A lab manager is working through a spreadsheet with over 80 rows. Each row represents a feature, each column a vendor. Three hours into the comparison, the vendors all look much the same. He is tired and still no closer to choosing one.
When selecting software for laboratory and R&D work, a broad range of features can make the decision more difficult. One contributing factor can be feature creep: software gradually gains features beyond its original focus, increasing its complexity. This leaves the team evaluating it with more work to distinguish essential capabilities from optional additions.
Feature fatigue is a related but distinct issue: too many features can overwhelm users when they make a product harder to navigate and use. What initially looks attractive in a comparison may prove unnecessarily cumbersome in everyday work.
Why feature lists keep growing
Feature creep can arise when vendors keep expanding their software to accommodate additional customer requests or compete on the number of features they offer. Not every addition is unnecessary. Growth becomes problematic when the product loses its original focus and added features make it harder to use without providing corresponding benefits for its users.
The selection process can reinforce this tendency. When comparison tables reward additional features regardless of their relevance, a more extensive offering can quickly appear superior. Teams then increasingly compare feature sets, while paying less attention to whether the software supports their actual workflows.
Why smaller R&D teams feel the burden
Small and medium-sized R&D teams often have little use for features built around the needs of large organisations. These might include multiple levels of approval, elaborate access permissions, or integrations with a dozen other systems. Such features can be essential in a large organisation. A team of five or fifteen people may need none of them.
Features that serve a clear purpose in a large organisation may go largely unused in a smaller team. When those unused features add costs or administrative effort without supporting the team’s work, the result can be overengineering. Yet the software may still lack a capability that is critical to the team—a gap that can easily go unnoticed in an 80-item comparison
Many vendors also design their licensing models and sales processes around large projects with many users. Small teams end up with the same extensive feature set as a large corporation, whether or not it suits their needs. For a small, agile team with specific requirements, this mismatch becomes apparent as soon as an issue falls outside the vendor’s standard process.
When more features make everyday laboratory work harder
Whether additional features are useful often becomes clear only after implementation. If employees have to navigate confusing menus or understand numerous options they do not need for their tasks, feature fatigue can develop. The software becomes harder to use, even though it offers more capabilities on paper.
Unnecessary complexity can also have organisational and financial consequences: additional configuration, training and administration, as well as costs for modules the team rarely uses. The extent of this effort depends on how the software is structured, licensed and deployed.
The problem is especially noticeable when teams work with lab data. Many vendors design their databases around the needs of large organisations. The resulting data models can be too rigid or too fragmented for smaller teams.
In day-to-day lab data management, teams resort to spreadsheets and manual workarounds because the software does not fit their workflow. This shows why a long feature list says little about how well a solution fits a team’s needs. What matters is whether teams can consistently record, link, and use material data in their daily work.
When lab software demos focus on features rather than workflows
A broad range of features can also make sales demos difficult to follow. Many vendors try to present as many features as possible, focusing on everything the software can do. The customer’s specific requirements and which features actually meet them often get little attention.
A useful demo, by contrast, starts with questions about how the team currently works: How is material data recorded today? Where do delays occur between lab testing and formulation development? Which data sources need to be brought together?
A vendor who asks these questions takes the time to understand what the team needs before presenting features. A long feature list is no substitute for that understanding. It can even get in the way: the more features a demo covers, the harder it becomes to tell which ones matter to the team.
Start with clear objectives: a path to Material Intelligence
To keep software selection focused on the team’s needs, start with a simple question: What problem should the new software solve? Teams that agree on what needs to improve in their daily work before contacting vendors can judge each feature by how well it supports those goals.
Clear objectives help teams define specific use cases. Broad requirements such as “data management” or “reporting” are not enough. Each use case should describe the problem to be solved, the people involved, the data they need, and how the process works today.
For example, this may show that the team needs to connect formulations, process parameters, and test results to understand how they relate to each other and track the reasoning behind decisions.
Unlike a traditional LIMS, which primarily manages test orders and quality control workflows, or an ELN, which focuses on documenting experiments, Material Intelligence focuses on formulation development. It brings together material data from across the development process on a single platform so that teams can use it in their day-to-day R&D work.
The more clearly a company defines its requirements, the easier it becomes to compare software against them. Each use case provides a basis for evaluating vendors: Which solution best supports the team’s workflow? Can it integrate the data the team needs? And which features help achieve the stated objective?
Teams can then choose software based on how well it meets their needs, rather than how many boxes it ticks.
How clear objectives shape software selection
In practice, this approach changes how teams select software. Clear objectives and a specific use case help narrow the search from the start. Rather than assessing every product against a standard checklist, teams can ask whether the software is built to support the way they work. Some vendors offer software designed for general lab management across large organisations, with features such as multi-stage approvals, resource planning, and equipment management across multiple sites. Others offer Material Intelligence platforms focused on materials and formulation development, connecting formulations with processing conditions and test results. For a small R&D team with a clearly defined formulation use case, the software’s core focus can reveal more about its suitability than any individual feature in a comparison table.
This also changes how teams evaluate demos. Rather than counting features, they can ask: Does the software support our use case, or would we need a series of workarounds to make it fit?
Finally, clear objectives make conversations with management and vendors more focused. Instead of debating individual features, teams can discuss how the software would improve their R&D work.
Limits of the approach
Clear objectives cannot address every challenge in choosing software. Teams’ needs evolve as they grow or face new regulatory requirements. Software that fits their workflow today may not meet all their needs two years from now.
Choosing software solely for current needs can lead to costly changes later as the team’s requirements grow. Objectives should therefore account for both today’s workflow and likely changes over the next few years, such as larger data volumes or additional sites.
A use case alone cannot tell you how well software will work in practice. That also depends on how it fits into existing processes and systems, and how departments work together.
In practice, lab workflows involve exchanging data with other systems, such as ERP or quality management software. When defining a use case, teams should identify early which data needs to move between systems and what interfaces are needed. Otherwise, they may discover late in the selection process that they need additional integrations or that the software cannot support the intended data flow.
What R&D teams should keep in mind when choosing software
More features do not automatically make one product a better choice. They can make software harder to compare and implement, while leaving teams less certain about whether it meets their needs. For R&D leaders, the priority is clear: start by defining what the team needs.
Clear objectives and a specific use case help teams look beyond feature counts and assess how well the software supports their material data and workflows. This makes selection easier and reduces the risk of choosing the wrong product. Even feature-rich R&D software can fall short of what a team needs.
The lab manager at the start of this article could have begun with a shorter, more focused comparison. Defining the problem he needed to solve would have helped him identify the relevant requirements from the outset and brought him closer to a decision than adding another row.