Rewiring Your Lab for AI: 6 Essential Steps to make your research lab AI-ready
Originally presented as a talk by Guru Singh, Founder & CEO, Scispot, at the Bioprocessing Summit 2025, Boston
The electricity metaphor tells the whole story. When factories first adopted electric power through dynamos, they didn't see real benefits until they completely rewired their floor plans and transformed their workflows. New power demanded new infrastructure. AI and Large Language Models (LLMs) in laboratory settings follow this same pattern.
The models themselves are the power source, but your data architecture and processes are the critical wiring that makes everything work. Without proper rewiring, AI remains largely ineffective, no matter how sophisticated the underlying technology.
This article synthesizes practical insights from real-world projects across bioprocessing, drug discovery, and diagnostics. It's not about any specific product or vendor, it's a roadmap based on what actually works. The six steps are deceptively simple to describe but challenging to execute well: digitize for machines, standardize your data model, automate your pipeline, harmonize and engineer features, integrate LLMs with proper context, and establish robust measurement and governance.
Step 1. Digitize for Machines, Not Just Humans
Data that merely "looks good" to human eyes won't be trusted by AI systems. Every element in your lab needs a unique identifier. You must track who performed each action and when. Implement barcoding wherever samples change hands. This creates the provenance chain that eliminates errors and builds system reliability.
This isn't theoretical advice; it's evidence-based best practice. According to the CDC's Laboratory Medicine Best Practices review, barcoding dramatically reduces specimen identification errors throughout the entire testing process compared to manual entry methods.
Manual transcription carries significant risks as well. Research from outpatient settings shows that 3.7% of manually entered glucose results disagreed with interfaced values, with approximately 5 in 1,000 cases judged clinically significant. When automated interfacing is feasible, it should be the default choice.
For regulated environments, ensure that electronic records and signatures meet trustworthy and auditable standards. This aligns with the principles of 21 CFR Part 11 and current FDA guidance for compliance.
Step 2. Standardize the Data Model (Your Lab's Digital Brain)
Spreadsheets proliferate rapidly in most labs, with critical logic buried in tabs and cell notes. LLMs require a structured worldview: samples, runs, reagents, instruments, observations, quality control, and reports, all with clear relationships and consistent units.
Begin with FAIR principles (Findable, Accessible, Interoperable, Reusable) so machines can locate and utilize your data effectively. Store measurements as FHIR Observation records and summaries as FHIR Diagnostic Report formats. Implement UCUM (Unified Code for Units of Measure) to prevent unit confusion, ensuring "mg/dL" and "mmol/L" are never mixed up.
These may seem like mundane technical choices, but they deliver value every single day by creating a foundation that scales.
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Step 3. Automate the Pipeline
Never manually type information that a device already knows. Implement automatic ingestion of files and signals. Validate data upon entry. Calculate the derived values your team actually uses, such as signal-to-noise ratios, and maintain complete lineage tracking.
The rationale is straightforward: automation eliminates avoidable identification and transcription errors, as demonstrated in barcoding and interfacing research. You're investing in data fidelity as much as operational speed.
Step 4. Harmonize and Engineer Features
Ensure consistency across names, identifiers, and units. UCUM provides a shared vocabulary for units, making downstream calculations reliable and safe. Add calculated fields that capture the metrics your scientists already rely on yields, growth rates, noise bands, and similar derived measurements.
Think of this as mise en place in cooking: better preparation leads to better results. The upfront investment in standardization pays dividends throughout the research process.
Step 5. Plug LLMs Into Your Context
LLMs predict the next token in a sequence; they're linguistically fluent but not omniscient. Without access to your specific facts and context, they'll fill knowledge gaps with educated guesses or hallucinations.
- Retrieval-Augmented Generation (RAG) - RAG offers the most accessible entry point. Index your Standard Operating Procedures (SOPs), batch records, and quality control guides, then retrieve the most relevant information at query time. This grounds the model in your actual documentation without modifying the base model weights. The advantages include fast updates and clear citations; the trade-off is the need to carefully measure and maintain retrieval quality.
- Knowledge Graphs - Knowledge graphs capture deeper relationships across samples, runs, and reagents. They excel at complex queries and reasoning tasks. The benefit is precision and sophisticated relationship modeling; the cost is upfront modeling work and ongoing curation.
- Fine-Tuning - Fine-tuning helps achieve consistency and appropriate tone for known tasks. You can teach models your preferred phrasing patterns or standardized Q&A formats. The advantage is predictable, on-brand responses; the drawbacks include cost and the need to manage model drift as protocols evolve.
- Agentic Workflows - Agentic systems chain multiple steps with integrated tools ingesting data, analyzing results, drafting reports, and sending notifications automatically. The benefit is speed at scale; the challenge lies in orchestration and implementing appropriate guardrails. Start with small, controlled implementations and maintain human oversight for critical decisions.
Regarding hallucinations: your concerns are well-founded. Surveys document this problem across various AI tasks. Retrieval and structured context significantly reduce hallucinations, but you still need robust evaluation and provenance tracking in production environments.
Step 6. Measure, Govern, and Iterate
Define clear success criteria. Track accuracy, groundedness, latency, and coverage metrics. Maintain provenance for every system output. Approach this like managing a bioreactor: observe performance, control variables, and adjust based on results.
Use the NIST AI Risk Management Framework as a common language with Quality Assurance, IT, and Legal teams. While voluntary, it provides practical guidance for identifying, measuring, and mitigating AI-related risks.
Monitor EU AI Act timelines if you operate in European markets. General-purpose AI model obligations begin August 2, 2025, with broader application following and general effectiveness by 2027. Start planning documentation and control frameworks now.
Our Projects Vary Can We Really Standardize? - Standardization works in layers. Create specific models for distinct workflows, bioprocessing here, analytical work there. Share identifiers and units across these models. Connect them later through a minimal set of relationships: sample ↔ run ↔ assay ↔ report. Retrieval systems can operate across these connected layers, answering cross-cutting questions with proper citations. This approach manages complexity without oversimplification.
How to get started?
Begin with identifiers and barcodes at custody transfer points. Connect one high-value instrument to a structured, Observation-style database table with corresponding Diagnostic Report summaries. Normalize units across the system. Add two or three derived fields that your team already trusts and uses regularly.
Then deploy a focused RAG assistant for SOP and batch recipe questions that provides citations with its answers. Learn from the gaps in performance and iterate based on real usage patterns.
What's next
Electricity didn't transform factories until organizations changed their floor plans and workflows. Similarly, AI won't revolutionize laboratories until we rebuild our data infrastructure. Invest in proper data wiring, and AI models will finally illuminate the full potential of your research operations.
The transformation isn't just about adopting new technology; it's about creating the foundation that allows that technology to deliver meaningful value. The six steps outlined here provide a practical roadmap for any laboratory ready to harness the power of AI while maintaining the rigor and reliability that scientific research demands.
