The 3 Hidden Gaps Sabotaging Your Laboratory Automation

Originally published as Part 1 of a two-part LinkedIn series on why laboratory automation projects underperform.

Automation does not fix a broken process. It amplifies whatever is already there.

Laboratory automation underperformance is not inevitable.

What 25 Years of Advisory Has Taught Us

LTS Global has been involved in laboratory automation advisory since the first wave of total laboratory automation reached clinical laboratories at scale.

Our work has spanned multiple continents and a wide range of environments, from high-volume clinical chemistry operations to specialized research facilities. We have covered the full lifecycle of automation investment: selection, facility design, readiness assessment, implementation support, and post-implementation performance review.

Over that period, a consistent pattern has emerged. It does not appear in every project, but it appears often enough to be structural rather than incidental.

The pattern is this: Laboratories that invest in automation without first developing the analytical capability to understand their own operations tend to automate their existing constraints rather than eliminate them.

The automation performs exactly as specified by the vendor. Yet, the operational problems persist.

Why does this happen? The reasons vary by context, but they almost always cluster around three recurring gaps.

The Three Gaps That Determine Automation Success

Gap 1: Process understanding precedes technology selection, but rarely does.

The most common failure mode in automation advisory is a laboratory that selects a platform based on throughput specifications, vendor relationships, and capital availability.

What they fail to do first is map the operational constraints that are actually limiting performance.

The result is a system that processes specimens faster through a workflow that was never designed for the volume or complexity it now handles. Automation selection, when done correctly, begins with a structured operational assessment.

Which constraints are pre-analytical? Where does rework originate? Where does turnaround time actually break down?

The answers to these questions should determine the specification, not the other way around. In practice, the assessment is often abbreviated or skipped entirely. The technology decision is made first, and the process is adapted to fit it afterward.

Gap 2: Readiness is assessed for the technology, not for the organization.

Automation readiness assessments in most laboratory contexts focus heavily on infrastructure: space, utilities, IT connectivity, specimen volume, and test menu compatibility.

These are necessary conditions, but they are not sufficient ones.

The organizational readiness question is different. It asks whether the laboratory team has the analytical capability to operate, monitor, and continuously improve an automated system once it is running.

Automation introduces entirely new categories of operational complexity. Track errors, middleware dependencies, analyzer interdependencies, and workflow bottlenecks only become visible at scale. Managing these requires a different kind of operational thinking than managing a manual laboratory.

Most readiness frameworks do not assess this. The assumption is that the team will learn on the job. For many laboratories, that assumption has proven highly expensive.

Gap 3: Post-implementation performance is monitored rather than managed.

The third gap appears after go-live. Laboratories invest significantly in implementation and commissioning, but very little in structured post-implementation performance management.

The system is handed over, KPIs are tracked, and improvement is expected to emerge naturally from operational experience.

What typically emerges instead is a stable but suboptimal steady state. The system is running, and targets are broadly met. However, the deeper performance potential of the automation investment is never realized because no one has been given the tools to interrogate it systematically.

This is where the largest unrealized value in laboratory automation sits, not in new technology, but in the structured capability to extract performance from the technology already installed.

What Happens Next?

When these three gaps go unaddressed, laboratories eventually become frustrated with their system's performance. This frustration leads to one of the most expensive mistakes in the industry: the replacement cycle.

The original series continues with "The Curse of the Incumbent," which examines why buying a new system rarely solves the problems created by the old one.

Are you preparing for an automation upgrade or struggling to get the expected value from your current system?

Let's ensure you are solving the right problems before you make your next capital investment. Contact LTS Global to discuss a structured operational assessment.