Lab informatics is at an inflection point. According to a recent industry survey, 77% of life sciences labs expect to be using AI within the next two years, signaling a rapid shift in how scientific work is executed and supported. At the same time, rising data complexity and the burden of managing multiple vendors are forcing teams to rethink not just how many tools they use, but how well those tools actually support modern lab workflows. LIMS, in particular, are being pushed beyond what many legacy systems were designed to handle.
While LIMS needs vary across diagnostics, gene therapy, manufacturing, development, and more, one theme cuts across all of them: expectations for how informatics software should look, feel, and function are changing fast. The trends below highlight what’s driving this shift, and how teams can evaluate LIMS going forward.
1. The next generation of users is setting new expectations for lab informatics design and function.
As a new generation enters the workforce, expectations have shifted dramatically. Younger scientists have grown up with user-friendly apps such as Amazon, Uber, and Netflix. They expect software that looks and feels like the other apps and tools they use daily. But many legacy systems haven’t evolved in decades. For the newer generations, an outdated UI signals outdated technology; and some scientists even choose companies based on the strength of their tech stack. Adopting modern LIMS is quickly becoming a staff retention issue as much as a scientific or software one.
“The speed to get products to market is not five or ten years anymore. Teams need systems that can do a lot more, and are now looking for systems that can keep up with the science.” — Paul Sanders, VP of Sales and Marketing, Semaphore Solutions
But ease of use alone isn’t sufficient. Some newer tools prioritize simple interfaces at the expense of workflow and scientific functionality. This limits their utility in complex, interdisciplinary environments, where product depth is crucial.
What to look out for: A modern, intuitive interface is table stakes…but it must be paired with robust workflows, strong data models, and scientific depth.
2. Software bloat is driving a shift from point solutions to platforms
“Platformization is pushing labs toward fewer software tools. Teams want to invest in a smaller number of platforms they can build apps on, as opposed to bringing in bespoke apps.” — Brian Jack, COO, Semaphore Solutions
Scientific software has historically been built for highly specific teams and processes. As a result, today’s lab environments are filled with point solutions—ELNs, LIMS, QMS, CTMS, and more—often poorly connected.
Many organizations are now feeling the cost of this fragmentation. Vendor and software fatigue is widespread, particularly among larger enterprises that operate multiple LIMS across teams, geographies, or stages of development. Companies want fewer systems and more visibility into data and processes, not a growing list of hyper-specialized features.
In response, vendors are attempting to expand beyond narrow workflows, positioning themselves as platforms. Vendors are now competing to own and harmonize data sources, promising to provide teams with a complete view of lab operations.
But not all teams are interested in re-platforming. For highly complex and interdisciplinary functions like molecular diagnostics, specialized tooling still matters. Instead of chasing a monolithic solution, these organizations are standardizing on a small but capable set of tools…while still striving to avoid adding software that doesn’t serve a clear business need.
What to look out for: Prioritize platforms that reduce tool sprawl and improve operational efficiency, while still delivering sufficient depth for your core workflows. Avoid both narrowly scoped point solutions that increase fragmentation and overly broad platforms that dilute essential LIMS functionality.
3. LIMS are evolving from rigid, protocol-driven systems to flexible, workflow-based solutions
Despite advances in LIMS, many teams still choose spreadsheets because of their flexibility. Even with clear limitations around governance and scalability, they can adapt to how labs actually work. Historically, teams were forced to choose between configurable but unregulated spreadsheets and rigid but traceable LIMS. This tradeoff is now starting to disappear.
“Accelerating scientific innovation is leading to increasingly complex laboratory processes. The traditional notion that LIMS are only used for data capture no longer applies. The next generation of LIMS needs to efficiently manage the execution of complex workflows in the creation of laboratory data.” — Peter Smith, CEO, Semaphore Solutions
The LIMS space is evolving toward flexible, workflow-centric systems that map to real lab processes while still enforcing data quality, compliance, and governance. Rather than assuming linear, protocol-driven workflows, modern LIMS are designed to support complex, multi-step processes and frequent changes: reflecting the variability and unpredictability of real-world development.
This shift is critical as scientific workflows become more complex and higher stakes. Older, linear systems worked in lower-complexity environments. Today’s labs operate expensive instruments, diverse sample types, and tightly coupled downstream analyses. As pressure to deliver results faster increases, flexibility is not optional.
What to look out for: Look for LIMS that prioritize workflows over rigid protocols, support easy data querying without complex SQL, and can quickly adapt as science evolves.
4. AI is delivering real value: but separating substance from hype is now the challenge
AI is no longer a far-away concept for LIMS; it’s now commonplace both within LIMS and in LIMS development. At the same time, hype remains widespread. Buyers are increasingly skeptical of broad, undifferentiated claims like “AI-powered,” “intelligent LIMS,” or “next-generation,” which now appear verbatim across much of the market. The challenge for teams is learning how to evaluate useful, practical AI applications versus empty marketing messages.
AI delivers tangible value when it’s embedded into a team’s existing workflows and day-to-day tasks, taking on the repetitive and burdensome work so humans in the lab can focus on more important activities. For example, AI is powerful for reducing manual effort, speeding up review cycles and QA, or accelerating system configuration and development. The strongest use cases are narrow, practical, and clearly tied to outcomes.
One example of practical AI in LIMS is a QA/QC “copilot” that flags anomalies and prioritizes items for human review. These tools augment existing processes rather than attempting to replace them, saving significant time without removing oversight.
Importantly, successfully leveraging AI also depends on the quality of the underlying data. Without strong data foundations, even promising use cases will fail. Following FAIR data principles (findable, accessible, interoperable, reusable) remains essential to ensuring AI delivers accurate, dependable outputs.
“Upstream of using AI, the first step for companies is getting their data correct. Teams need a bedrock of data that’s managed the right way, with systems that can cross multiple domains. That interoperability—moving and consolidating data across systems—is what makes downstream AI actually viable.” — Steven Bates, LIMS Consultant, 20/15 Visioneers
What to look out for: Look beyond marketing claims. Prioritize vendors with real AI features in production and proof of measurable improvements in speed, efficiency, or quality.
5. Culture is still the biggest blocker of LIMS adoption—but compliance is making modernization inevitable
“Despite advances in LIMS, many teams still rely on Excel because the friction hasn’t reached a tipping point. There isn’t enough glue in the process to meaningfully slow teams down. It's not hurting the bottom line enough for them to make the change or the investment.” — Brett Saremba, Field Application Scientist, Semaphore Solutions
Despite major progress in LIMS capabilities, many organizations continue to rely on outdated systems…and some even still use paper. Distributed teams and outsourced workflows often slow adoption and make it difficult to rip and replace legacy systems.
In many cases, modernization is driven by individual leaders with the expertise (or appetite) to push change forward. Cultural readiness is just as important as technical capability, and new functionality alone is rarely enough to drive real behavior change.
But external pressure to modernize is mounting. Regulatory bodies like the FDA and EMA are increasingly emphasizing electronic data practices, auditability, validated workflows, and standardized data capture. As guidance around AI and digital systems continues to evolve, modernization will become less optional and more of a baseline requirement.
What to look out for: Choose LIMS vendors that embed compliance directly into lab workflows and can evolve alongside changing regulations. This includes validated workflows, audit-ready traceability, controlled change management, and clear AI governance.
If you want to see what this looks like in practice, join our upcoming webinar “Electronic Records and Signatures: Rethinking 21 CFR Part 11 in a Graph-Based World”. We’ll explore how modern, graph-based architecture delivers immutable records that preserve context and traceability.
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