition and foresight to anticipate issues several

steps ahead. This allows them to recognize when subtle adjustments are needed or when an experiment should be aborted early to avoid wasted resources. LLMs can help bridge this gap by acting as active participants in the experimental process. They can assist during protocol development, provide suggestions during execution, and support data validation by flagging inconsistencies or anomalies and ultimately enhancing adaptability and responsiveness in automated workflows.

2.2 New PCR Accelerated 10-30x faster – Activating the Short Cycle

By introducing AI into PCR, we enable a paradigm shift from inter-experimental to an intra-experimental process.

Operational mode makes fully automatic adjustments without manual intervention between every cycle within a single experiment. Between each cycle changes to the annealing or denaturation temperature, duration of each step, and enabling completion as soon as results are acceptable will generate results of high quality and fully optimized parameters for the unique primer set. For more detail on this example check out my case study “PCR Optimization Case Study: 10-30X Faster Process.”

3. Conclusion: Better, Faster, Stronger

In the evolving landscape of laboratory automation, the integration of large language models (LLMs) marks a transformative leap toward making scientific workflows better, faster, and stronger. By enriching automation with contextual awareness, LLMs enable smarter decision-making across the experimental lifecycle, from hypothesis generation to in-lab execution and data analysis. They make lab operations better by enhancing data quality, supporting FAIR principles, and simulating tacit knowledge that traditionally resides only in experienced researchers. They make processes faster by automating metadata generation, streamlining data cleaning, and offering immediate protocol adjustments during experiments like PCR, ELISA, and cell growth assays. And they make systems stronger by increasing resilience to unexpected conditions, flagging anomalies, and ensuring reproducibility through transparent documentation and feedback loops. While human oversight remains essential to mitigate biases and validate outputs, LLMs empower laboratories to operate with unprecedented agility and intelligence. This synergy between human expertise and AI-driven adaptability paves the way for a new era of scientific discovery, one where automation is not just efficient, but also insightful, responsive, and robust.

If this is something you are interested in bringing into your lab feel welcome to reach out and start a conversation with me! As the Global Laboratory & Robotics Automation Visioneer I’d be more than excited to make your automation goals into a reality.

Commented [JC1]: life cycle?