Choosing the first lab workflow to automate is not just a tactical decision — it’s strategic. Think of it like choosing the first tile in a mosaic: the right piece sets a pattern and makes the rest easier. Pick the wrong piece, and you spend time and money forcing things to fit. In this long, practical guide I’ll help you pick that first tile. I’ll walk through a simple decision framework, show the common high-impact workflows that are perfect for early automation, explain how to pilot and validate them, and flag the traps that make expensive systems sit unused.
What “automate first” actually means in practice
Automating first means picking a single process or a compact set of tasks that will reliably deliver measurable improvements with limited risk. It’s not about installing a massive, integrated line on day one. It’s about finding tasks that are repetitive, stable, high-volume, error-prone, or ergonomically bad for staff, and applying modest automation to them. The first automation should be a learning project: small enough to pilot quickly but big enough to prove value.
A simple framework to choose your first workflow
You need a practical lens, not a checklist full of buzzwords. Ask four questions: is the task repetitive, is it stable, is it measurable, and is it high-impact? Repetitive tasks multiply labor costs; stable tasks don’t change every week; measurable tasks let you prove success; high-impact tasks influence experiment success or staff time dramatically. Workflows that pass most of these tests are excellent first candidates.
Why repetition and volume are your friends
Automation thrives when the same action repeats many times. Pipetting tens of plates per week, opening hundreds of tubes, or running dozens of ELISAs are the kinds of tasks where robots shine. The higher the volume, the faster the machine’s cost is amortized. If your lab does a step only occasionally, the time to develop and validate an automated method may not be worth it. If you run it repeatedly, the gains compound quickly.
Why protocol stability matters more than you might think
If your protocol changes every month, automation becomes a maintenance headache. Machines do exactly what you tell them; they don’t improvise. Stable, locked-down protocols are ideal because you only program and validate once and then reap continuous benefits. If your work is exploratory, pick tasks within the workflow that are routine — sample intake, aliquoting, or plate washing — instead of the changing experimental core.
Why measurable outcomes are critical
You’ll hear that automation “improves quality,” but vague claims don’t fund purchases. Choose a workflow where you can measure hands-on time saved, reduction in failed runs, throughput increased, or cost per sample reduced. These metrics make the case to your PI or finance team and guide continuous improvement.
Why ergonomic and safety issues are high-priority
Sometimes the highest-impact automation is not about data at all but about people. Repetitive strain from hours of pipetting, exposure during sample handling, or repetitive opening of cryovials are real risks. Automating these can reduce sick days, increase staff satisfaction, and even reduce liability. Ergonomic wins pay back in morale if not always on the balance sheet.
Low-hanging fruit: what “easy wins” look like
Low-hanging fruit are tasks that require small investments but produce visible advantages quickly. They are usually isolated, don’t require major lab rearrangement, and can be validated quickly. Good examples include barcode-based sample intake, tube decapping/recapping, benchtop pipetting of plates, automated plate washing, and data capture into a LIMS. These projects are approachable and widely applicable across disciplines.
Barcode-driven sample intake and accessioning
Automating the sample intake step is often the most underrated first move. Humans mistype IDs, forget metadata, and mislabel tubes. A barcode-based intake station captures sample identity and metadata as soon as the sample arrives, reducing lost samples and transcription errors. Because barcoding hardware is inexpensive and integration with LIMS can start simple, this step delivers immediate traceability and fewer downstream headaches.
Automated tube decappers, recappers, and sample handling
Opening and resealing dozens or hundreds of tubes is slow and rough on wrists. Decappers and recappers speed up sample prep and reduce the chance of splashes and aerosolization. They’re compact, straightforward to validate, and they directly reduce repetitive motion injuries. For labs with biobanks or heavy sample processing, this is a humane and cost-effective first automation.
Aliquoting and sample normalization systems
Aliquoting and normalizing sample concentrations are tedious tasks that are highly repeatable and directly affect downstream data quality. Automated aliquoters and normalization modules can produce consistent volumes and concentrations with less hands-on time. Because these devices are often benchtop and programmable, they fit well into small-to-mid-size labs and improve the quality of everything that follows.
Benchtop liquid handlers for PCR setup and plate transfer
Automating PCR setup or plate replication with a compact liquid handler is a classic early automation move. It replaces the most repetitive and error-prone parts of many pipelines. Benchtop robotic pipettors take up little space, are relatively affordable compared to full automation lines, and they provide big gains in reproducibility and throughput without a long infrastructure project.
Automated plate washers and ELISA processing
ELISAs and plate-wash steps are unusually friendly to automation because the protocol is straightforward, the plates are standardized, and the wash cycles are mechanical in nature. An automated washer produces uniform washes that reduce background and improve signal, cut hands-on time significantly, and are relatively simple to validate.
Plate readers with autosamplers for measurement bottlenecks
If measurement is limiting you, adding a plate reader with an autosampler turns measurement into a back-end process. You stack plates, let the instrument run, and collect data automatically. That frees people up and smooths workflows, especially for kinetic assays or workflows that benefit from unattended overnight runs.
Nucleic acid extraction platforms
Extraction is a step where variability and contamination risk can dramatically affect downstream success. Extraction robots standardize yield and purity, reduce hands-on time, and lower contamination risk when used correctly. Because they can be pricier than benchtop pipettors, consider extraction automation when your lab runs frequent, consistent throughput of nucleic acid preps.
Targeted automation for library prep when protocols are stable
Sequencing library prep is laborious but, in many labs, stable. If your library prep protocol doesn’t change often, automating key steps like cleanup, adapter ligation, and normalization can save many hours and improve reproducibility. The ROI here depends on volume and the cost of failed libraries; when those costs are high, automation pays back quickly.
Serial dilutions and plate replication: precision + speed
Serial dilution series and plate replication are perfect for machines because they require precise, repetitive transfers. Robots perform these flawlessly and remove cumulative pipetting error that humans inevitably introduce. If your assays revolve around dose-response curves or MIC testing, a compact automation step here will raise data quality immediately.
Colony pickers and plate spreaders for microbiology labs
Microbiology benchwork, with its repetitive streaking and subjective colony picking, benefits from automation that standardizes plating and colony selection. Basic colony pickers remove operator subjectivity and can dramatically speed workflows in labs performing routine cloning or library screening.
Sealing and de-sealing plates to protect samples
Automated plate sealers and de-sealers are small devices that solve a very real problem: evaporation, contamination, and inconsistent sealing. Sealing in a reproducible way improves long incubations and shipping prep, while de-sealers speed downstream processing. For labs that do many plate-based assays, this automation reduces sample loss and improves data quality.
Small-scale automated storage and retrieval
You don’t need a multimillion-dollar cold store to start automating storage. Compact plate hotels and small robotic shelves give quicker access to plates and reduce the time lost hunting for samples. Integrate simple storage automation with barcodes and a LIMS and you’ll quickly eliminate the time sink of manual sample retrieval and misplacement.
Robotic plate movers and basic orchestration
Moving plates between devices manually costs time and introduces variability. Robotic plate movers connect isolated devices and create mini workcells. They let you stitch together a sequence of tasks — wash, incubate, read — with minimal human intervention. This is a logical intermediate step toward more integrated automation and works well once several standalone instruments are in place.
Quality control runs and control sample automation
QC runs are rules-driven and repetitive. Automating controls ensures they are run consistently and on schedule, enabling robust monitoring of assay performance. This not only keeps data trustworthy but also enables early detection of drift and faster troubleshooting.
Inventory and consumable tracking as administrative automation
Automation isn’t just pipetting and robots. Tracking consumable use, monitoring inventory levels, and automating reorder alerts keep experiments from stopping cold because someone forgot to order tips. Simple barcode or RFID systems with lightweight software dramatically reduce the time spent managing supplies and prevent costly interruptions.
Instrument-to-LIMS data capture and integration
One of the biggest soft wins is automating data flow. Getting instrument outputs automatically into a LIMS removes transcription errors and frees analysts from manual data wrangling. Start simple with CSV imports or scheduled uploads, then tighten up to API integrations as you grow. Automating metadata capture improves traceability and reduces pains during audits or method transfer.
Workflows to avoid automating first
Not every procedure should be automated early. Avoid automating experimental steps that change frequently, require delicate hand judgment, or are low-volume. Exploratory protocols where conditions are being tuned, one-off dissections, or highly variable manual manipulations are poor first choices. Automating unstable processes simply locks in confusion and increases validation burden.
How to design a pilot for your first automation
A pilot should be short, focused, and measurable. Choose one well-defined task, run it manually and automated in parallel for a set period, and collect metrics on hands-on time, throughput, failed runs, and consumable usage. Include operators in pilot design so they own the change. Keep scope tight to avoid scope creep; the pilot’s goal is learning and proving value, not proving every edge case.
Validation: prove the automated method works
Validation is not paperwork theater — it’s the evidence that your automation reliably reproduces or improves upon manual outcomes. Define acceptance criteria up front: acceptable coefficient of variation, matching yields, or reduced error rate. Document the conditions, run enough replicates to show statistical confidence, and keep records for audits and publications. A robust validation shortens long-term headaches.
Training and change management: bring people with you
Automation changes job roles and routines. Invest in training, create quick-reference guides, and appoint champions who can mentor peers. Explain how automation removes tedious tasks and enables more interesting work. Early wins help; so do opportunities for staff to be involved in protocol tuning. People who feel ownership of automation are the reason it succeeds.
How to measure success and the KPIs that matter
Track hands-on time saved, change in failed-run rate, throughput increase, and cost per sample. Don’t forget soft KPIs like operator satisfaction, fewer ergonomic complaints, and faster turnaround time. Measure before and after in the pilot and use those results to justify further investment.
Common pitfalls and how to avoid them
Typical errors include automating poor processes, underestimating consumable costs, skipping validation, and relying on a single power user. Avoid these by improving the manual workflow first, modeling consumable expenses, building validation into the project, and cross-training users. Also watch out for vendor lock-in: verify consumables and data formats before committing.
How to scale after a successful first automation
If your pilot shows real gains, scale horizontally by automating the next highest-impact task, or scale vertically by adding plate movers and orchestration. Keep governance: protocol version control, change management, and periodic revalidation. Use lessons learned from the pilot to refine procurement, training, and support processes before larger buys.
Return on investment: realistic expectations
ROI varies. For repetitive, high-volume tasks you can often see payback in months. For more complex integrations, payback may take a few years. The key is realistic utilization assumptions and careful modeling of labor saved, reagent waste avoided, and new capacity enabled. Conservative scenarios are far better than optimistic hopes when you present the case to decision-makers.
Conclusion
Picking which workflow to automate first is both art and science. The best first choices are repetitive, stable, measurable, and high-impact. Start with small targets like barcode intake, aliquoting, benchtop pipetting, plate washing, or tube decapping. Run a tight pilot, validate rigorously, train people, and measure outcomes. When a first automation becomes a consistent win, use it as a learning platform and scale thoughtfully. Automation should multiply human creativity, not replace it; chosen and executed well, it becomes a lever that makes the whole lab work smarter.
FAQs
How long does it usually take to see benefits from my first automation pilot?
You can often see hands-on time improvements in the first week of regular use, but measurable benefits like reduced failed runs and throughput gains typically appear after a few weeks to a few months once you optimize protocols and train staff.
Can small labs afford to start automating, or is it only for big facilities?
Small labs can absolutely start. Many low-footprint, benchtop devices and barcode systems are affordable, and pilots can be run on leased or demo equipment. Focus on single-task automations that give fast wins rather than big integrated systems.
What is the single most common mistake labs make when choosing a first workflow to automate?
The most common mistake is automating an unstable or poorly documented manual workflow. Fix and standardize the manual process first; otherwise automation just makes the problem happen faster and on a larger scale.
How do we avoid vendor lock-in with an early automation step?
Prefer modular systems that use standard labware and open data formats. Validate third-party consumables if cost matters and clarify data export and API options before purchase to ensure future flexibility.
Should we automate sample intake or the core experimental steps first?
Automating sample intake is often a very good first move because it improves traceability and reduces downstream errors without changing experimental methods. If your core experimental steps are stable and repetitive, automating them can show bigger scientific ROI, but intake automation is a lower-risk, high-payoff starting point.
Thomas Fred is a journalist and writer who focuses on space minerals and laboratory automation. He has 17 years of experience covering space technology and related industries, reporting on new discoveries and emerging trends. He holds a BSc and an MSc in Physics, which helps him explain complex scientific ideas in clear, simple language.
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