Beyond Sample Tracking: How Next-generation LIMS Are Powering the Connected Laboratory

Laboratory information management systems (LIMS) have traditionally helped laboratories track samples, manage workflows, and support regulatory compliance. That role is now expanding as laboratories adopt cloud platforms, automation, AI, and greater integration across laboratory systems.

Next-generation LIMS are becoming central to this transformation. Beyond organizing laboratory information, these platforms can connect samples, instruments, storage units, scientific applications, enterprise systems, data, and people through coordinated digital workflows. This connectivity makes trusted information available to scientists and operational teams when they need to make decisions.

These themes were at the center of Frost & Sullivan’s recent Growth Webinar, “The Connected Lab Revolution: How Next-Generation LIMS Are Accelerating Innovation,” in which industry experts discussed the forces shaping LIMS adoption, the use cases delivering practical value, and the priorities laboratories need to address as they move toward more intelligent and autonomous operations.

The session brought together the following Growth Experts:

Click here to access the on-demand recording.

Here are some highlights of the discussion:

LIMS Is Becoming the Backbone of the Connected Laboratory

The life sciences LIMS market is entering a new growth phase and is expected to expand from approximately $1.3 billion in 2025 to more than $2 billion by 2031, at a CAGR of 7.9%. This growth reflects a broader shift in the role of LIMS, from a system of record to a strategic platform supporting compliance, efficiency, and digital transformation.

Three forces are central to this change:

• AI and machine learning (ML): AI and ML are helping LIMS evolve from a data repository into an intelligent decision-support platform. Predictive analytics, anomaly detection, intelligent search, generative AI copilots, and automated decision support are extending its role across laboratory operations.
• Regulatory and data-integrity requirements: Requirements such as the US Food and Drug Administration (FDA) Title 21 Code of Federal Regulations (CFR) Part 11, ALCOA+, and Good Manufacturing Practice (GMP) Annex 11 are accelerating the replacement of paper-based and spreadsheet-driven processes. AI-assisted compliance capabilities could further automate audit-trail reviews, data checks, and deviation management, while connected laboratory and quality systems can support continuous regulatory monitoring and real-time inspection readiness.
• Cloud and Software-as-a-Service (SaaS) adoption: Cloud and SaaS delivery are making software management easier while supporting integration across laboratory and enterprise systems. This is helping LIMS become the digital foundation for more connected and automated laboratory ecosystems.

By 2030, LIMS is expected to operate as a central orchestration hub connecting instruments, robotics, electronic laboratory notebooks (ELNs), laboratory execution systems (LESs), scientific data management systems (SDMSs), chromatography data systems (CDSs), and enterprise platforms such as enterprise resource planning (ERP), manufacturing execution systems (MESs), and quality management systems (QMSs).

Successful Transformation Starts with a Clear Business Outcome

Project complexity alone does not determine whether a LIMS transformation will succeed. The discussion highlighted that complex projects can progress smoothly when teams have a clear vision and disciplined execution, while less complex initiatives can stall when requirements, responsibilities, or timelines are not properly defined.

Three priorities can help organizations keep transformation projects on track:

• Define the intended outcome: Laboratories should begin by identifying what they want to achieve and what success will look like. Clear outcomes, such as shorter cycle times or faster batch release, give everyone a shared goal.
• Set a realistic scope: Teams need to identify the systems, samples, storage units, sites, workflows, quality touchpoints, and stakeholders involved. The scope will often cover a specific part of the organization rather than everything at once.
• Plan for every stage: Data migration, software and hardware integration, validation, and end-user training are essential parts of implementation. Bringing the right stakeholders into the process early gives the project a better chance of succeeding.

This disciplined approach helps laboratories connect implementation decisions with measurable outcomes and build a stronger case for moving from a pilot to an enterprise investment.

AI Value Depends on Trusted, Contextualized Data

AI is creating practical opportunities in laboratory operations, but its value depends on the quality and context of the data it receives. Laboratories need data that is available, readable, and connected across systems before AI can support meaningful analysis.

One example discussed during the webinar involved using AI to collect information from papers, Excel sheets, and different systems to create master data. This enabled the organization to complete the process in approximately 10% of the time it would otherwise have taken, with a person reviewing the information before it moved to the next stage.

Three priorities emerged from the discussion:

• Make data ready: Information needs to be available in a readable format and organized so that data from different sources can be related.
• Build a trusted foundation: Unique identifiers and standardized fields can help connect data across systems. A reliable data foundation allows AI to address specific scientific questions using the available sample information.
• Keep people involved: AI can assist scientists by bringing together relevant information, trends, and alerts. However, people should continue to review the results, make decisions, and approve changes.

LIMS can support this approach by organizing and connecting laboratory data in a standardized way, making it more useful for AI-driven analysis.

Interoperability Will Define the Connected Lab

A typical laboratory environment includes instruments, scientific applications, and enterprise systems from multiple vendors. Bringing these technologies together remains a key challenge, particularly when each system stores and manages information differently.

Three capabilities are particularly important:

• Centralized sample visibility: Scientists need a single view of sample data, including how a sample was collected and stored, as well as any restrictions on its use. This helps them determine whether it is fit for purpose.
• Integration with existing systems: Laboratories already use specialized software and equipment for different scientific activities. LIMS must work with these existing technologies rather than operate as another isolated system.
• Standardization with flexibility: Common data fields make it easier to bring sample information into one view. At the same time, laboratories need the flexibility to configure fields, workflows, reports, dashboards, and integrations around their scientific requirements.

A vendor-agnostic approach can support this connectivity by allowing LIMS to work with systems and instruments from different providers. This can help laboratories create a more unified digital environment.

From Automation to Autonomy for Scientists

The next stage of laboratory transformation is not simply to automate more tasks, but to give scientists greater autonomy to focus on science. This means reducing the time they spend searching for information, reviewing records, and preparing for routine laboratory work.

AI could act as a laboratory partner by bringing together relevant sample information, historical trends, and alerts when an analyst begins work. Auto-scheduling and integration with other laboratory systems could also help ensure that the required materials and inventory are ready before testing starts.

The aim is to support scientists, not replace their judgment. With the right information available at the right time, they can spend more time asking new questions, identifying problems, and working toward meaningful scientific solutions.

Where Next-Generation LIMS Can Deliver Greater Value

The discussion highlighted three areas where next-generation LIMS could deliver additional value:

For laboratories, the opportunity lies in using LIMS to turn connected data into practical value, whether by accelerating routine work, making existing sample collections more useful, or strengthening audit readiness.

Click here to access the full discussion’s recording.

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