Lab automation: from isolated instruments to integrated workflows

Lab automation explained in simple terms

Automation refers to apparatus, processes, or systems that achieve outcomes with minimal human input. In laboratory automation, this means integrating automated technologies into the lab workflow to enable new and improved processes.

These technologies include:

• Instruments and devices that perform tasks such as sample preparation, analysis, and storage (e.g., autosamplers, liquid handlers, spectrometers, chromatographs).
• Supporting devices such as barcode readers and RFID tags that track samples throughout the lab.
• Software and connectivity that orchestrate data collection, analysis, and reporting.

Lab automation is not just about buying a single robot or instrument. It involves designing an end-to-end workflow where automated steps connect, data flows reliably, and people interact with the system safely and efficiently.

Where an automatic lab can make the biggest impact

Automated technologies can be adopted into almost any part of the lab workflow.

The most common are:

• Instrument data collection and analysis: Automated data capture from instruments, integration with LIMS or other informatics systems, and automated analysis pipelines reduce manual transcription and calculation errors.
• Sample processing: Tasks such as aliquoting, mixing, incubation, and washing can be automated to improve precision and reduce cycle times.
• Sample management: Automated sample identification, tracking, and routing (for example, via barcode readers, RFID tags, and conveyor or robotic systems) help prevent misplacement and mislabelling.

Beyond these, many labs also automate:

• Sample preparation (e.g. automated pipetting, capping/decapping, reagent addition)
• Sample storage and retrieval (e.g. automated freezers and archives)
• Inventory management and sample libraries
• Procedure execution for standardised assays

The more these areas are connected, the closer a lab comes to true end‑to‑end lab automation.

Why automate? Core drivers behind lab automation

Protecting personnel safety and well-being

Even in an automatic lab, humans remain essential. It is therefore critical to protect personnel from harm and support their well-being. Manual, repetitive tasks such as capping and decapping vials or extended pipetting sessions carry significant risks:

• In one Red Cell Serology laboratory, the average technician decapped between 800 and 1000 tubes per shift — a workload associated with repetitive strain injury (RSI) and reduced quality of life.
• A survey found that almost 90% of subjects using a pipette for more than 60 minutes reported hand complaints.

By automating these high‑strain, low‑value tasks, labs can:

• Reduce the risk of musculoskeletal injuries and repetitive stress.
• Limit exposure to hazardous substances and infectious materials.
• Free personnel to focus on more mentally stimulating, higher‑value work.

The hidden challenge: partial automation in the lab

From manual workflows to partially automated labs

Many laboratories start automation by adding individual automated instruments to address specific bottlenecks, such as an auto-sampler, liquid handler, or automated storage system. This is a natural first step but can create a patchwork of partially automated processes.

Labs without holistic automation plans may face:

• Reduced instrument connectivity: Instruments purchased at different times may not be compatible. Integrating them later can be complex and costly.
• Incompatible labware and infrastructure: existing labware may not fit new automated platforms. Labs may need to replace labware or adapt physical infrastructure, for example, if large equipment cannot fit through standard doorways.
• Limited flexibility and scalability: traditional automation platforms can be rigid and hard to modify. Labs that partially automate without considering future needs may struggle to change workflows or move toward full end-to-end automation.

Partial automation can solve immediate problems but create new ones if not aligned with a long-term strategy.

Moving towards end‑to‑end lab automation

To avoid these pitfalls, labs need an overall plan for automation from the start. This does not mean automating everything at once. Instead, it means:

• Defining a long‑term vision for how the lab should operate.
• Identifying which steps will be automated first and how they will connect later.
• Selecting technologies and partners that support modular, scalable integration.

Linking workflow automation technologies can be done in stages, but each should move the lab closer to a coherent, end-to-end automated workflow. Engaging an experienced automation partner early helps ensure each investment fits the bigger picture and sets the lab up for future growth.

In practice: real‑world examples

Fast MDx: rapid molecular diagnostics with minimal hands‑on time

Fast MDx is a compact, fully automated molecular diagnostics platform delivering fast, reliable results with minimal manual intervention. It combines automated sample preparation, assay setup, amplification, and detection in a single integrated system.

This allows laboratories to:

• Sample in parallel with short turnaround times.
• Reduce manual pipetting and handling steps to lower the risk of contamination and error.
• Standardise workflows to ensure consistent results across runs and operators.

Fast MDx is a practical example of how end‑to‑end lab automation can help clinical labs respond to fluctuating testing demand while maintaining high quality. This use case can be explored in more depth in a dedicated Fast MDx blog post.

5 success factors for your lab automation journey

When moving toward a more automatic lab, technology is only part of the story. Several critical factors determine whether lab automation delivers full value. Below are practical, action-oriented considerations.

1. Plan a long-term automation strategy: define an overall concept for how your lab should operate in the future, so each automation step fits into a scalable, end-to-end workflow rather than creating isolated, rigid automation islands.

2. Engage your team early: involve scientists, technicians, quality, and IT from the start to map real workflows, surface constraints, and build buy-in for new automated processes.

3. Evaluate cost-effectiveness holistically: look beyond purchase price to include integration, training, infrastructure changes, and savings from higher throughput, fewer errors, and reduced injury risk.

4. Set a realistic, phased timeline: Automate in stages, prioritising high‑impact areas while allowing time for validation and optimisation, so you minimise disruption and avoid rushed, sub‑optimal decisions.

5. Choose the right automation partner: Work with an experienced partner early on to avoid pitfalls of partial automation, such as poor instrument connectivity and incompatible labware, and to ensure your setup can evolve with future needs.

To explore each of these factors in depth, including practical examples and pitfalls to avoid, download our dedicated guide to successful lab automation.