How Automation Technology Is Helping Laboratories Scale Their Operations

Laboratories face a familiar scaling problem. Demand rises steadily, the work itself does not change, and the only obvious way to process more of it is to add people and hours. That approach has a ceiling. Sample numbers tend to grow faster than headcount, and the work absorbing those extra hours rarely requires a trained scientist.

Each additional operator also introduces their own technique, which becomes a variable in its own right.

Automation technology has become the practical response. It is generally applied not to the analysis, which is usually automated already, but to the manual preparation upstream of it.

Where Laboratory Time Actually Goes

The instruments that produce results are often not the constraint. A real-time PCR run takes a fixed amount of time, and that time does not change with the size of the workload around it.

The constraint sits earlier, in sample preparation. Assay setup, reagent dispensing, aliquoting, serial dilutions, plate normalisation and library preparation for sequencing all come down to moving small volumes of liquid between vessels, accurately, hundreds or thousands of times. Filling a single 96-well plate requires 96 individual transfers for every reagent added, and most protocols add several.

Manual preparation is slow and difficult to schedule around. It also does not compress, because capacity scales only with the number of trained hands available on the day.

Automating The Repetitive Middle Of The Workflow

This preparation layer is what liquid handling instruments are built for. They execute programmed transfer protocols, aspirating and dispensing defined volumes between tubes, plates and reagent reservoirs, repeating the same movement on every run.

The immediate gain is time returned to staff. A protocol saved in software also functions as a specification, so the workflow no longer depends on individual technique. Execution becomes repeatable, and repeatable execution is what makes a process safe to scale.

Not every protocol benefits equally. Transfers that are numerous, identical and well defined automate cleanly. Exploratory work involving frequent judgement calls does not.

Consistency Is Usually The Bigger Win

Throughput is often the reason automation is proposed. Reproducibility is frequently what changes the numbers.

Skilled technicians pipette accurately, but small differences accumulate across large batches, between operators and between runs. Those differences appear later as variability in the data, and variability means repeat work. A repeated run consumes reagents, instrument time, and the days between a sample arriving and a result leaving.

The same logic applies across sites. Running an identical protocol on the same platform removes one source of difference between two facilities, even though reagent lots and individual instruments still need accounting for.

Automation does not remove the need for good laboratory practice. Maintenance schedules, consumable selection and cleaning procedures still matter, and a poorly designed protocol runs poorly whether a person or an instrument executes it.

Standardise The Protocol Before You Automate It

Automating a process that has never been documented properly surfaces every undocumented decision at once. The usual failure is not technical. It is that steps described as standard turn out to vary between operators.

Documentation comes first: volumes, sequences, quality control checks, and any variation that needs resolving before programming begins. The next question is which workflows justify automation, which generally means the repetitive, high-volume ones. Practical fit is the last check: bench space, labware compatibility, whether the instrument must sit inside a biosafety cabinet, and how it hands off to the next stage.

Throughput planning deserves more attention than it usually receives. A system sized only for current volumes becomes a constraint again as demand rises, while an oversized platform adds complexity a small lab may never need.

What Scaling Looks Like Afterwards

Labs that automate well do not necessarily process any single run faster. What changes is that capacity stops being tied to available staff hours.

Larger batches become routine rather than disruptive. Adding an assay means writing a protocol rather than finding people to run it, and staff spend their time on method development, troubleshooting and analysis.

The shift also makes the work portable. Compact systems now travel to field sites for crop disease testing, environmental monitoring and veterinary diagnostics in remote areas, running the same validated protocols used on the bench. Scaling, in that context, means running a workflow somewhere it previously could not run at all.

For labs weighing this up, the first step costs nothing. Count the individual liquid transfers in one high-volume workflow, then multiply by the number of times it runs each month. That figure usually settles the question faster than a specification sheet does.