Laboratory science is entering a period of rapid transformation as automation, artificial intelligence, and new biological models reshape how research is conducted. At the same time, laboratories are facing growing pressure to process more samples, manage increasingly complex data, and meet sustainability and regulatory expectations—all while accelerating discovery and time to market.
To understand how these forces will shape the year ahead, Labcompare recently sat down with a few industry leaders to get their perspectives on the trends likely to define lab science in 2026. From the rise of agentic AI and biologics-focused mass spectrometry to evolving lab operating models and sustainability-driven instrument choices, they outline where labs are investing, the bottlenecks still limiting scale, and the under-reported developments that could have an outsized impact on research and innovation.
Our experts:
- Jon Peters, Director of Marketing, Shimadzu Scientific Instruments
- Matthew Grulke, Chief Technology Officer, LabVantage Solutions
- Mark Paskanik, AIA, Fellow, Lab Planning and Strategy, CRB
- Sudhir Dahal, Product Manager of Raman Spectrometers and Microscopes, Thermo Fisher Scientific
Q: What three trends do you see as breakthrough for laboratory science in 2026?
Peters: In 2026, laboratory science will be transformed by the rapid maturation of AI‑driven automation and method development tools, enabling labs to streamline decisions, accelerate workflows, and reduce staff burden. Biologics‑focused mass spectrometry is also breaking through as biopharma scales, driving demand for higher‑sensitivity, compliance‑ready analytical workflows. Finally, integrated sample‑to‑answer systems are becoming essential as labs face growing throughput pressures and seek more efficient, connected purification and analysis solutions such as Prep LC and fully automated sample preparation modules for LCMS.
Grulke: Agentic AI, Agentic AI, and Agentic AI! I say this with good reason – we are moving beyond AI as a passive assistant toward systems that can plan tasks, make decisions, and act across workflows with minimal human intervention. In the lab, this means Agentic AI can monitor data quality, trigger follow-up experiments, and adapt protocols in real time. In healthcare, Agentic AI can look for anomalies and notify medical teams, among other things.
Essentially, Agentic AI reinforces the role of LIMS from a static data storage repository into a proactive lab assistant. This allows lab scientists to become more resilient, responsive, and scalable with their workflow, which in turn, accelerates innovation. The impact is not just automation, it is a shift in how work gets done, with software taking on operational judgment that previously required constant human oversight.
Paskanik: With gaps in the R&D market and increased research overseas, competition for IP and speed to market will be faster than it ever has. Innovation is going to be key. Additionally, new technology, such as organoids, will reshape our discovery process. We will also continue to see brand new science that will challenge the basis. With that said there will be “aha” moments such as the GLP-1 example. Originally it was designed for a diabetes‑only mechanism, and now it is used as a powerful metabolic‑weight axis.
Dahal: First, faster, non-destructive analytics are showing up earlier in the workflow, and they’re being used in a more routine, pragmatic way than they were even a couple years ago. We’re seeing a lot more interest in techniques like Raman, NIR, XRF, and related spectroscopy tools being run alongside traditional methods. They don’t replace HPLC or chromatography, but they do work well for the kinds of decisions labs have to make quickly such as ID checks, raw material screening, and in-process calls where waiting a day for a full method just isn’t practical. In pharma especially, the pressure is to move faster without giving up the defensibility of the result, and that’s where these tools earn their keep. Second, there’s a shift toward automation that’s actually useful day-to-day, plus better digital connectivity. Automation isn’t just “how many samples can I run” anymore. Labs want instruments and software that talk to each other, push data directly into LIMS, and cut down the number of manual handoffs. That improves consistency, but it’s also a staffing reality: most lab teams are stretched, and eliminating repetitive steps and transcription points is one of the few ways to add capacity without adding headcount. Third, analytics are moving closer to the manufacturing and QA decision points. Instead of doing everything in a centralized lab and finding out late that something drifted, more organizations are placing analytical tools closer to where the process is actually happening in manufacturing, QA, incoming materials. It fits well with PAT and risk-based quality thinking, and it generally leads to fewer “surprises” at the end, when you’ve already sunk time and cost into the batch.
Q: How are sustainability goals influencing equipment choices or experimental design in 2026?
Peters: Laboratories in 2026 are increasingly selecting instruments that reduce energy consumption, solvent use, and overall environmental impact, reflecting growing sustainability requirements. These goals are also reshaping experimental design, pushing teams toward lower-volume workflows, automation that minimizes waste, more circular approaches to resource use, and analytical techniques that are non-destructive, such as high sensitivity XRF, or that use non-toxic reagents, such as supercritical fluid chromatography (SFC), as an alternative to traditional liquid chromatography.
Paskanik: We hope that the industry has a deeper grasp on how this all ties together. We will continue to see improvements in assessment such as the My Green Lab ACT Ecolabel 2.0.
Dahal: They are still influencing both equipment choices and design, even more than a few years ago, and it’s showing up in both purchasing decisions and day-to-day method choices. Labs are paying closer attention to solvent use, consumables, and overall waste, and that naturally makes techniques that require less sample prep or are non-destructive more attractive. In some cases, being able to test a product through its packaging and still ship it if it passes can be a real cost and sustainability win, because it reduces both waste and rework. Energy usage and instrument lifetime are also part of the conversation now, so “sustainable” is becoming less of a slogan and more of a practical, total-cost-of-ownership discussion.
Q: How can labs prepare for the increasing volume of high-throughput experiments, and what bottlenecks are still limiting scale?
Peters: Laboratories preparing for rising volumes of high-throughput experiments are increasingly turning to automated method-development platforms and experimental design tools such as LabSolutions MD software, which used Analytical Quality by Design (AQbD) approach to method development. However, scale is still limited by bottlenecks such as mobile-phase preparation, column and setup changes, and experience-dependent optimization of analytical systems. High throughput multiplex LC-MS/MS systems are in high demand to address these throughput challenges.
Grulke: Laboratories continue to focus on shortening research and routine testing lifecycles. Agentic technology is emerging as a way to both speed up and augment existing processes, and rethink or redefine them entirely. A key near-term priority for labs is to identify practical use cases where agentic AI can reduce cycle times, for example in sample intake and routing, formulations, and reagent and consumable management. At the same time, laboratory and technology leaders will also need to understand the pricing models, security controls, and data privacy associated with Agentic AI, since these factors will shape how quickly the technology can scale.
Paskanik: For clinical and testing labs, their biggest challenge is always the accessioning process and how custom workflows can be integrated with conveyors, pneumatic tubes or the use of drones for sample movement. Integration of AI and digitization with all this testing will greatly enhance results as well by leveraging data in the cloud.
Dahal: A lot of labs are being more intentional about where speed matters most and where it doesn’t. Many are trying to use faster screening methods upstream so they don’t overload slower, more detailed techniques, and that helps keep high-resolution tools available for the analyses that truly need them. Automation helps, but lab directors are thinking more critically about which tests really require gold-standard methods every time, especially when the goal is triage, screening, or in-process decision-making. Even with those improvements, the biggest bottlenecks are still familiar: manual sample prep, data review, and limited skilled staff. Those constraints can cap throughput even when instruments can technically run faster. Even in regulated environments, not every test needs to be “gold standard,” but changing that mindset can take time because it requires confidence, validation strategy, and a comfort level with hybrid workflows that still satisfy quality expectations.
Q: Are you seeing a shift toward decentralized or distributed lab models, and how might that affect collaboration, data sharing and oversight in 2026?
Peters: Yes—labs are steadily shifting toward more decentralized and distributed operating models in 2026, driven by remote‑connected instrument control and the need to coordinate work across multiple sites. While this improves collaboration and expands access to shared resources, it also places greater pressure on organizations to standardize data handling and maintain compliance through centralized systems like Shimadzu’s LabSolutions CS client/server software that unify audit trails, method control, and data integrity across locations. As distributed workflows grow, the biggest challenge will be ensuring that remote flexibility doesn’t outpace oversight structures required for reproducibility and regulatory accountability.
Grulke: It seems to me the trend toward centralization and decentralization are somewhat company and domain-specific. However, what remains consistent is the growing need for laboratories to share data across sites and teams, and an emphasis on making the data AI ready, with consistent structure, context, and governance.
Paskanik: When it comes to pure research, labs are shifting to consolidation and pooling of resources in preferred live-work-play environments. If you are testing or manufacturing, it becomes more of a regional focus based on cities and locations that not only offer incentives but can back that up with the proper talent pool.
Dahal: Yes, especially in pharma and applied markets, and this shift is practical rather than theoretical. Testing is happening closer to raw material receipt, production lines, and even outside traditional lab spaces, which can speed up decisions and reduce downstream surprises. The trade-off is that ease-of-use and consistency become even more important, because methods need to work reliably across different environments and operators. That also puts more emphasis on standardized methods, good data sharing practices, and strong oversight to make sure results are comparable across sites. As lab work distributes, collaboration can improve because teams are closer to operations, but it only works if the organization invests in the fundamentals—consistent workflows, disciplined documentation, and systems that make oversight realistic rather than aspirational.
Q: What under-reported trend in laboratory science do you think will have the biggest impact in 2026, and why isn’t it getting more attention?
Peters: One of the most under‑reported trends for 2026 is the rise of advanced biosensing and research‑grade physiological‑measurement tools like LIGHTNIRS, which are rapidly moving from niche academic use into mainstream life‑science workflows. While these technologies enable real‑time, high‑resolution biological insights that could reshape clinical research, drug discovery, and human‑performance studies, they’re not getting as much attention because they sit outside traditional instrument categories and lack the visibility of chromatography or mass spectrometry platforms. Their impact will grow dramatically as labs integrate them into multimodal analytical pipelines.
Paskanik: Of course, NIH/NSF funding has a tremendous impact, but our hope is that opportunities will continue to streamline and expand while new sources of support can be realized to advance research. We innovate in the lab; we can also innovate in how we secure, structure, and sustain the resources that make discovery possible—building a funding ecosystem as adaptive and forward‑thinking as the science itself.
Dahal: One thing that doesn’t get talked about enough is using complementary analytical techniques more strategically. Fast, non-destructive tools can take pressure off HPLC and other traditional methods instead of trying to replace them outright, and that distinction matters. That hybrid approach can save time, reduce cost, and still meet quality expectations, but it’s not always highlighted because the conversation often jumps straight to “replacement” rather than optimization. In reality, the bigger win in 2026 may come from building smarter workflows that combine techniques well, rather than betting everything on a single “next-gen” method.