Liquid transfer is the one step almost every molecular protocol has in common, and it is usually the step treated as overhead rather than as a variable worth controlling.
That framing survives a handful of samples. It stops surviving once protocols involve hundreds of transfers, or volumes small enough that a fraction of a microliter matters, or results sensitive enough that a transfer error and a biological signal become hard to tell apart.
Those pressures are also driving demand for automated liquid handling. Coherent Market Insights estimates the automated liquid handling systems market at USD 3.5 Bn in 2026, reaching approximately USD 5.56 Bn by 2033. Growing sample workloads, wider use of sequencing and pressure to make expensive reagents go further support that growth. Each increases the need to transfer the right amount repeatedly, without adding more manual work every time the workload grows.
The gains from automated liquid handling workflows are not evenly spread, though. Some processes absorb manual pipetting without much cost. These six do not.
1. qPCR And Assay Setup
Reaction setup is usually the highest-volume transfer task in a molecular laboratory. Master mix into every well, template into every well, controls positioned correctly, and the plate assembled before anything degrades.
Quantitative results are sensitive to exactly the thing manual setup struggles to hold steady. A volume deviation shifts the effective concentration in that reaction, and the shift appears in the amplification data as though it were biological. Separating a genuine difference between samples from a pipetting artefact after the run is rarely straightforward, because both look the same in the output.
The structural problem is that setup errors are invisible at the point they occur. Nothing about a plate reveals which wells received a slightly short transfer, and by the time the run produces data, the evidence of what went wrong has been consumed.
Smaller reaction volumes put that weakness under more pressure. Laboratories can stretch reagent budgets by reducing the amount used per reaction, but the acceptable margin for a transfer error shrinks with it. This makes miniaturisation a reason to automate, provided the method still performs reliably at the smaller volume.
2. NGS Library Preparation
Library prep is long, reagent-heavy, and unforgiving. Fragmentation, end repair, adapter ligation, clean-up, amplification, each stage feeding the next.
Errors here compound rather than average out. An under-delivered adapter volume cannot be corrected downstream, and the consequence often surfaces only when sequencing data returns and one library sits underrepresented in the pool. By then the reagent cost and the instrument time are spent.
Protocol length adds a second problem. Multi-hour workflows built from near-identical transfer steps are not conditions under which sustained manual precision comes easily, and a single lapse carries through every stage that follows. Automation holds the same tolerance at the final step as at the first, which is the part of the workflow where manual consistency is under most pressure.
Keeping those stages together reduces the number of times an operator has to move the process along. Platforms increasingly bring transfers and incubations into one workflow, reducing manual handoffs. How much of that sequence can be automated depends on the equipment. The market covers pipettes and consumables, microplate reagent dispensers, automated workstations and other systems.
Workstations are expected to account for 47.5% of the automated liquid handling systems market in 2026. Larger sample batches and longer protocols support their adoption because several operations can run on the same platform. Library preparation makes that useful because consistency has to survive the whole sequence.
3. Serial Dilutions
Dilution series are the clearest case in the list, because the maths works against the operator from the start. Each step takes its input from the step before, so a handling error does not stay where it happened. It carries forward into everything downstream of it.
A relative deviation at the first step is still present at the eighth, sitting underneath whatever new deviation that step introduces. Nothing corrects it. A standard curve built on a drifting series produces quantification that looks internally consistent and is quietly wrong, which is worse than a curve that visibly fails.
This is where instrument-level consistency earns its place. An automated liquid handler performs every transfer in the series against the same programmed volume, the same aspiration profile and the same mixing routine, so the series does not inherit the drift that varying manual technique can introduce across repeated steps. Mixing is the part most often underestimated. An incompletely mixed dilution carries a concentration error into the next step regardless of how accurately the volume was transferred.
Tip handling matters as much as the volumes. Carryover between dilution points corrupts a curve as effectively as a volume error does, and it is harder to detect afterwards.
4. Sample Normalisation
Normalisation is a calculation problem as much as a transfer problem. Each sample needs a different volume, derived from its own quantification result, and no two wells match.
Done by hand across a full plate, it means reading a value, calculating a volume, setting the pipette and transferring, ninety-five more times, without a transcription slip. There is also no uniform expected volume to check the result against, so a mistake in the calculation is effectively invisible until the downstream data looks wrong.
Automated systems take quantification data directly and transfer per-sample volumes without the intermediate transcription step, which is where most of these errors originate.
Removing that transcription step also means connecting the measurement to the transfer. Systems with integrated quantification carry results into normalisation without requiring an operator to export values and rebuild instructions elsewhere. That connection becomes more useful as sample numbers rise because every additional sample brings another calculation, even when the protocol itself stays the same.
5. Assay Standards And Controls
Standards and controls carry more weight per well than samples do. A dilution error in a standard shifts the entire curve, and every sample quantified against that curve inherits the error. A mis-spiked positive control invalidates the check it exists to perform, which can send a laboratory looking for a problem in the samples that was never there.
The asymmetry is what makes this worth automating first in some laboratories. A handful of wells determines whether the rest of the plate means anything, yet they are usually prepared by the same hand, under the same time pressure, as everything else.
Repeat testing is the cost being avoided. A plate that fails on its standards consumes reagent, instrument time and an analyst's day, and produces nothing usable.
That burden helps explain demand from biotechnology and pharmaceutical companies, expected to account for 44.2% of the market in 2026. Expanding screening workloads and repeated assay development make consistent controls essential for comparing results across batches. Among the other end users, hospitals and diagnostic centres need dependable preparation for testing, while research and academic institutes need experiments to remain comparable across operators.
In U.S. laboratories, drug discovery, molecular testing and genomic research put these preparation demands alongside one another. As workloads expand, the pressure comes from keeping samples moving without allowing preparation to become the bottleneck. More screening plates need more controls; more sequencing samples need more libraries. Each adds transfers that have to remain consistent, even when turnaround times leave little room for repeat work. That combination supports demand for automated handling that adds preparation capacity and limits repetitive manual work. Systems that fit established assays and connect sample measurements to transfers address both the volume of work and the consistency it requires.
6. Quantification And Pooling
Pooling concentrates everything upstream into a single tube. Many samples go in, one library comes out, and the proportions determine how sequencing depth is distributed. Over-represent one sample and another may not reach usable coverage.
What separates pooling from the earlier workflows is that the error is not isolated to the sample that caused it. One inaccurate transfer changes the proportions for everything else in the tube. Given that input volumes are typically small and rarely identical between samples, this is among the least sensible places to rely on manual transfers.
Suppliers including Tecan and Hamilton are building around that need for connected preparation. Their approach brings library preparation, quantification, normalisation and pooling into automated workflows, reducing the manual work left between stages.
Across these six workflows, a small transfer error can outlast the step that caused it. Automation earns its place where keeping that step consistent prevents lost samples, repeated runs and results that are difficult to trust.
