Authors: Rosario Muthu Rajan, Vincent Xavier
Introduction
In today’s R&D environment, scientists often spend a significant amount of time setting up experiments, creating workflows, filling in data fields, and exporting quantitative results as Certificates of Analysis (CoA). While these tasks are essential, they slow down scientific productivity and introduce room for human error.
With the rise of AI-powered lab informatics systems—spanning ELN, LIMS, and
digital workflow platforms—labs can now automate major parts of laboratory synthesis workflows.
This includes automatically determining whether an experiment should be for small-scale or large-scale, creating the right duplex experiment, and pre-populating most details using historical or predefined data.
AI: Automatically determining experiment scale
With configured rules and AI-assisted inference, the system can instantly decide—based on sample quantity thresholds—whether a small-scale or large-scale experiment is required.
AI can also factor in historical data, based on:
- What scale (Small scale experiment or Large-Scale Experiment) was previously used for similar samples
- Results observed earlier
Such intelligent decision-making capabilities are powered by modern AI and Machine Learning technologies increasingly used across life sciences research environments.
Auto-Create duplex experiments without user intervention
Once the scale is determined and synthesis is carried out, the system utilizes lab automation protocols to automatically:
- Select the correct small scale or large-scale duplex template
- Create the experiment record
- Select associated child tasks like:
- Whether samples need to undergo normalization, purification and/or
- Whether QC assays need to be performed etc
This eliminates manual experiment creation, which is one of the time-consuming steps in many digital labs using ELN and LIMS platforms.
Auto-Filling experiment details: The power of AI/ML
An AI/ML-powered lab informatics system minimizes manual input by automatically populating critical fields using intelligent automation.
Prompts
- Registering samples
- Creating batches
- Creating experiments based on prompt messages
- Adding samples to experiments
- Performing system-wide searches
- Importing files into workflows or experiments
Historical data
- Previously used materials and reagents
- Previously used instrument types
- Expected yield and purity ranges
Predefined or configurable data
- SOP-based input fields
- Preset naming conventions
- QC pass/fail thresholds
- Reason codes for failed samples
Predictions
- Predicting on the sample working volume quantities
- Performing normalization checks
- Registering Batches based on QC status
- Validating inputs in the workflow. Incorrect inputs are suggested
- Missing data is predicted or suggested
- Errors, inconsistencies, or unusual parameter values are flagged in real-time
- Analyse the assays run for the samples and utilize the correct experiments result for further analysis
This improves efficiency and enhances compliance, turning laboratory workflows into AI-assisted, self-driving systems.
Impact on lab productivity
Implementing advanced scientific informatics and lab automation strategies delivers measurable benefits:
- Faster Experiment Setup: Scientists save hours by eliminating manual experiment creation and data entry
- Better Decision-Making: AI learns from historical data to recommend optimal conditions
- Reduced Errors: Less manual data entry means fewer inconsistencies and deviations
- Standardized Workflows: Consistency improves across individuals, projects, and departments
- Higher Throughput: Automation frees scientists to focus on core research instead of administrative tasks
Conclusion: AI Is reshaping the future of synthesis workflows
AI in lab informatics is not just automating tasks—it is transforming how synthesis workflows operate. Automatic scale determination, intelligent experiment creation, and smart data population enable labs to work smarter, faster, and with greater consistency.
This shift allows scientists to move away from repetitive administrative steps and focus on the innovation that truly drives scientific progress.
