Despite significant investments in digital technologies, laboratory data often remains fragmented. TetraScience estimates there are more than 10 million data silos across the industry, while Siemens reports that on average, one in a thousand drug candidates make it to market with 57% of scientists citing data silos as the biggest barrier to effective lab data use. These findings highlight a challenge faced across the industry: laboratory data, technologies, and workflows often remain fragmented despite significant digital investment.

Fragmentation appears in many forms, from inefficient workflows and duplicated effort to delayed technology adoption and integration overhead. While each issue may seem manageable on its own, together they create a significant burden on laboratory operations.

The industry has a name for many of these individual problems. What it lacks is terminology for the cumulative effect. Throughout this paper, we refer to this effect as the fragmentation tax. 

Unlike a capital investment that appears as a single budget line, the cost of fragmentation is rarely visible in one place. Instead, it shows up as lost productivity, additional effort, ongoing integration maintenance, slower technology adoption, and delayed business outcomes. Because these costs are distributed across workflows and departments, organizations often underestimate their true scale.The effects of fragmentation can be seen in four key areas:

• Operating Cost: The most visible cost is the effort required to move and reconcile data between systems. Scientists and lab technicians spend valuable time moving, verifying, and preparing information for reporting, when that time could be spent on generating insights.

• Compliance Cost: While integrations help systems communicate, they require regular testing, validation, and documentation. Over time, maintaining these connections can become a significant burden for laboratories.

• Opportunity Cost: Fragmented ecosystems make it harder to introduce new instruments, software, and automation. Laboratories spend significant time integrating new technologies before they realize their full value.

• Organizational Cost: When systems and workflows are not well connected, progress slows down. Teams become more cautious, investment becomes harder to justify, and future initiatives face greater resistance.

The laboratory industry is at an inflection point. While advances in instrumentation, automation, software, artificial intelligence, and data analytics continue to accelerate, the true challenge is no longer the availability of technology. It is the ability to connect these capabilities into a cohesive, interoperable ecosystem.

Throughout this paper, we have argued that a one lab ecosystem is not defined by any single instrument, software platform, or automation solution. Rather, it is created through the effective integration of people, processes, data, and technology across the laboratory landscape.  While people, processes, and technology have long formed the foundation of laboratory excellence, data has emerged as an equally critical pillar. Data not only connects laboratory systems and workflows but also enables traceability, decision-making, automation, compliance, and the application of AI-driven insights.

Achieving this vision requires more than system deployment. It requires a deliberate transformation strategy focused on interoperability, workflow integration, data accessibility, and organizational alignment.

As laboratories pursue automation, AI, and digital transformation initiatives, disconnected systems increasingly limit their ability to scale these investments. Building a connected ecosystem has therefore become a strategic priority for organizations seeking greater efficiency, data integrity, agility, and scientific productivity.

Key Takeaways

• A One Lab Ecosystem begins with workflows, not technology.
• Connectivity creates value only when people, processes, data, and technology evolve together.
• Open standards and interoperability are becoming critical enablers of future-ready laboratories.
• The transition to connected laboratories will be gradual, requiring legacy and modern systems to coexist.
• Success depends on reducing fragmentation and enabling seamless data flow across the laboratory ecosystem.