Pharmaceutical research is generating more data, while drug development remains a lengthy and expensive process. IQVIA reports that the median end-to-end clinical development timeline reached 10 years in 2025, the longest point of the past decade. The Congressional Budget Office reinforces this picture, estimating that average R&D costs range from less than US$1 billion to more than US$2 billion per new drug, including failed drugs and capital costs. For pharmaceutical laboratories, the challenge is therefore not only to conduct more research. It is to reduce manual work, make experimental information easier to use and connect the systems involved in scientific decision making.
Five trends are reshaping how that work gets done: voice interaction, artificial intelligence, automation, interoperability, and the growing connection between precision medicine and sustainable research.
1. Moving beyond the screen: How voice is changing the lab
Scientists spend significant time working with software, entering information and completing digital documentation. As laboratory workflows become more digital, these interactions increasingly become part of the work itself. Voice interaction offers an alternative to constant keyboard and screen use. Scientists interact with digital systems through spoken commands, reducing manual software interaction and administrative work.
Augmented reality is also being explored in laboratory environments. Its role is to bring digital information closer to the physical work being performed, rather than requiring scientists to move repeatedly between laboratory activity and separate software interfaces.
The move from paper to digital documentation provides another example. For instance, Laboperator digitises paper documentation. Sustainability is a core part of the Lab Execution System (LES) software offering. By replacing paper-based documentation with digital workflows, the solution reduces paper consumption while supporting more efficient laboratory operations. These changes affect how scientists work with laboratory technology. The interface becomes part of the laboratory workflow rather than a separate administrative layer around the experiment.
2. Turning research history into a source of discovery with AI
Pharmaceutical companies have spent years digitising laboratory processes and accumulating experimental data. Years or decades of research therefore exist within company systems, creating a substantial historical record of experiments and results. Artificial intelligence provides a way to analyse that record. AI systems recognise relationships and patterns across large datasets and search historical information for similar experiments and results.
That changes the value of previous research. Experimental records are not only documentation of completed work. They provide information that researchers can use when evaluating new experiments and deciding what to investigate next.
The infrastructure used for this analysis also matters. Larger pharmaceutical companies are developing and operating internal AI models locally, keeping research data within their own infrastructure. The World Economic Forum highlights usable, representative data, trustworthy AI and interoperability as important enablers of AI adoption in healthcare. The need for usable research data becomes particularly important when considering drugdevelopment outcomes. A review in Nature Reviews Drug Discovery identified a lack of predictive preclinical models as one of the key reasons for high attrition in oncology drug development.
Researchers are also examining AI protein-folding research and quantum computing alongside classical AI. These approaches add new computational methods to pharmaceutical research, while scientists remain responsible for evaluating the evidence produced by those systems.
3. Redefining the scientist’s role in an automated lab
Automation is changing how work is distributed inside the laboratory. Scientists continue to define objectives, plan experiments, understand previous research and determine the next steps. Automated systems perform more repetitive execution, while researchers superviseprocesses, interpret outputs and make decisions. The change resembles developments in software development, where automation handles parts of repetitive execution while people remain responsible for defining requirements and reviewing results.
In pharmaceutical laboratories, this transition is progressive rather than a move directly to fully autonomous research. A fully self-improving system remains further on the horizon. The more immediate change is the scientist’s relationship with laboratory execution. Researchers spend less time performing repetitive actions and more time interpreting results and deciding how those results affect the next stage of the research process.
This shift also increases the importance of digital skills. Scientists need to understand how laboratory technologies, instruments, data systems and software platforms interact because research increasingly depends on those systems workingtogether.
4. Building a connected lab on open standards
Automation and AI depend on data moving between laboratory systems. That is difficult when laboratories use equipment from dozens of manufacturers, each with different data formats and technical ecosystems. Open standards address this problem by providing common methods for communication between equipment and software. OPC UA LADS and SiLA are relevant standards with different purposes. Both facilitate communication between laboratory equipment and software. A balance measurement, for example, can be retrieved automatically by another system rather than transferred manually by a scientist.
Interoperability also affects equipment investment decisions. Laboratory instruments often remain in use for years or decades, so a connected laboratory needs to accommodate equipment purchased at different points in time. Decades-old instruments are already being integrated into modern laboratory systems. Open communication standards therefore support the continued use of existing equipment while allowing laboratories to add newer systems. The issue is also directly connected to data quality. When information moves reliably between instruments and software, researchers have a more consistent basis for analysing experimental results and reusing laboratory data.
5. Bringing precision and sustainability into the research workflow
Precision medicine is increasing the amount and variety of information involved in pharmaceutical research. The Personalized Medicine Coalition reported that personalised medicines accounted for 38% of new drug approvals by the US Food and Drug Administration (FDA) in 2023. As personalised approaches develop, experimental information also needs to be reused across different applications. A chemical reaction associated with one drug, for example, may need to be recorded multiple times when developingpersonalised versions.
That makes structured and accessible research data increasingly important. Information captured during one experiment needs to remain available when researchers return to it for another application. Laboratory sustainability introduces another operational requirement. Laboratories generate waste through consumables and other materials, creating a need to distinguish necessary consumption from avoidable use.
Any change to laboratory processes also takes place within extensive regulations and standards. Requirements reviewed by regulators and independent organisations therefore remain part of decisions about how laboratory workflows arechanged. Animal testing is another area under examination. Animal testing has historically played an important role in medicine and vaccine development, while organ-on-a-chip and lab-on-a-chip technologies are being developed to reduce reliance on some forms of animal testing. Further development remains necessary. Precision medicine and sustainability therefore both increase the importance of how laboratories capture, manage and reuse information while controlling the resources required to conduct research.
Connecting the laboratory workflow
These five trends address different parts of pharmaceutical research, but they meet in the laboratory workflow. Voice interaction changes how scientists interact with digital systems. AI changes how historical research data is analysed. Automation changes how laboratory tasks are divided between people and machines. Interoperability determineswhether instruments and software exchange information reliably. Precision medicine and sustainability increase the requirements for reusable data and efficient laboratory processes.
For pharmaceutical laboratories, the practical question is how these capabilities work together in daily research. Scientists need systems that reduce repetitive data handling, connect equipment and software, preserve experimental informationand support decisions based on research results.
Turning these principles into practice requires a more automated approach to laboratory operations. Laboperator connects laboratory equipment, workflows and data within a digital laboratory environment, helping organisations build more integrated and scalable research operations. Explore Laboperator to see how automated laboratory infrastructure can support more efficient pharmaceutical research: https://laboperator.com/contact