How OPC UA LADS, SiLA, and Laboperator Are Powering the AI-Enabled Lab of the Future

By Jeroen, Chief Product Officer

As laboratories evolve toward higher automation, data intelligence, and cloud connectivity, one challenge continues to hold back true digital transformation: fragmented, inconsistent, and non-standardized lab data.

Robots and advanced instruments have become mainstream, but many labs still struggle with basic interoperability. Files come in incompatible formats. Devices use proprietary APIs. Integrations take months. AI initiatives stall because the underlying data lacks structure and context.

This is why modern lab data standards such as OPC UA LADS (Laboratory and Analytical Device Standard) and SiLA (Standardization in Lab Automation), combined with orchestration platforms like Laboperator, are becoming essential. They create a unified digital foundation that laboratories can build on—enabling automation, analytics, and AI at scale.

Historical Insight: The Long Journey Toward Lab Interoperability

For decades, labs operated as independent “data islands.” Early instruments had:

Instrument integration was costly and error prone. Data scientists spent more time cleaning data than analyzing it.

The introduction of SiLA and later OPC UA LADS marked a turning point—establishing open interfaces and structured data models that encouraged vendor-neutral integration.

Laboperator emerged as the missing link: the platform that turns these standards into an operational, scalable reality for modern labs.

What Are Lab Data Standards and Why Do They Matter?

OPC UA LADS and SiLA are industry-driven standards designed to make instruments interoperable, discoverable, and machine-readable.

These standards:

• Provide a common communication layer
• Define structured device capabilities
• Standardize metadata and data models
• Reduce integration complexity
• Improve reproducibility and traceability

In short, they give laboratory devices a shared language.

But the story doesn’t end there—because a common language still needs an environment where devices, data, and workflows can come together.

This is where Laboperator, a leading laboratory integration platform, plays a transformative role.

Will Data Standards Make AI in the Lab Easy? Not Quite—And That’s Okay

Even with OPC UA LADS, SiLA, and Laboperator working together, AI in the lab will never be “easy.”
And understanding this is critical for any lab embarking on digital transformation.

1. Biological and chemical systems are inherently unpredictable.
AI must handle noise, variability, and domain complexity.

2. Standards provide structure—not full semantic meaning.
Contextual scientific understanding still requires ontologies, domain models, and expert-defined workflows.

3. Lab AI requires deep integration across devices, data, and processes.
AI in the laboratory will always require careful engineering + scientific expertise + high-quality standardized data.

The value is immense, but it is not plug-and-play—and it never will be.

Laboperator accelerates this, but AI still needs high-quality structured data and well-defined workflows.

How Laboperator Complements OPC UA LADS and SiLA

While OPC UA LADS and SiLA define how devices communicate, Laboperator operationalizes these standards so labs can actually use them at scale.

Laboperator provides:

✔ Seamless Device Connectivity
Connect standardized or proprietary devices into a single ecosystem, with plug-and-play simplicity for OPC UA LADS and SiLA-compliant instruments.

✔ Workflow Orchestration
Build and automate complex protocols that combine multiple instruments, operators, and data streams.

✔ Centralized Data Capture and Metadata Management
Structured data collection ensures that every experiment is reproducible, searchable, and AI-ready.

✔ Vendor-Neutral Integration
Choose the best instruments for your lab—without worrying about compatibility.

✔ Cloud-Based Collaboration
Enable distributed teams to work with the same standardized experiment and device data, from anywhere.

With standards + Laboperator, labs finally get the interoperability they’ve been promised for years.

The Future of Laboratory Automation: What Standards + Laboperator Are Enabling

1. Self-Configuring, Self-Discovering Labs
Devices automatically identify themselves via OPC UA LADS or SiLA and integrate into Laboperator without manual effort.

ML models propose optimized parameters, run workflows, capture results, and learn continuously.

Switch instruments easily while preserving data structures and workflows.

AI models make decisions mid-run, with Laboperator orchestrating next steps.

Every step—from instrument parameters to metadata—is captured in a machine-readable, standardized format.

This is the laboratory of the future: automated, intelligent, reusable, and resilient.

Conclusion: Data Standards + Platforms = The Foundation of the AI Lab

OPC UA LADS and SiLA provide the language.
Laboperator provides the orchestration.
AI provides the insight.

Together, they’re enabling a generation of laboratories that are more interoperable, more automated, and more intelligent than ever before.

As a CPO, I believe the future of digital labs won’t be defined by any single instrument or algorithm, but by the shared foundation that allows them all to work together. That foundation begins with open standards and is made real by platforms like Laboperator.