Authors: – Shawani Shome (Scientific Systems Analyst, Scientific Informatics )
In brief: lab digitalization fails after go-live not because the technology is flawed but because implementation is treated as an IT project rather than an organizational transformation. The four elements that determine whether ELN, LIMS, and scientific data management platforms achieve lasting adoption are: process redesign before digitizing workflows, stakeholder co-design that involves scientists in shaping the system, sustained adoption support beyond one-time training, and contextual implementation tailored to each lab’s actual reality.
The lab informatics landscape has evolved dramatically over the past decade. Electronic Lab Notebooks (ELNs), Laboratory Information Management Systems (LIMS), AI-powered analytics, and integrated scientific data platforms have matured into powerful technologies capable of transforming research and development. Yet despite these advances, many organizations fail to realize the expected return on their digital investments—not because the software falls short, but because successful adoption requires changes in processes, behaviors, and organizational culture.
The technology will rarely the bottleneck, the organizational readiness to use it well certainly is.
The ‘shelf-ware’ pattern described here — where ELN and LIMS platforms are deployed but not adopted — is explored in operational detail in Excelra’s blog on Maximise Your ELN/LIMS Investment with Managed Services for Life Sciences, which examines how managed services and ongoing support programmes can recover the value of lab informatics investments that have stalled after go-live.
Where the real gap lies
While digitalization programs are scoped as IT ventures, they are actually organizational transformation projects. The problem doesn’t exist in the gap between what the platform can do and what is required by the lab but in the gap between what is set up to do and what scientists actually adopt into their daily practice.
Addressing this challenge will take more than just reaching the go-live date. It needs:
- Process redesign – redesigning the workflows before digitizing them, not after
- Stakeholders co-design – involving scientists in shaping the system instead of just training them on it
- Sustained adoption support – ongoing support of adoption instead of one-time training
- Contextual Implementation – Solutions tailored to the actual reality of that particular lab
Herein lies the point at which many pure-play technology vendors reach the edge of their offering. Building good software and driving organizational change are two different skill sets, and both are becoming more widely accepted as necessary elements in this industry.
The four requirements listed above — process redesign, stakeholder co-design, sustained adoption support, and contextual implementation — are the same requirements described in Excelra’s companion blog on The Usability Gap in Lab Informatics: Putting Scientists at the Centre of Digital Transformation. Together, these two blogs form a complete picture of the lab digitalization adoption challenge: Blog 10 identifies the usability gap as the structural problem; this blog identifies what a capable implementation partner must do to close it.
For organizations assessing where their current lab informatics ecosystem stands before committing to an implementation programme, Excelra’s blog on Data Landscape Assessment: Building the Foundation for Digital Transformation in Pharma outlines a structured approach to understanding the current state of your data, systems, and workflows — the prerequisite for any implementation that aspires to be contextual rather than generic.
Bridging the gaps: Where partners such as Excelra add value
Our activities at Excelra sits seamlessly at this intersection. As a Scientific informatics and Data Solutions partner working across biotech, pharma and CRO environments, we have seen firsthand that those laboratories which could successfully make the move toward digitalization are those who approach it as a scientific and organizational challenge, rather than technical one.
Excelra does not seek to replace the platforms that scientists already use but to improve on them within the specific context of each organization. Our role involves understanding how researchers think and operate by embedding ourselves in the scientific workflow early, recognizing friction points, and developing implementation approaches based on this insight.
In the case of a mid-sized biotechnology firm transitioning from an ad-hoc lab notebook to a structured environment, the first question is not ‘what system?’ but ‘how do we define success for our researchers running these experiments after six months?’ That orientation — prioritizing science over technology — shapes everything from workflow design to training to post-deployment support.
Excelra’s track record in lab informatics implementation spans ELN, LIMS, and SDMS platforms across pharma, biotech, and CRO environments. Our blog on Lab Informatics Implementation and Delivery describes the implementation methodology behind the approach outlined here — including how Excelra structures discovery, workflow design, phased deployment, and post-go-live support to ensure adoption rather than shelf-ware. For organizations at the very beginning of this journey, see also Excelra’s An Introduction to Business Consulting and Scientific Advice in Lab Informatics — which frames the strategic choices labs face before selecting a platform or implementation partner.
Fig. 1. Excelra value in digitalization.
The Future of digitalization is Human-Centred
The most exciting period of digitalization of labs is yet to come. AI-augmented experiment design, seamless real-time harmonization of data from various locations, and fully traceable digital research facilities are possible for those companies that have set up their foundation for such progress. But those foundations are not purely technical. They are cultural, procedural and human.
The key vendors and partners shaping the landscape of informatics for R&D of the next ten years will be the ones who understand that their job does not end at deployment — it begins there. Assisting researchers in trusting new platforms, assisting organizations in developing their digital cultures, and guiding leadership connect technology investment to scientific progress are some of the skills turning digitalization from a project into an organizational capability.
The lab that will lead tomorrow are not necessarily the ones with the most modern tools, they will be the ones where science and technology co-evolved.
That is the challenge worth solving. And it is one that, at Excelra, we are built to take on alongside the organizations we serve.
Lab digitalization, as described in this article, succeeds or fails not at the level of technology selection but at the level of organizational transformation: specifically, whether the implementation includes process redesign before digitizing workflows, stakeholder co-design with scientists, sustained post-go-live adoption support, and contextual configuration for each laboratory’s actual operating reality. As this article concludes: the labs that will lead tomorrow are not necessarily the ones with the most modern tools — they will be the ones where science and technology co-evolved.
Excelra’s Scientific Informatics and Lab Informatics capabilities span the full lab digitalization lifecycle — from initial data landscape assessment and system selection through workflow design, phased implementation, user training, and managed post-deployment support. To explore how Excelra’s approach can turn your go-live into lasting scientific progress, visit our Lab Informatics service.
Why do ELN and LIMS implementations fail after go-live?
ELN and LIMS implementations fail after go-live for predictable organizational — not technical — reasons. The most common failure pattern is the shadow spreadsheet relapse: scientists who find the new platform slower, less intuitive, or more rigid than their current workflow quietly revert to maintaining personal spreadsheets alongside the official system, defeating the purpose of digitalization. This happens because implementation programs focus on deployment milestones rather than adoption outcomes. Training is provided once at go-live and then discontinued. Workflows are digitized without being redesigned — the inefficiencies of the old process are simply reproduced in digital form. Scientists are trained on what the system can do rather than involved in shaping what it should do for them. And solutions are configured to generic vendor defaults rather than the specific experimental workflows of the lab. Addressing these causes requires treating lab digitalization as an organizational transformation, not an IT project.
What does 'beyond go-live' mean in the context of lab informatics?
‘Beyond go-live’ in lab informatics refers to the sustained support, adoption management, and continuous improvement work that determines whether a laboratory information system delivers its intended scientific value — as opposed to ending at the deployment milestone. Go-live is the point at which the system is technically operational and handed over to users. What happens in the weeks, months, and years after go-live determines whether it becomes part of how scientists actually work or becomes expensive shelf-ware. Beyond-go-live activities include ongoing user support and troubleshooting, continuous feedback loops that surface workflow friction points, iterative workflow refinements as scientists identify gaps between the configured system and actual experimental needs, digital culture development to normalise data entry and platform usage, and evolving the platform as scientific workflows, regulatory requirements, and organizational priorities change. The ratio of investment in go-live versus beyond-go-live is one of the strongest predictors of long-term digitalization success.
What is stakeholder co-design in lab informatics implementation?
Stakeholder co-design in lab informatics implementation is the practice of involving scientists, lab managers, and other end-users in the design of the workflows, templates, and system configuration — rather than presenting them with a completed implementation for training and adoption. The distinction matters because the people who will use the system daily have operational knowledge that implementation teams and vendors do not have access to unless they deliberately seek it: which steps in a workflow are truly sequential and which are flexible, what data fields are genuinely important versus what will be left blank because they do not reflect how experiments actually run, and where the current paper or spreadsheet process has features that the digital replacement needs to preserve. Co-design converts scientists from passive recipients of a technology project into active architects of a tool that reflects their reality — a transformation in relationship that significantly improves adoption because the system is genuinely designed for how they work, not how a vendor thinks they work.
What is process redesign in lab digitalization and why must it happen before digitizing?
Process redesign in lab digitalization is the activity of evaluating and improving scientific workflows before encoding them into a digital platform — as opposed to the common alternative of simply replicating existing processes in digital form. Process redesign matters because digitalization amplifies the efficiency or inefficiency of whatever process it encodes: a poorly designed workflow becomes a poorly designed digital workflow that scientists must navigate at every experiment. Before a workflow is digitized, the implementation team and scientists should ask whether each step is necessary, whether the sequence reflects actual scientific logic or historical accident, whether data capture points are positioned where scientists naturally record observations, and whether approval and review steps are proportionate to the risk at each stage. The investment in process redesign before go-live is consistently associated with higher post-go-live adoption, because scientists are adopting a system that reflects improved practice rather than a digital replica of whatever they were doing before.
How should organizations evaluate a lab informatics implementation partner?
Organizations evaluating a lab informatics implementation partner should assess five dimensions beyond software expertise. Scientific domain knowledge: does the partner understand the experimental workflows of your specific research area — analytical chemistry, discovery biology, quality control — or do they apply a generic implementation template regardless of scientific context? Organizational change capability: does the partner have explicit processes for stakeholder co-design, sustained adoption support, and digital culture development, or do they define their engagement as complete at go-live? Reference track record: can the partner demonstrate outcomes from comparable implementations — specifically adoption rates and workflow utilization metrics, not just deployment timelines? Configuration depth: does the partner configure the platform to match your workflows, or do they train your scientists to adapt to the platform’s defaults? Post-deployment support: what ongoing engagement does the partner offer after go-live, and how is that engagement structured to evolve as your scientific needs change? These questions distinguish partners who can drive real scientific progress from vendors who can execute a technical deployment.
What are the foundations required for AI-augmented lab digitalization?
The foundations required for AI-augmented lab digitalization — AI-assisted experiment design, real-time data harmonization across locations, and fully traceable digital research workflows — are primarily organizational and data quality foundations, not technical ones. The technical platforms for AI augmentation in labs are available today. What limits their deployment is the absence of the structured, complete, consistently captured data that AI models require as inputs. A lab that has deployed an ELN but where scientists maintain shadow spreadsheets does not have the data foundation for AI augmentation — it has fragmented, inconsistently captured data in two incompatible systems. Building the AI foundation requires first achieving consistent ELN and LIMS adoption across all experiments, with complete and structured data capture at every step. This is exactly why the human and organizational work of the beyond-go-live phase — sustained adoption support, digital culture development, continuous workflow refinement — is a prerequisite for AI capability, not a nice-to-have addition to it.
Ready to Turn Your Go-Live into Lasting Lab Digitalization?
Excelra's Scientific Informatics team combines domain expertise across ELN, LIMS, and scientific data management with organizational change management and sustained adoption support — ensuring that lab digitalization investments deliver real scientific progress, not just successful deployments. If your platform has become shelf-ware or your next implementation needs to be built on a stronger foundation, our team is ready to help.
