Automated laboratory solutions: Why the next decade needs adaptive infrastructure

• To overcome complexity, laboratories must shift from rigid, monolithic architectures to modular, adaptive lab platforms 
• Integrating robotics, machine learning, and smart transport allows laboratories to dynamically optimize sample pathways in real time 
• Over the next decade, the focus of automated laboratory solutions will shift toward supporting personalized diagnostics and empowering clinical partnerships

While laboratory automation has advanced significantly over the past decade, many institutions still face immense operational complexity stemming from years of accumulated heterogeneity, fragmented IT landscapes, and rigid, monolithic system architectures. Today, high throughput and connectivity have become baseline expectations rather than competitive differentiators. To meet the needs of patients and navigate changing healthcare demands over the next decade, laboratories must evolve.

In this exclusive interview, Patrick Kammer, the International Business Leader for Automation at Roche Diagnostics, shares his perspective on the present status and future trajectory of the automated laboratory. Drawing from his experiences speaking with laboratory leaders across diverse markets, Kammer discusses the macro trends driving the industry and outlines a vision for the next generation of automated laboratory solutions.

For me, complexity is the accumulated heterogeneity that has developed within laboratories over many years. Large, historically grown laboratories have progressively connected additional disciplines to full automation and automated more process steps. Over time, these additions have created system breaks across both the IT and hardware landscape, with different vendors, user interfaces and increasingly complex connectivity.

The greatest source of friction lies, in my experience, on the IT side. Laboratories often operate multiple software solutions alongside the laboratory information system, creating numerous interfaces but no harmonized data foundation that can be used effectively. On the hardware side, laboratories face another important question: Should every instrument be connected to automation, or only those that improve throughput without compromising the overall workflow?

As a result, many laboratories have now reached a ceiling. Even highly automated laboratories still need to manage the complexity that has accumulated over time. As diagnostics continue to evolve, laboratories want to integrate new disciplines and testing capabilities, yet their historically grown infrastructure often becomes the limiting factor.

This is why modularity is widely discussed across the industry, but often remains more theoretical than real. Many solutions still rely on fundamentally monolithic architectures with limited opportunities for expansion. Complexity is no longer a question of automation itself, but of how the overall laboratory infrastructure has evolved over time.

The macro trends have remained remarkably consistent. Laboratories continue to face shortages of skilled professionals, increasing cost pressure and growing demands for efficiency.1  As skilled staff become scarcer, repetitive tasks increasingly need to be supported through automation and technology.

What has changed is not the challenges themselves, but the solutions available. Digital tools are becoming a standard for laboratory monitoring and retrospective analysis. Laboratories are now investing in infrastructure that can accommodate new workflows, additional functionalities and future innovations without requiring major system replacement, while minimizing the need for retraining as new capabilities are introduced.

In practice, efficiency remains essential, but has become the baseline rather than the differentiator. Instead of designing around today’s workload with a modest growth buffer, laboratories are looking for modular, platform-based solutions that can adapt to future requirements. That also changes how laboratories modernize. Rather than relying on disruptive “big-bang” migrations, they want to introduce innovation step by step during live operations. Their biggest concerns remain maintaining routine operations, managing organizational change and navigating complex IT migrations. Modular, scalable infrastructure enables that gradual transition while spreading investment over time.

What amazes me is that the appetite for innovation varies considerably. Some laboratory leaders remain focused on connectivity and throughput, while others are already exploring how machine learning can optimize workflows and how AI can augment human expertise by supporting  clinical decision-making and more personalized lab operations. And for many, the priority is simply easing pressure on their teams and making day-to-day work more manageable. These differences are shaped by geography, culture and funding models. Private laboratories, facing greater competitive pressure, often move faster than publicly funded institutions.

Robotics has become a global trend over the past six to seven years, helping laboratories compensate for shortages of skilled professionals by taking over repetitive manual tasks. This allows highly qualified staff to focus on activities where their expertise adds the greatest value.

AI and machine learning represent an even more fundamental shift. Today, laboratory workflows are largely rule-based: Samples follow predefined pathways through the laboratory according to fixed logic. I believe the next generation of systems will become increasingly self-learning. By analyzing sample profiles, understanding the laboratory’s real-time operating conditions, and learning from workload patterns and historical data, they will continuously optimize workflows rather than simply execute predefined rules.

This also addresses one of the long-standing frustrations for clinicians. Clinicians submit a sample without knowing when the result will become available. AI can improve transparency through reliable turnaround-time prediction while identifying bottlenecks and helping laboratories continuously improve their workflows.

Another important shift is the need for openness. Laboratories today want the flexibility to integrate innovations emerging across the market. Open platform ecosystems—across both hardware and software—provide the technological backbone that makes this possible.

But the real shift comes when you combine robotics, AI and next-generation transport technologies—and this is where it gets exciting. That’s when laboratories can adapt operations in real time. Instead of treating every sample the same, the system can make intelligent decisions based on clinical priority, test requirements, or available capacity. Combined with a modular platform architecture, this creates the foundation for a truly adaptive laboratory infrastructure.

Traditional laboratory automation is built around fixed, track-based systems, where scaling functionality or capacity has traditionally meant adding dedicated systems. While this has improved efficiency, it has also increased operational complexity, laboratory footprint and limited flexibility. Because sample volumes fluctuate throughout the day, analytical capacity is often sized for peak demand rather than average utilization.

Today, however, laboratories are being asked to do much more than increase throughput. They need to accommodate growing test volumes, support complex diagnostic workflows and respond to changing clinical priorities—all within the same space, workforce and financial constraints.

I believe that’s changing the way we think about designing solutions for labs.
Optimizing individual technologies remains essential, but the real question is how they come together as a platform that can evolve as laboratory needs change.

To me, that’s what adaptive laboratory infrastructure means. Rather than expanding laboratory automation by adding more systems over time, the future lies in building platforms around a unified architecture that enables continuous evolution—not only in throughput, but also by supporting new functionalities and workflows without proportionally increasing complexity.

Transport plays a central role in that transformation, and I’m not just referring to the connecting track between systems, but to how samples move throughout the laboratory. We’re now seeing the first generation of solutions where sample transport is becoming progressively smarter and more adaptive. I believe this evolution will soon enable laboratories to optimize each sample’s journey in real time according to clinical priority, workflow conditions and available capacity. In practice, that could mean applying individual turnaround-time targets to samples with different testing profiles, from different senders and clinical urgency, rather than one or two uniform targets. Over time, this will enable laboratories to make far more intelligent use of available space and analytical capacity, respond dynamically to changing demands and deliver more predictable turnaround times.

Ultimately, the challenge is no longer simply managing volume, it’s managing volume and variability at the same time. That requires infrastructure capable of continuously balancing clinical urgency, sample characteristics, workflow conditions and available analytical capacity. This helps labs flatten demand peaks, reduce bottlenecks, improve utilization and create the headroom needed for future growth.

Whether modernizing an existing laboratory or planning a new mega-laboratory, future-ready platforms are designed to expand step by step as laboratory requirements evolve, enabling continuous modernization instead of disruptive replacement cycles.

Adaptive laboratory infrastructure isn’t about more automation. It’s about enabling laboratories to respond continuously to change.

Everything should begin with the patient. Patients care less about how laboratory results are generated than about receiving reliable results that provide a sound basis for clinical decisions and allow them to benefit from advances in diagnostics and therapy. Patients are also taking a more active role in managing their own health, driving the adoption of home and alternative sampling, which means laboratories must process a much broader spectrum of samples, many of which still require manual handling today.

At the same time, the laboratory’s role within the healthcare system is evolving. Beyond delivering test results, laboratories are playing an increasingly active role as clinical partners. They support clinicians by interpreting findings, guiding diagnostic pathways and enabling more personalized diagnostics. That also changes how laboratory workflows need to be designed. Laboratories need to respond to different requesters, sample profiles, and clinical priorities while reducing complexity for clinicians who rely on laboratory information to make treatment decisions.

My advice would therefore be twofold.  First, think about automation holistically by combining AI, robotics, and new transport technologies to address this growing diversity of workflows and requirements. Second, embrace change, but don’t assume everything has to change at once. Choose a strong partner and a platform that can accommodate new requirements as they emerge. In my opinion, the best long-term investments are those that preserve the freedom to adapt.

In my view, the next decade will be shaped by two forces. The first is economic. Evolving reimbursement, funding, and regulatory requirements are increasing cost pressure across many healthcare systems, forcing laboratories to rethink how they invest while remaining competitive—particularly in the private sector, where competition is strongest.^{2, 4}

The second is personalization. After years focused on standardization and scale, the challenge now is combining that efficiency with the flexibility needed to support individualised diagnostics and evolving models of care. This is what's needed for better-informed treatment decisions, improved patient outcomes, and healthcare systems that can sustain that progress over time.

Together, these forces will transform the role of the laboratory. Rather than being valued primarily for processing samples efficiently, laboratories will be recognized for how well they support patient management and treatment decisions. As diagnostics move closer to the point of care and home, laboratories will play a growing role in expanding access to high-quality diagnostics, while helping people take a more active role in managing their own health.

To me, that’s ultimately what the future of laboratory automation should be about.