The Organizational Reality Behind Implementation Failures
We hear the stories all the time: lengthy LIMS implementation projects, significant investments, and years of effort, only to end up with a system that doesn’t deliver the functionality, adoption, or operational impact the lab expected.
After over a decade of implementing LIMS across complex laboratory environments, the Labbit team knows that most implementation challenges don’t begin during configuration, testing, or go-live. They start much earlier, with unclear processes, unrealistic timelines, limited user involvement, and selecting a system that doesn’t align with where the lab is today or where it needs to go next.
A successful LIMS implementation is about more than selecting the right technology—though that decision is certainly important. It also requires a clear understanding of the lab’s workflows and how they connect across the broader organization, strong collaboration between technical and operational teams, thoughtful change management, and a strategy that supports both current needs and future growth.
To get an outside perspective, we spoke with Labbit client and seasoned LIMS implementation expert Ann Chatelle, Implementation Specialist III at Ancera. With experience on both the vendor and customer side of LIMS projects spanning clinical diagnostics, genomics, research, and regulated laboratory environments, Ann has seen firsthand what separates successful implementations from those that struggle.
Drawing from her experience across real-world implementations, Ann shares the lessons learned, challenges encountered, and best practices that can make or break a LIMS project. In this three-part series, we’ll explore the factors that influence implementation success across the full lifecycle, from organizational readiness to vendor selection to post-go-live execution.
In Part 1, we focus on the common pitfalls labs encounter before go-live, including overlooked user adoption, underestimated complexity, and systems designed too rigidly to support future change.
Q: Give us the quick version of your LIMS career. How did you end up in this world, and what kinds of labs and systems have you worked with over the years?
Ann: I actually started my career as a Medical Technologist at a local hospital. Early on, I developed a reputation for picking up new technology quickly, so I was often sent to vendor trainings to support different instrumentation platforms across the lab. That included systems like Sysmex hematology analyzers and Roche chemistry platforms, which gave me exposure to a wide range of workflows and made me a subject matter expert in multiple areas of the lab.
From there, I moved into supporting a Pfizer Clinical Research Unit before transitioning into a Business Analyst role connected to a Mount Sinai research initiative in Connecticut. The project focused on understanding why some individuals carrying certain genetic markers develop disease while others do not, which was my introduction into translational genomics, clinical NGS workflows, and lab informatics.
At Sema4 (now GeneDx), I helped design, configure, and validate clinical NGS workflows and LIMS processes. After the acquisition, I joined Thermo Fisher as a Senior Technological Support Specialist and Trainer, where I traveled globally training customers and internal teams on configuring and managing SaaS/PaaS LIMS platforms.
After COVID, I moved into a more hands-on operational role at Ancera, where I became deeply involved in implementation strategy, workflow design, integrations, validation, and bridging the gap between technical and operational lab work.
Q: You’ve been on both sides of this relationship, the vendor side training and supporting labs, and the customer side selecting and running an implementation yourself. How does that dual experience shape how you see LIMS implementations now?
Ann: Having experience on both the vendor side and the customer side completely changed how I view implementations. I understand where time, money, and effort are actually best spent because I’ve lived both perspectives.
Coming from the bench originally, I also understand how scientists and technicians physically move through the lab and interact with systems. That’s why I strongly believe a LIMS should be as streamlined as possible, with the fewest clicks necessary to complete a task. A workflow may look great in a demo, but if it slows the bench down, adoption becomes a problem quickly.
I’ve also learned that rushing design or validation usually creates bigger downstream problems. A clean build upfront almost always saves time and frustration later.
Probably the biggest lesson is that lab buy-in is critical. The best implementations involve technicians early, during design and testing, so they shape the system rather than react to it. Without that, even good systems get rejected in practice.
Being on the customer side also made me much more critical during vendor evaluations. I now ask detailed questions about real capabilities versus roadmap promises, because demos rarely reflect the complexity of real lab workflows.
At the end of the day, most implementation failures are less about technology and more about change management, communication, and early user involvement.
At the end of the day, most implementation failures are less about technology and more about change management, communication, and early user involvement.
Q: What does a genuinely successful LIMS implementation look like? How do you know when one has actually gone well?
Ann: A successful LIMS implementation starts with user adoption, and that needs to happen early. The best projects involve bench scientists and technicians from the beginning so workflows are shaped with real operational input.
Most techs are not against new systems, they just want something that helps them do their jobs efficiently while capturing all required data. A good implementation supports real-world lab operations, including manual steps, imports, and QC review processes.
Go-live is not the finish line. The hyper-care period is where you really see system usability, including issues like click-heavy workflows, bottlenecks, and edge cases that only appear in production.
Strong communication between the lab and support team also matters long term. When users understand how builds and deployments work, collaboration improves significantly.
At the end of the day, I think an implementation is successful when the lab stops calling it “the new system” and it just becomes part of how they work. The users trust it, the workflows feel natural, and the workarounds and spreadsheets start disappearing.
A successful LIMS implementation starts with user adoption, and that needs to happen early. The best projects involve [lab users] from the beginning so workflows are shaped with real operational input.
Q: Walk us through an implementation that went badly. What were the early warning signs?
Ann: One implementation that really stands out to me was a molecular lab project at a large academic medical center. Early on, I could tell the project was heading toward trouble.
The lab was still operating on a very old system that handled patient data and billing, while a huge amount of operational and QC work was being managed through spreadsheets and offline processes. Leadership decided to roll out a LIMS broadly across the department after success in another lab.
The problem was scale. They underestimated how many workflows needed to be supported, how much clinical data needed to be migrated, and how difficult it would be to replace deeply embedded manual processes all at once.
But the biggest warning sign was lack of user buy-in. The bench teams were not heavily involved in shaping workflows, so the system felt imposed rather than built with them. In hindsight, it should have started with a single lab as a proof of concept and expanded gradually.
Instead, it became a large-scale, department-wide rollout. After several years and significant investment, the project was ultimately scrapped.
It reinforced a lesson I still believe strongly today: even good technology can fail if rollout strategy, user involvement, and organizational expectations are not aligned with operational reality.
Q: What’s the single most common failure point you’ve seen across implementations? Does it still surprise you?
Ann: One of the most common failure points is building systems too rigidly for the lab as it exists today instead of designing for where it will go in the future.
In regulated environments, teams often focus on locking processes down. But labs evolve constantly—new assays, instruments, reporting requirements, and integrations.
I also see a disconnect between IT teams and the actual complexity of LIMS workflows. A LIMS is often treated like a database, when in reality it involves chain of custody, QC review, validations, workflow branching, and daily operational exceptions.
The systems that struggle long term are usually the ones designed too narrowly, without enough flexibility to evolve. And it still surprises me how often scalability gets treated as a “later problem,” even though it always becomes the main one.
A LIMS is often treated like a database, when in reality it involves chain of custody, QC review, validations, workflow branching, and daily operational exceptions.
Q: Which mistakes show up over and over again?
Ann: There are a few mistakes I see repeatedly, and most of them are avoidable.
The biggest is not involving bench staff early enough. The people using the system every day should help shape workflows and be heavily involved in testing because they know where real operational friction exists.
Labs also underestimate validation and testing. Whenever possible, lab staff should be involved because they catch edge cases that developers and managers often miss.
Another common mistake is recreating legacy workflows exactly, including inefficient ones, instead of using implementation as an opportunity to improve processes.
Over-customization is another trap. The more customized a system becomes, the harder it is to maintain, validate, scale, and upgrade later.
And finally, training is consistently underestimated. Without good documentation and ongoing education, users fall back into workarounds, even when better processes exist.
Q: How much of implementation failure is technical vs. organizational?
Ann: Honestly, most implementation failures are organizational and human rather than technical. The technology itself is usually capable, the challenge is alignment and communication.
A good implementation specialist needs to listen first and understand real workflows instead of assuming based on similar labs. When subject matter experts are in the room, you have to let them explain how the lab truly operates.
A LIMS implementation also exposes existing process problems. Success depends on whether the organization uses that as an opportunity to improve or resist change.
Many customers also underestimate how much involvement is required from their side. This isn’t software you just install, it’s a highly configurable system that depends on customer input to function properly.
That’s why user buy-in and communication matter so much. Without them, even technically strong implementations can struggle.
A good implementation specialist needs to listen first and understand real workflows instead of assuming based on similar labs. When subject matter experts are in the room, you have to let them explain how the lab truly operates.
Conclusion
Across Ann’s experience, the same patterns show up early and often: limited bench involvement, rushed timelines, overly rigid system design, and an assumption that implementation is primarily a technical exercise rather than an operational change. When those elements are out of alignment, even strong platforms can struggle in real-world use.
What stands out most is that the technical build is rarely the hardest part. The real challenge is aligning people on how the lab operates today, how it needs to operate in the future, and how much change the organization is realistically prepared to absorb along the way. Without that clarity, implementation decisions get made in isolation, and that’s where problems compound.
In Part 2, we shift from internal dynamics to external ones: how labs evaluate vendors, what often gets missed in demos, and why choosing the wrong partner early can quietly shape the outcome long before go-live ever happens.
