The pace of scientific discovery is accelerating, and laboratories are under increasing pressure to deliver results faster, more efficiently, and with greater accuracy than ever before. In this environment, digital lab maturity is no longer a distant aspiration -it’s a strategic necessity. But what does it really mean to be a “digitally mature” lab, and why does it matter now?
Digital lab maturity is about more than just adopting new technologies; it’s about integrating automation, data analytics, and connected systems into the very fabric of laboratory operations. This transformation enables labs to streamline workflows, reduce errors, and unlock new levels of collaboration and innovation. Yet, as recent research and Cenevo’s own experience show, only a small fraction of labs has fully realized this vision, with most still navigating the early stages of their digital journey.
Of the respondents to Cenevo’s 2025 survey on the The Future of Digital Lab Operations, only 15% felt that their labs were fully digitized. These findings align with Capgemini 2023 research findings on lab transformation (“Building the next-gen pharma lab”, Capgemini, 2023), which finds that most R&D labs are still in the “partially scaled up” or “proof-of-concept” phases, with only a small fraction fully digitized. Together, these findings suggests that the hurdles to achieving digitization in the laboratory are significant, despite the benefits that might be realized. In this blog, we’ll explore the tangible benefits that laboratory digitization brings, and the common hurdles organizations face along the way. Drawing on real-world examples, we’ll also share practical strategies for overcoming these challenges and unlocking the full potential of the digital lab.
The Benefits of Laboratory Digitization
Cenevo’s own customer feedback and Capgemini’s research highlight that there are enormous tangible benefits to be achieved from greater lab digitization. At a high level, these include:
- Reduced time to market
Through faster and more accurate decision making at each stage of the R&D cycle. - Fewer human errors
Through integration of data systems and automation platforms to minimize manual data handling. - Reduction in cost
Increased automation, less wastage or rework. - Strategic advantages
Strong data foundations facilitate the adoption of the latest technologies – including AI – to accelerate R&D. - Improved collaboration
Higher data integrity enables and fosters collaboration internally and externally.
Centralized Data and Sample Management
One global R&D organization struggled with fragmented sample data, scattered across spreadsheets and siloed databases. This complexity led to inefficiencies, errors, and barriers to scaling operations.
In one example, an organization that initially found it challenging to manage 5,000 RNA clone samples was able to effortlessly scale up to tracking a library of more than 500,000 by implementing purpose-built digital platforms. This shift eliminated manual tracking and enabled the team to handle increased complexity and volume with confidence.
Compliance and Traceability
Digital systems can automatically record every action - who did what, when, and why - creating a robust audit trail.
This adds value for any lab type but is of particular significance for labs that are regulated and must meet strict legal and industry standards. But for any lab setting it means reduced risk - errors or unauthorized changes are quickly detected and traced, which can avoid lost research time and associated costs.
Organizations that have successfully digitized their validated processes are able to face internal and external audits with confidence, knowing that audit trails, data validation, and access controls are taken care of.
Real-Time Insights and Cross-Team Collaboration
Digitization enables automated reporting, real-time dashboards, and seamless data sharing between teams and systems.
The benefits of this are faster, more accurate reporting generated without manual effort. Teams can analyze trends, spot issues, and make informed data-driven choices. Informatics and scientific teams can link data from different sources, breaking down silos.
Examples of dashboards and data visualizations include performance & KPI Dashboards, study sample locations and status, or operational data metrics such as automation usage and capacity.
Process Standardization and Globalization
Digital platforms enforce standard workflows and processes across sites and teams, replacing ad hoc or site-specific methods. Such consistency reduces errors, and variability makes it easier to scale processes across the organization.
Cenevo have seen organizations realize this benefit time and time again, adopting global standards leading to improving reliability and supporting business growth.
Automation and Scalability
Laboratory automation such as liquid handling platforms and work cells replace manual, repetitive tasks, allowing labs to handle more work with less resources. This in turn leads to:
- Time savings: Automated processes are faster than manual ones.
- Cost reduction: Fewer staff are needed for routine tasks, freeing up resources for higher-value work.
- Growth: Labs can rapidly scale up operations without a proportional increase in staff or complexity.
Organizations have been able to increase their throughput while radically reducing the resources required to run key processes, allowing scientists to spend more time on valuable research and less on manual tasks. One organization demonstrated a substantial scaling of their materials management operations whereby they could support a much larger number of clients with 50% fewer operational staff.
Data Integrity
Digital systems ensure that all data is accurate, consistent, and traceable from origin to outcome. Here we are talking about enforcement of data validation and access controls, as well as tracking of unique assets using barcoding. This means that every sample, reagent, or compound can be tracked throughout its lifecycle. Automation can play a part here too, with automated processes less error-prone than manual ones.
Ultimately, reliable data underpins scientific integrity and reproducibility.
Cenevo has seen countless examples of organizations that have dramatically reduced errors and eliminated occurrences of lost samples by embracing digitization.
The Foundation for AI/ML
Finally, perhaps the most transformative benefit of digital lab maturity is the ability to harness artificial intelligence. Only with robust digital systems in place can labs unlock AI-driven insights, automate complex analyses, and accelerate discovery in ways that were previously unimaginable.
Why Are So Few Organizations Digitally Mature? The Hurdles
Despite the clear benefits, most organizations find that the journey to digital lab maturity is not straightforward. Organizations face a common set of hurdles—legacy systems, data fragmentation, cultural resistance, and compliance considerations. These barriers aren’t insurmountable but require a considered approach.
Here are some examples of the challenges and how labs have tackled them:
Taming Legacy Systems and Data Chaos
Many labs face years of manual records, fragmented IT systems, and inconsistent data formats. Migrating and integrating these legacy systems is often the biggest hurdle. The key is to break the process into manageable phases - such as migrating data in stages or tackling one system at a time - to achieve standardization without major disruption.
Building a Future-Proof IT Foundation
Modern digital labs need robust, scalable infrastructure. But that doesn’t mean you have to do everything at once. Capgemini’s research suggests success comes from starting with pilot projects to test new technologies, then scaling up what works. Cloud-based and modular solutions are helpful here - letting you grow without massive upfront investment. Increasingly, organizations are partnering with specialist providers for core services rather than relying on their in-house IT teams as a way of accelerating progress. For example, by adopting cloud-hosted solutions, organizations can significantly reduce their total cost of ownership while benefiting from faster, more reliable support and access to the latest technology. Cloud platforms also enable access to advanced features such as AI-driven analytics and seamless integration with other SaaS products, and benefit from continuous updates and robust security-freeing teams to focus on strategic goals rather than day-to-day infrastructure management.
Regulatory and security concerns
In regulated environments, compliance and security are non-negotiable. Established systems may already be in place that have been approved by regulatory bodies, and making changes to these and re-validating new systems can be daunting, requiring close collaboration with IT and compliance teams. And yet improved compliance is one of the biggest benefits of digital lab maturity, so holding on to this vision is the key to overcoming the hurdles.
Winning Hearts and Minds
Technology is only half of the equation - people are the other. Changes to processes and adoption of automation may understandably provoke resistance to change. We have seen that success in driving successful change in the lab hinges on a few key principles:
- Involve stakeholders early, communicating the vision so everyone understands why change matters
- Choose solutions that are intuitive and flexible, pilot them
- Empower advocates within the organization to champion adoption
- Introduce changes in phases, and celebrate early wins to build trust
- Make provision for training and support so users feel confident adopting new digital processes
The Path to Digital Lab Maturity
Achieving digital lab maturity is an ongoing journey, not a one-off project. While challenges like legacy systems, cultural resistance, and compliance concerns are real, the benefits - greater efficiency, improved compliance, scalability, and readiness for future innovation - are substantial. By taking a phased approach, standardizing processes, engaging stakeholders, and seeking specialist support, organizations can successfully navigate the path to digital transformation. Furthermore, as AI continues to evolve, labs that have invested in digital foundations will be best positioned to lead in innovation, efficiency, and scientific impact.
Let’s explore where your lab sits on the digital maturity curve - and how to move forward.
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