Automation 101: The challenges of laboratory automation implementation

Labs in 2026 face mounting pressure to deliver faster, more reproducible results with tighter budgets and smaller teams. Automation promises a way forward with higher throughput, better data integrity, and fewer errors. But many labs get stuck before they even start. The options feel overwhelming, the risks seem high, and it's hard to know if automation will actually solve your problems or create new ones.

If you're looking at lab automation but aren't sure where to begin, this post walks through the most common challenges of lab automation and what you can do about them.

Choosing the Right Starting Point

Navigating instruments, platforms, and vendors

Walk into any automation conversation, and you'll hit a wall of options: liquid handlers, robotic arms, mobile robots, track systems, and software platforms. Do you start with one instrument? A full workcell? Something off-the-shelf or designed from scratch?

Labs often waste months evaluating technologies that don't match their actual needs, or they end up picking something that looks impressive but doesn't fit their workflow.

Questions to define your first automation project

Before you talk to vendors or compare platforms, ask yourself:

• What's the biggest bottleneck in my workflow right now?
• Which instruments or steps would give me the most time back if automated?
• Do I need to run samples overnight, or just speed up a specific prep step?
• What constraints do I have—space, budget, staff expertise?

Why a focused pilot beats "automate everything"

It's tempting to think big, but trying to automate everything at once is how projects stall or fail. Start with one high-impact workflow. Pick something repeatable, measurable, and annoying to do manually.

A focused pilot gives you a win you can point to, teaches your team what automation actually requires, and makes it easier to justify the next step.

The Learning Curve: Skills, Training, and Change Management

Skills and expertise gaps

Lab automation implementation challenges often come down to knowledge gaps. Most labs don't have automation engineers on staff. Scientists know their assays inside and out, but scripting workflows, troubleshooting integrations, and managing networked systems? That's a different skill set.

Change management in the lab

People resist what they don't understand or trust. Lab staff who've done things manually for years might see automation as a threat or just another thing that's going to break and make their day harder. Skepticism is real, and it's often justified. Bad automation implementations disrupt workflows more than they help.

Lab quality depends on buy-in. If your team doesn't trust the system or see the benefit, they'll find ways to work around it.

Practical ways to reduce the learning curve

Identify automation champions in your lab who are curious, tech-comfortable, and respected by their peers.

Plan for training upfront. Biosero offers comprehensive training programs and on-demand resources, so your team can learn at their own pace.

Run pilot projects that let people experiment without pressure.

Budget Constraints and Total Cost of Ownership

Beyond the sticker price

Labs focus on purchase price because it's the number they see upfront. But automation costs don't stop there. You're also paying for implementation, training, maintenance, software licenses, and the internal time your staff spends getting the system running. Barriers to lab automation often come down to underestimating these hidden costs.

If you're budgeting only for hardware, you're setting yourself up for surprises. And if you're working with legacy systems, integration costs can add up fast.

Framing ROI in realistic terms

Automation is less about cutting headcount and more about doing more with the people and instruments you already have. The ROI might show up as:

• Reduced hands-on time so your team can focus on higher-value work
• Higher throughput without hiring more staff
• Fewer repeat runs because of reduced errors
• Faster turnaround time
• Better data integrity for regulatory submissions
• Improved lab safety with less manual handling
• Enhanced sustainability through optimized resource use

Put numbers to these where you can. If a scientist spends 10 hours a week on manual plate prep, that's roughly 500 hours a year. What's that time worth?

Strategies to make the business case

• Run conservative and expected scenarios. Show the business case if things go well—and if they go okay.
• Ask vendors for ROI models based on your specific workflow.
• Compare options. A standardized workcell might cost less and deploy faster than a fully bespoke build.

Data, Compliance, and Integration Challenges

Connecting automation to LIMS, ELNs, and instruments

Automation doesn't live in a vacuum. Your workcell needs to talk to your LIMS, pull sample lists from your ELN, and push results back to analysis tools. If those connections don't work smoothly, you end up with manual data transfers. Integration challenges are one of the biggest reasons labs get burned by automation.

Traceability and regulatory expectations

If you're in a regulated environment, you need audit trails, user access controls, and electronic records that meet compliance standards.

Even non-regulated labs care about traceability. If something goes wrong with a batch, you need to know exactly what the system did and where the problem started.

Designing data flows from the start

Don't treat data integration as an afterthought. Before you deploy automation, map out where sample data comes from, where results need to go, and what metadata needs to be captured at each step.

Automation scheduling platforms such as Green Button Go Scheduler and GBG Orchestrator are designed to handle these data flows across instruments and software platforms.

Key Takeaways for Planning Your First Automation Project

Implementing lab automation doesn’t have to be overwhelming. The key is breaking it into manageable pieces:

1. Start with a clear, specific problem instead of trying to automate everything at once.
2. Understand the total cost of ownership, not just the purchase price.
3. Plan for training and change management from day one.
4. Design data flows and integrations upfront, not after the system arrives.
5. Work with a partner who gets it—someone who's helped labs scale their operations before.

You don't have to solve every challenge before you start. Pick one workflow. Prove it works. Learn what automation actually requires in your lab. Then expand from there.

Want to talk through what automation might look like in your lab? Contact us to discuss your workflows or learn more about Green Button Go software and ready-to-run workcell options.