For decades, the Electronic Lab Notebook (ELN) has been the digital standard for laboratory documentation. Scientists document experiments, attach data files, and archive results in searchable databases. The shift from paper notebooks to ELNs was celebrated as a major step forward—and it was.
But as laboratories evolve toward automation, real-time analytics, and AI-driven workflows, a fundamental question is emerging:
Is documentation enough?
The answer is increasingly clear: No.
Modern laboratories need more than a digital record keeper—they need an execution engine. This is why forward-thinking labs are shifting from ELNs to Laboratory Execution Systems (LES).
An ELN is retrospective. It captures what happened after the fact. A scientist runs an experiment, collects data from instruments, and then documents it in the ELN. The system is a repository—a structured way to store, search, and retrieve experimental records.
An LES is prospective. It guides, enforces, and orchestrates what happens in real-time. The system doesn’t just record the experiment—it executes it. It connects directly to instruments, enforces SOPs, captures metadata automatically, and triggers the next step in the workflow.
This shift mirrors what happened in manufacturing decades ago: the evolution from paper logbooks to Manufacturing Execution Systems (MES). MES didn’t just document production—it controlled it.
Laboratories are now following the same trajectory.
As labs push toward higher automation, stricter compliance, and AI readiness, the limitations of ELN-centric workflows become impossible to ignore.
In most ELN workflows, data still moves manually.
A scientist weighs a sample on a balance. The balance displays a reading. The scientist writes it down—or exports a CSV file to a USB stick—and later uploads it to the ELN, manually entering the sample ID, the method, and any relevant notes.
This process is slow, error-prone, and strips away critical context. Was the balance calibrated? What was the temperature in the lab? Who performed the measurement? These metadata points are often lost—or entered inconsistently.
An ELN cannot trigger the next step in a workflow.
If a pH reading is out of range, the ELN cannot alert the operator or halt the process. If a sample needs to move from prep to analysis, the ELN cannot schedule the instrument or notify the next user.
Workflow logic exists in the scientist’s head—not in the system. This works for small, exploratory labs.
It breaks down at scale.
Robotic systems, liquid handlers, and automated instruments often operate independently of the ELN.
Data reconciliation happens later—if at all. Multi-instrument workflows require human coordination. A scientist manually checks when the preparatory step is complete, then starts the analytical instrument, then uploads results separately.
The promise of lab automation is undermined by software that cannot keep up.
Metadata is the connective tissue of the modern laboratory. Without it, data cannot be trusted, reused, or analyzed by AI.
ELNs were designed for human-readable notes—not machine-readable context. Environmental conditions, device states, calibration status, operator identity, and method parameters are often entered manually or omitted entirely.
The result: incomplete records, compliance gaps, and data that cannot power advanced analytics.
When labs rely on ELNs as their primary execution tool, each site develops its own usage patterns. Standardization requires extensive training, governance, and auditing.
Process variability across locations becomes the norm. Comparing results from different sites becomes difficult.
Regulatory audits expose inconsistencies.
For global organizations, this fragmentation is a strategic liability.
A Laboratory Execution System transforms how work gets done. It doesn’t replace the need for documentation—but it redefines where and how that documentation happens.
An LES connects directly to instruments—balances, liquid handlers, chromatographs, spectrometers, incubators—using standards like OPC UA LADS, SiLA, or proprietary drivers.
Data flows automatically from the device to the system. No USB sticks. No manual entry. No lost context.
The moment a measurement is taken, it’s captured—along with calibration status, device ID, timestamp, operator, and environmental parameters.
An LES turns SOPs into executable workflows.
Method parameters are enforced at execution time. The system guides the user step-by-step. Conditional logic enables adaptive protocols: if pH < 6.5, trigger alert and halt; if weight > threshold, proceed to next step.
Multi-device workflows are coordinated automatically. The LES schedules instruments, manages queues, and ensures the right sample reaches the right device at the right time.
Metadata is not an afterthought—it’s built into the fabric of the system.
Every action generates a structured record: who, what, when, where, why, and how. User attribution, timestamps, device states, calibration records, environmental logs—all captured automatically.
This metadata makes the data compliant, reproducible, and AI-ready.
An LES enforces compliance at the point of execution—not during an audit six months later.
Critical process parameters are validated in real-time. Deviations trigger alerts. Non-compliant operations are prevented before they happen.
The system generates audit trails automatically, meeting ALCOA+, 21 CFR Part 11, and EU Annex 11 requirements without manual effort.
An LES is not an island. It’s designed to integrate.
APIs connect the LES to LIMS, ERP, QMS, and MES systems. Data flows bi-directionally. A sample registered in LIMS triggers a workflow in the LES. Results from the LES update quality records in the QMS.
Cloud-native or hybrid architectures enable distributed teams to work with the same standardized data, from anywhere.
Structured, contextualized data is the foundation of AI.
An LES produces data that machine learning models can consume directly. Predictive maintenance becomes possible. Process optimization algorithms can run continuously. Digital twins can simulate experiments before they’re performed.
The lab becomes a learning system—one that improves itself over time.
The rise of the LES does not mean the death of the ELN. Understanding when each system makes sense is critical.
Exploratory research with unstructured, evolving workflows
Academic labs where protocol variability is high and automation is low
Documentation-heavy environments with minimal device integration needs
Early-stage R&D where flexibility matters more than standardization
GxP-regulated environments (pharma, biotech, food, medical devices)
High-throughput and automated workflows
Multi-site operations requiring standardization
AI/ML readiness initiatives
Process development and scale-up environments
Labs preparing for digital transformation
As a CPO, I see many labs trying to force-fit ELNs into execution roles they were never designed for. The result is workarounds, frustration, and missed opportunities for automation. An LES isn’t just a “better ELN”—it’s a fundamentally different category of software built for orchestration, not just documentation. The labs that understand this distinction are the ones pulling ahead.
Many labs run both: an ELN for unstructured research notes, and an LES for standardized execution.
Some modern LES platforms include ELN-like documentation capabilities, blurring the lines further.
The trend is clear: consolidation onto fewer, more integrated platforms that prioritize execution over passive documentation.
The difference is not theoretical. It’s measurable.
Scientist exports CSV from balance → uploads to ELN → manually enters sample ID, method, and notes
Transcription errors occur 5-10% of the time
Metadata (calibration status, environmental conditions) is missing or incomplete
Time per sample: 10-15 minutes
Compliance audit finds gaps in traceability
Balance auto-transmits weight + calibration status + timestamp to LES
Sample ID pre-loaded from LIMS integration
Workflow validates reading against method parameters automatically
Complete audit trail generated without human intervention
Time per sample: <1 minute, zero manual entry
60-80% reduction in data entry time
95%+ reduction in transcription errors
100% metadata completeness
50% faster protocol execution cycles
70% reduction in compliance audit preparation time
These are not aspirational numbers. They’re being realized today in labs that have made the transition.
Moving from an ELN-centric model to an LES-driven lab requires planning, but the roadmap is well-established.
Identify high-value, high-frequency workflows where automation and standardization would have the greatest impact. Map device integration requirements. Evaluate compliance gaps and data quality issues.
Choose one automated workflow or critical process. Prove value before full-scale deployment. Learn what works, iterate quickly, and build organizational confidence.
Connect critical devices first: balances, liquid handlers, analytical instruments. Establish metadata standards. Ensure connectivity to LIMS, QMS, and other enterprise systems.
Expand to additional workflows and devices. Standardize processes across sites. Train users on an execution-first mindset—where the system guides the work, not just documents it.
Enable real-time analytics dashboards. Launch predictive models for maintenance and optimization. Pursue digital twin capabilities to simulate and improve processes.
Executive sponsorship and change management
Cross-functional collaboration (IT, QA, R&D, Operations)
Vendor selection: prioritize open architecture, non-coding platforms, proven compliance, and strong support
The boundaries between LES, LIMS, and MES are blurring.
Unified lab informatics platforms are emerging, like Laboperator, —cloud-first, AI-native, and designed for interoperability. These systems don’t just connect devices; they orchestrate entire lab ecosystems.
What this enables:
Self-optimizing laboratories that learn from every experiment
Autonomous experiment execution guided by AI
Real-time supply chain integration that adjusts protocols based on material availability
Predictive quality release that reduces cycle times
Fully digital, paperless operations from sample intake to final report
The question isn’t whether your lab will move toward execution-centric systems—it’s when.
The labs making this transition now are building competitive advantages in speed, quality, and innovation that will compound over years.
The Electronic Lab Notebook served laboratories well in the era of manual, documentation-first science. But as labs embrace automation, AI, and digital transformation, the limitations of passive documentation become impossible to ignore.
The Laboratory Execution System represents the next evolution: a platform that doesn’t just record what happened, but actively orchestrates it—with precision, compliance, and intelligence.
A number without context is just a number.
An experiment without orchestration is just a procedure.
A lab without execution software is just a room full of disconnected devices.
For laboratories ready to move from documenting science to executing it at scale, the shift from ELN to LES isn’t just an upgrade.
It’s a transformation.