Automation shouldn't be reserved for enterprise labs with dedicated automation teams and million-dollar budgets. Yet that's how it feels for many smaller research teams: like automation is something you graduate into after years of manual pipetting and spreadsheet chaos.
The reality is different. Accessible lab automation isn't about shrinking down high-end systems or accepting watered-down capabilities. It's about designing automation that works for the lab you have today while leaving room to grow tomorrow. It's automation built around your workflows, your existing instruments, and your team, not the other way around.
If your lab has been holding off on automation because it seems too complex, too expensive, or too risky, this post breaks down what makes lab automation accessible for labs of any size and maturity level.
What Accessible Automation Really Means
Accessibility is more than "low-cost equipment"
When labs think about making lab automation affordable, the conversation usually starts and ends with purchase price. But accessible automation is about more than finding the cheapest liquid handler or simplest robotic arm.
True accessibility means automation that fits across multiple dimensions:
- Budget: Not just upfront cost, but total cost of ownership, including implementation, training, maintenance, and upgrades
- Staffing: Systems that work for 3–10 person teams without requiring a dedicated automation engineer
- Skills: Interfaces and workflows that scientists can learn without coding bootcamps or vendor dependency
- Data and integration: Tools that connect to your existing LIMS, ELNs, and instruments without enterprise IT projects
- Physical space: Configurations that don't require lab redesigns or dedicated automation suites
Making lab automation fit your existing lab
One of the biggest barriers to automation is the assumption that you need to start from scratch. Rip out your existing instruments. Redesign your lab layout. Retrain your entire team.
Accessible lab automation starts from where you are today. It works with the liquid handlers, readers, incubators, and other instruments you already own. It fits into your current physical footprint. It builds on workflows your team already knows instead of forcing them to learn entirely new protocols.
Labs don't need a full redesign to see automation wins. Early progress often comes from automating focused, high-friction steps: plate handling between instruments, overnight incubations, repetitive sample prep. These incremental improvements prove value quickly and build confidence for bigger projects later.
Aligning lab automation with people, process, and data
Technology alone doesn't make automation accessible. The best-designed workcell still fails if scientists don't trust it, can't use it, or don't have the training to run it effectively.
Accessibility requires thinking about three layers:
- People: Intuitive interfaces, clear error messages, and training that sticks. If only one person can operate the system, it's not accessible.
- Process: Clear protocols, documented workflows, and transparent performance. Scientists need to understand what the system is doing and why.
- Data: Connected data flows that track samples from prep through analysis. Automation that creates data silos or manual transfer steps just shifts the bottleneck elsewhere.
When these three layers align, automation becomes a shared tool rather than an imposed system. Labs that involve end users early (getting input on interfaces, workflows, and pain points) see better adoption and fewer surprises during implementation.
Making Automation Feasible for Smaller and Resource-Limited Labs
Starting small with accessible lab automation workflows
The fastest path to first steps in lab automation is picking one workflow that's repetitive and time-consuming to do manually and automating just that. Not the entire lab. Not every assay. One specific, repeatable, high-friction process.
Examples of good starting points:
- Sample prep: Automating serial dilutions, normalization, or reformatting
- Plate handling: Moving plates between liquid handlers, readers, and incubators without someone standing by
- Overnight runs: Scheduling time-sensitive steps that currently require weekend or late-night lab visits
Modular lab automation systems work well here because they let you start with a focused workcell (one or two instruments plus scheduling software) and expand when you're ready. Pre-configured setups reduce the planning burden and get labs running faster than building everything from scratch.
The goal isn't perfection. It's proving that automation works for your team, your workflows, and your constraints. Once you have that win, expanding becomes easier to justify.
Using existing instruments in your lab automation strategy
Many labs assume automation means buying all new equipment. That's rarely true, and it's definitely not necessary for lab automation for small labs.
Accessible automation is built on integration, not replacement. If you already have Hamilton liquid handlers, Tecan readers, or Liconic incubators, scheduling software can coordinate them into automated workflows without touching the instruments themselves. The automation layer sits on top, orchestrating when each device runs and where samples move next.
The key is working with software platforms and automation partners who support open integrations. Systems that connect to hundreds of devices through standard drivers and APIs rather than locking you into proprietary ecosystems. Biosero's approach to automated liquid handling is a good example: integrate what you have, add what you need, and keep everything coordinated through scheduling software.
Designing Automation That Works for People, Not Just Instruments
Interfaces scientists will actually use
The best automation in the world doesn’t help if it’s too difficult for the team to use consistently. Accessible lab automation depends on interfaces that are built for scientists and day-to-day lab work—not just automation specialists. The goal is to reduce, not eliminate, technical complexity.
That means:
- Intuitive UIs that reduce reliance on manuals for common tasks.
- Clear run status so users know what's happening, what's queued, and when results will be ready.
- Simple error handling with plain-language messages instead of cryptic codes.
- Configurable workflow builders where appropriate, so updates can be made without rewriting scripts for every change.
If only one person in your lab feels comfortable running the automation, accessibility breaks down. The system should be usable by trained team members across roles and experience levels.
Training, documentation, and ongoing support
Even user-friendly systems need proper training. Accessible automation includes structured onboarding that covers not just "how to press this button" but "what to do when something goes wrong" and "how to modify this protocol safely."
Role-based training works well here. Scientists need to know how to queue samples and monitor runs. Lab managers need visibility into throughput and error rates. Automation champions need deeper knowledge about troubleshooting and workflow optimization.
Biosero supports this through GoCare, a subscription offering designed to help teams adopt and scale automation over time. GoCare includes resources like the Biosero Portal, the Green Button Go (GBG) Academy, and virtual training, so teams can learn at their own pace, revisit materials as workflows evolve, and build internal confidence across roles.
Documentation matters too. Accessible systems come with clear SOPs, troubleshooting guides, and resources scientists can reference without waiting for vendor support calls.
Building trust and adoption around lab automation
Trust doesn't come from promises. It comes from transparent performance and quick wins. Labs adopt automation when they see it working reliably and delivering the benefits they were told to expect.
Early in implementation, focus on building confidence:
- Run transparent pilots where the team can see exactly what automation is doing and compare results to manual processes
- Celebrate small wins like overnight runs that finished successfully or error-free plate handling
- Involve end users in workflow design so automation feels like a shared tool, not something imposed from above
Keeping Automation Accessible as the Lab Grows
Scalable lab automation configurations that grow with you
Accessible automation shouldn't box you in. The workcell that makes sense for your lab today should be able to expand as throughput demands increase, new instruments arrive, or protocols evolve.
Scalable lab automation starts with modular designs: systems where you can add devices, connect new workcells, or extend workflows without rebuilding everything from scratch. A single-instrument setup might grow into a multi-instrument workcell. That workcell might eventually connect to other workstations across the lab through orchestration software.
The key is planning for growth without overbuilding for it. You don't need a 20-device workcell on day one. But you should choose automation that can grow incrementally as your needs change. Labs successfully scale sample processing when their automation architecture supports expansion, not replacement.
Flexible lab automation data and integrations that don't box you in
Nothing kills accessibility faster than automation that creates data silos or locks you into proprietary formats. If your scheduling software can't talk to your LIMS, or your workcell data lives in a closed system you can't export, you've traded one bottleneck for another.
Accessible automation prioritizes open integrations: APIs that connect to standard data systems, device drivers for common instruments, and database connections that let you pull data into your own analysis tools. This flexibility matters both today and down the road. When you add new instruments, switch LIMS providers, or adopt new analysis software, your automation should adapt without major reengineering.
This is especially important for labs managing changing research priorities. When your focus shifts from drug screening to cell therapy manufacturing, or from NGS library prep to molecular diagnostics, your automation needs to pivot with you. A flexible data and integration architecture makes that possible.
Taking the First Step Toward Accessible Automation
Automation doesn't have to be overwhelming. The labs that succeed start with clear problems, focused solutions, and realistic expectations. They pick one workflow that's painful to do manually, automate it well, and build from there.
If you're not sure where to begin, review the challenges of lab automation implementation to understand common barriers and how to navigate them strategically. For labs in specific application areas like diagnostics, seeing how automation scales from small to large setups can help clarify what's possible at your current stage.
The right automation partner makes a difference. Look for teams that understand accessible automation means working with what you have, properly training your staff, and building systems that grow with you. Ready to explore what automation could look like in your lab? Contact us to talk through your workflows, constraints, and goals.
Automation for labs of any size isn't about compromise. It's about meeting you where you are and helping you move forward at a pace that works for your team.
Let’s get started—talk to an automation expert to map your first workflow.
