Lab Software Adoption: How 30+ Labs Improved Efficiency with SciSure
We analyzed workflow data from 30+ labs using the SciSure platform: hazard reports dropped from 3.5 days to 5 minutes. Here's how to measure your Scientist Experience.
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TL;DR
We analyzed averaged workflow data from 30+ organizations using the SciSure Scientific Management Platform, grouped into three measurable parts of the Scientist Experience (SX). Here's what changed across these dimensions:
- Adoption.
Everyday tasks (viewing a chemical record, adding a container, updating inventory) got about 88% faster. The biggest single shift was identifying everyone working with a specific high-risk material: 14 hours down to 14 minutes.
- Engagement.
Complex reporting work fell by an average of 99%. Hazard lists across labs and groups went from 3.5 days to 5 minutes, and cross-team emails chasing information dropped from around 45 a week to near zero.
- Efficiency.
Administrative drag (correcting inventory data, rebuilding records, pulling compliance summaries) fell by an average of 88%. A safety summary for leadership went from three days to under 30 minutes.
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Labs track system uptime and usage. Neither tells you if scientists can work.
In today’s labs, digital systems shape almost every moment of scientific work. They guide how experiments are planned, how samples move, how inventories stay up-to-date, and how data gets shared. But scientists often experience these tools very differently from how they were designed.
This lived reality is the Scientist Experience (SX): the feeling of whether a system helps scientific momentum or interrupts it. Whether finding a record is effortless or frustrating. Whether documenting results feels natural or like a chore. Whether digital tools give scientists time back or quietly take it away by adding layers of administrative burden.
Is your lab software actually helping scientists?
Labs measure metrics like uptime and usage, but rarely the ease, efficiency, or usability that scientists depend on. When the Scientist Experience is neglected, friction accumulates unnoticed. Workarounds appear. Digital adoption stalls.
This piece offers a practical framework for making the Scientist Experience measurable. Using data collated from a large range of organizations using the SciSure Scientific Management Platform, it illustrates how SX can be assessed through real metrics including workflow times, usage patterns, efficiency savings, combined with the voices of the scientists themselves.
The result is a clearer understanding of how your digital systems support research, and where they need to evolve.
Measuring the Scientist Experience (SX) in labs: 4 metrics that matter
1. Adoption: Are your scientists actually using the system?
Adoption is the first indicator of a healthy Scientist Experience. Scientists only adopt tools that genuinely help them work, tools that minimize friction, reduce cognitive load, and feel intuitive in day-to-day use. A system can be fully deployed, but if scientists aren’t choosing it, SX is already under strain.
How to measure adoption:
- How often scientists initiate workflows inside the system
- Completion rates for inventories, reconciliations, and updates
- Reduction in off-system spreadsheets or manual records
- Evidence that scientists maintain their own data readily
- Real-time usage instead of delayed or end-of-week data entry
What SciSure usage data shows about real adoption
To understand digital adoption patterns, we analyzed averaged efficiency data collated from more than 30 organizations using SciSure’s Scientific Management Platform (SMP). While individual results vary, the data shows that when adoption is strong, routine workflows become dramatically faster, making the system the default place where scientists choose to work.
These improvements don't automatically prove adoption. But they provide a strong behavioral signal: scientists continue using the system because it meaningfully reduces time and effort. When workflows speed up, the system becomes the path of least resistance. Staying on top of your scientist's usage data is the starting point.
2. Engagement: How deeply are scientists interacting with the system?
Where adoption asks, “Will scientists use it?”, engagement asks “How fully do they rely on it?”. Engagement reflects repeated, multi-step, and high-effort use, meaning scientists returning to the system because they trust it will support them, not slow them down.
How to measure engagement
- Repeated interactions throughout the week
- Trust in the system to handle high-volume or high-stakes processes
- Reduction in cross-team questions or requests for information
- Regular completion of complex workflows directly in the platform
- Interest in integrations with third-party tools
What the SciSure user data shows
Averaged SMP user results show that strong engagement leads to deep behavioral shifts: scientists move high-effort, multi-stage activities into the platform because the Scientist Experience is reliable, predictable, and fast. With more accurate records, this encourages EHS and LabOps teams to collaborate with and support scientists better.
These behaviors only emerge when engagement is strong. Scientists don't bring complex, high-stakes tasks into a system unless the SX consistently supports their workflow.
3. Efficiency: How much time and effort does the system return to scientists?
Efficiency is the most immediately visible dimension of the Scientist Experience. When digital systems reduce the time and cognitive effort required to complete work, scientists gain back the focus they need for actual science. When systems are inefficient, even by a few minutes per task, friction accumulates quickly across the research day.
Because at the end of the day, efficiency is about how well a system preserves scientific flow.
How to measure efficiency
- Time to complete end-to-end workflows
- Reduction in repetitive administrative tasks
- Fewer interruptions or context shifts
- Lower cognitive load (fewer steps, decisions, or handoffs)
- The degree to which scientists can stay within one system instead of switching tools
What the SciSure user data illustrates
Efficiency data collated from SMP users reveals dramatic time savings across workflows that typically disrupt scientific momentum. These improvements show how a stronger Scientist Experience directly reduces operational drag and simplifies collaboration with their EHS and Lab Ops colleagues.
Across all of these examples, the pattern is consistent: efficiency improvements emerge when the Scientist Experience is strong enough that scientists can move through work without unnecessary interruption, searching, or administrative overhead.
A strong Scientist Experience gives scientists more time for analysis, experimentation, and critical thinking. I.e., the work that made them want to be scientists in the first place!
4. Satisfaction: Do scientists feel supported by their digital environment?
Satisfaction is the most human dimension of the Scientist Experience. It captures how scientists feel when they move through a digital system: whether it gives them confidence, helps them stay organized, or quietly adds strain to an already demanding workload. Satisfaction can’t be inferred from workflow metrics alone but rather emerges from scientists’ own voices.
Unlike adoption, engagement, or efficiency, satisfaction is best measured qualitatively. Scientists know better than anyone where friction lives, what derails their concentration, and which parts of the digital environment genuinely help them stay in flow to produce their best work.
How to measure satisfaction
- Short, recurring surveys focused on clarity, ease of use, trust, and perceived effort
- Quick “friction check-ins” during lab meetings (What slowed you down this week?)
- Open prompts that capture emotional tone as well as practical issues
- Monitoring themes in internal conversations, i.e. what scientists celebrate vs. what they avoid
Qualitative feedback often surfaces issues that quantitative data misses. Scientists might describe feeling “stuck,” “interrupted,” or “unsure,” even if the system appears to be functioning well. Conversely, they may report feeling more in control, more organized, and less stressed long before major efficiency gains show up in metrics.
This feedback gives shape to the experience behind the numbers
- Is the system intuitive or mentally taxing?
- Does it let scientists stay focused, or does it pull them into administration?
- Does it create confidence in data, or uncertainty?
- Does it reduce stress, or add to it?
Why satisfaction matters for SX
Satisfaction is the best predictor of long-term digital success. If scientists feel supported by a system, they continue using it, even as processes evolve and workloads intensify. If they feel frustrated or burdened, disengagement follows, regardless of how efficient the system might be on paper.
Strong satisfaction signals a digital environment that genuinely works for scientists, not against them, which is a critical foundation for a resilient, sustainable Scientist Experience.
At SciSure, our post-software implementation retention rate stands at a 96%. We have never had a failed implementation.
Putting the Scientist Experience at the center of digital strategy
The Scientist Experience is a measurable, operationally meaningful metric of how modern labs function. When SX is strong, scientists move through their digital environment with confidence, clarity, and momentum. When it is weak, friction accumulates quietly but decisively, slowing research long before leadership sees the impact in productivity, quality, or timelines.
Together, these signals create a complete, actionable picture of digital health.
If you want digital transformation to stick, measure the Scientist Experience with the same rigor you apply to compliance, uptime, and operational KPIs. Digital success is defined by whether scientists can do their best work inside the systems provided to them, not simply by deployment.
When the Scientist Experience becomes a core part of your decision-making, you stand to improve the daily working lives of the scientists who drive discovery forward.
Want to understand how your digital environment supports (or hinders) your scientists? Contact us to explore how SciSure can help you measure and improve the Scientist Experience.
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