The turn of the year is the moment when many R&D teams and heads of development set budgets and redefine priorities. And with that comes the question of which laboratory software will genuinely move things forward in 2026. Which solution will help maintain competitiveness over the coming years? And which system deserves priority and budget in the coming cycle?
Day-to-day pressures are increasing. Many routine tasks add little value, regulatory demands are rising, and with generational change, valuable tacit knowledge is disappearing. At the same time, expectations for speed, data quality and traceability continue to grow. New software categories, such as Material Intelligence platforms, are emerging that go far beyond classic LIMS, ELN or Excel setups—raising digitalisation in the lab to an entirely new level.
This article provides guidance: Which systems exist today? Where are their strengths and limitations? And how can organisations make a software decision in 2026 that remains viable in the long term?
The Current System Landscape
The laboratory software market has changed significantly in recent years—clear evidence of advancing digitalisation in R&D. While LIMS, ELN and Excel remain the best-known tools, several additional categories have emerged that enable new ways of working. In total, six groups can be distinguished: traditional LIMS, electronic lab notebooks, Excel-based approaches, modern cloud-based LIMS, specialised R&D platforms, and Material Intelligence solutions.
In practice, this leads to highly heterogeneous environments. Many teams use a mix of Excel, ELN modules and LIMS structures, simply because these tools are well established in different parts of the organisation. Research teams increasingly rely on platform approaches, as development work requires iterative processes and flexible comparison options. Even in less regulated labs, fragmented tool landscapes are common—with Excel or point solutions running alongside structured systems.
Strengths and Weaknesses of the Classical Systems
LIMS: Stable, but not flexible
A Laboratory Information Management System (LIMS) performs well when laboratory processes are clearly defined. Sample management, traceability, audit trails and certificates can be reliably modelled. This is why LIMS solutions have been standard in regulated environments for years.
However, implementation is often resource-intensive: customisation requires programming, and overall project scope quickly results in six-figure budgets. For research and development, a LIMS is only partly suitable. Its core logic is designed for repeatable, routine workflows. This fits quality control but not the iterative processes typical in R&D.
ELN: Useful for notes, but not a data hub
An ELN (Electronic Lab Notebook) is the digital counterpart of the traditional lab journal. It helps document experiments, observations and notes in a structured manner. In many labs, it complements a LIMS and is often integrated into it. But functionality remains limited: beyond documentation, basic search and traceability, an ELN typically offers little more. It is not designed for complex data flows or cross-process work as required in R&D.
Excel: A classic with familiar risks
Excel remains ubiquitous due to its immediate availability and flexibility. But it is not a database and is inadequate for large datasets. Many IT departments disable macros for security reasons, and collaborative work quickly leads to inconsistencies. Excel is a versatile tool—but not laboratory software. It cannot reliably support structured development processes.
Modern Alternatives: Where Laboratory Software Is Heading
Unified / Cloud LIMS: Evolution for Quality Assurance
“Unified Cloud LIMS” or “LIMS 2.0” represent more modern variants of traditional LIMS. Their core logic remains the same, but they add cloud storage, improved interfaces and additional functional modules. This enables more structured data capture and location-independent access.
However, the challenges remain similar: these systems still follow rigid routines and are best suited for quality-oriented and regulatory workflows. Costs range widely—from low-cost entry models requiring extensive self-configuration to expensive U.S. solutions. Implementation times of over a year are common.
R&D Platforms: Modern, but with a narrow focus
R&D platforms map the development process within a single environment. They combine ELN functionality with formulation management and project organisation and add early-stage AI features such as formulation suggestions or faster data search.
However, most systems focus solely on formulation. Test data, procedures and downstream process steps often remain outside their scope. AI models are typically generic and not trained on customer data, resulting in limited insights. Additionally, these platforms come with high license costs, long implementations and lead times of up to 18 months for the first real use cases.
Material Intelligence Solutions: From Data to More Efficient Development
Material Intelligence is a fundamentally different approach for R&D, distinct from LIMS, ELN and classical R&D platforms. Its core idea is not only to store data, but to understand, link and make it usable for decisions.
It combines three elements in one platform:
- Structured data: harmonised, linked and analysable
- Clear workflows: transparent processes and consistent documentation
- AI assistant: answers to concrete questions using historical formulations and measurement data
This produces a system that not only manages data but actively supports development work. Teams find relevant information within seconds, reuse existing knowledge, reduce redundant experiments and accelerate development cycles significantly. The integrated AI assistant helps analyse and interpret data, making correlations visible more quickly. In addition, the platform provides the foundation for data-driven machine-learning methods such as virtual screening or predictive formulation suggestions.
Material Intelligence is not just another piece of laboratory software—not another LIMS and not a new variant of an R&D platform. It is a digital foundation for research and development: connected, intelligent and future-proof.
A practical example from the coatings industry shows what this looks like in reality:
In a case study, dispersed data sources were consolidated, workflows standardised and formulation development measurably accelerated—resulting in development times reduced by around 30%. The combination of a central data foundation, clear processes and AI support proved to be the decisive success factor.
How to Choose the Right Laboratory Software in 2026
Many organisations know they need to change something, but struggle with the structured decision process. These five steps have proven effective:
1. Clarify needs: What needs improvement? Where are delays occurring today? Which use cases matter most?
2. Market screening: Focus on system logic and data ownership—not brand recognition.
3. Request realistic demos: Avoid generic tours; insist on real processes.
4. Define evaluation criteria: Openness, cloud capability, integrations, support.
5. Prioritise value over price: Which problems are solved, and will the solution still fit in 3–5 years?
Conclusion – What Laboratory Software Must Deliver in 2026
Classical systems such as LIMS or ELN remain relevant, but they cover only parts of the workflow—documentation and standardised processes. Modern alternatives such as R&D software and Material Intelligence solutions close the gaps between documentation, data integration and analysis. For R&D teams, the future lies in systems that make data usable—because only then can development cycles accelerate, expertise be retained, and better decisions be made.
Further details on how LIMS compares to Material Intelligence, as well as practical examples, can be found in a webinar.
FAQ: Frequently Asked Questions About Laboratory Software 2026
What is the difference between a LIMS and a Material Intelligence solution?
A LIMS manages samples, traceability and standardised workflows. A Material Intelligence platform integrates data, workflows and AI within one solution, enabling genuinely data-driven decisions.
Which laboratory software is best suited for research and development?
R&D teams need systems that support iterative processes, flexible comparisons and data integration. Traditional LIMS are often too rigid. R&D platforms and Material Intelligence solutions offer far more support.
When is a modern cloud LIMS worthwhile?
A cloud LIMS is useful when workflows are standardised, teams work across locations, or traceability is essential. For exploratory development work, it is usually not sufficient.
Is Excel suitable as laboratory software?
Excel is versatile but not a database. With growing datasets and team collaboration, inconsistencies arise quickly. For expanding teams and more complex data structures, Excel is only partly suitable.
How long does it take to implement new laboratory software?
Implementation times vary greatly by system type:
- Traditional LIMS: typically several years, often ~3 years
- ELN: around 6 months
- Cloud LIMS / LIMS 2.0: despite modern architecture, often >1 year
- R&D platforms: first use cases after ~18 months; full rollout often 2–3 years
- Material Intelligence platforms like LabV: significantly faster, typically productive after ~3 months
Author: Dr Marc Egelhofer