Where Intelligent Automation Actually Works in Drug Discovery and Clinical Trials

Intelligent automation is already operating at three distinct points in the life sciences pipeline: laboratory analysis and preclinical work, clinical trial design, and clinical trial monitoring. At Pfizer's R&D site in Andover, automated tissue workflows delivered a roughly 40% increase in throughput for downstream LC-MS analysis while significantly reducing analyst workload. In clinical research, agentic AI is beginning to absorb the routine site communications and data requests that consume Clinical Research Associates' days, freeing them for strategic oversight. The pattern across these examples is consistent: machines take over repetitive, high-volume tasks, while humans retain critical decision-making.

Introduction: The Automation Revolution in Life Sciences R&D

Drug development has long been constrained by inefficiencies that span the entire research and development pipeline. A review in AI in Clinical Medicine summarizes ten key domains where AI applications are emerging with varying degrees of demonstrated impact, ranging from target identification and hit discovery through preclinical modeling, clinical trial design and stratification, and regulatory decision support. The breadth of that list matters: automation is not a single technology applied at a single bottleneck, but a set of tools spreading across the whole workflow.

What distinguishes the current wave from earlier laboratory mechanization is intelligence. Systems are no longer merely executing fixed protocols; they are scanning data, drafting communications, and supporting decisions. Reporting by Chemical & Engineering News describes how, in the preclinical stages, companies use advanced AI tools and automation to scan for new proteins implicated in diseases and explore chemical space to identify drugs that can target those proteins. The open question, as that reporting notes, is whether these tools can ultimately bring a larger number of novel drugs to market.

Intelligent Automation in the Drug Discovery Workflow

At the laboratory bench, automation targets the repetitive physical steps that limit how many experiments a research team can run. Tecan's eProtein Discovery benchtop system, for example, is designed to speed up protein expression and purification by automating construct screening, using digital microfluidics, protein quality assays, and cell-free protein synthesis on dedicated cartridges. For labs using AI for protein design, this removes a manual bottleneck between computational prediction and experimental validation.

Instrument integration extends the same logic to analytical chemistry. Rahul Baghla, Senior Manager of the Echo MS Center of Excellence at SCIEX, described a collaboration with Beckman in which samples in kinetic studies were analyzed at five seconds per sample, "without disrupting the timing of the kinetic study and with no sample clean up." Kinetic reactions are critical for understanding how compounds behave in changing physical and chemical environments, so preserving study timing while accelerating analysis is the specific value automation delivers here.

Upstream of the wet lab, preclinical modeling applies AI to the search for targets themselves. As Aayushi Pratap's reporting in Chemical & Engineering News details, companies deploy AI and automation to scan for disease-implicated proteins and explore chemical space for candidate drugs—an application that sits squarely in the preclinical stage of discovery.

Streamlining Clinical Trials with Intelligent Automation

Clinical trials present a different automation problem: less pipetting, more coordination. In an interview with Applied Clinical Trials, Michelle Longmire, a physician and co-founder and CEO of Medable, highlighted that 45% of clinical development time is wasted due to delays and siloed data. Her diagnosis points to where automation can bite—administrative friction rather than science.

Agentic AI addresses this by operating across the systems a CRA must navigate. Longmire described the CRA's reality: "When I'm a CRA, I am looking across over 13 systems. Sometimes I'm just emailing a site to say, 'Hey, can you provide me this one missing data piece?' or 'This site is enrolling faster. Let's kick off more drug supply now.'" These are exactly the monitoring tasks—routine site communication, data requests, document management—that CRA-focused agents are beginning to automate. Integration across legacy systems is facilitated by model context protocols, which reduce the manual effort of moving between platforms, and no-code AI platforms can be given clear job descriptions and validated much as human staff are trained.

At the design stage, the academic review identifies clinical trial design and stratification as one of the ten emerging AI domains, meaning automation's footprint in trials extends from protocol planning through day-to-day monitoring.

Intelligent Automation Across the Drug Discovery and Clinical Trial Pipeline

The table below maps specific automation applications from the evidence onto the R&D workflow, linking each stage to the technology used and the benefit reported. Use it to trace where automation fits and what outcome it produces, from early-stage lab work through late-stage trial administration.

Mapping automation technologies to R&D pipeline stages and their reported benefits
Stage of R&D Pipeline	Automated Task	Technology Used	Reported Benefit
Lab/preclinical — tissue workflows	Tissue processing for downstream LC-MS analysis	Automated lab workflows adopted by Pfizer's R&D team at Andover	~40% increase in throughput; ~5-fold wider working sample range; significant reduction in analyst workload; lower risk of operator error or variability
Lab/preclinical — protein discovery	Construct screening for protein expression and purification	Tecan eProtein Discovery: digital microfluidics, protein quality assays, cell-free protein synthesis	Faster protein expression and purification, including for labs using AI protein design
Lab/preclinical — kinetic analysis	Sample analysis in kinetic reaction studies	Integrated Beckman/SCIEX mass spectrometry instrumentation	Samples analyzed at five seconds per sample, with no sample clean up and no disruption to study timing
Lab/preclinical — target discovery	Scanning for disease-implicated proteins; exploring chemical space	Advanced AI tools and automation	Supports identification of drug targets in preclinical stages
Clinical trial design	Trial design and patient stratification	AI applications (emerging, with varying degrees of demonstrated impact)	Identified as one of ten key domains where AI supports faster, data-driven decisions
Clinical trial monitoring	Routine site communication, data requests, document management	Agentic AI, including CRA-focused agents; model context protocols for legacy-system integration	Reduces manual effort and frees CRAs for complex problem-solving and relationship building

Read together, the rows show a division of labor: physical automation dominates the lab, AI-driven search dominates preclinical modeling, and agentic software dominates trial administration. The common thread is that each application removes a volume constraint—of samples, candidates, or communications—that previously required human hours.

Measurable Benefits: Increased Throughput and Efficiency

The most concrete numbers come from the laboratory. Pfizer's Andover team reported a roughly 40% throughput increase for automated tissue workflows, alongside a roughly five-fold increase in working sample range compared with traditional methods, a significant reduction in analyst workload, and lower risk of operator error or variability. The SCIEX-Beckman collaboration contributes a second data point: five seconds per sample in kinetic studies, achieved without sample clean up.

On the clinical side, the quantified baseline is the waste itself. Longmire's figure—45% of clinical development time lost to delays and siloed data—defines the opportunity that agentic AI targets. The benefit case for trial automation is therefore framed as reclaiming that wasted time, shifting CRAs from reactive data collection toward proactive, strategic oversight of the sites and studies they manage.

The 'Human-in-the-Loop' in Automated Clinical Research

Automation in clinical research is explicitly designed around a boundary, not a replacement. The human-in-the-loop concept, as Longmire described it, ensures that AI supports rather than replaces human decision-making: automation handles routine tasks, while critical decisions remain human-controlled. The dividing line, in her framing, "really comes down to the work at hand."

In practice, the tasks best suited for automation are the repetitive ones—emailing a site for a missing data point, flagging that a site is enrolling faster and kicking off more drug supply, chasing documents across systems. The tasks that remain human-centric are the complex ones: problem-solving, relationship building with trial sites, and judgment calls about risk and strategy. Even the tooling reflects this division; no-code AI platforms require clear job descriptions and validation similar to human training, keeping the technology accountable within a defined scope.

Actionable Insights: Embracing Automation for R&D Efficiency

Organizations should strategically integrate intelligent automation into lab/preclinical stages and clinical trial administration to enhance efficiency and enable human resources to focus on critical tasks. The evidence supports a staged reading of where that integration pays off: physical lab automation where throughput and operator variability are the constraints, and agentic AI where administrative coordination is the constraint.

For teams evaluating adoption, the measurable indicator to monitor is a measurable increase in throughput for automated workflows and a reduction in time spent on routine administrative tasks by human researchers and CRAs. Pfizer's reported 40% throughput gain and the 45% of clinical development time currently lost to delays and siloed data give both sides of the pipeline a concrete baseline against which any automation program can be judged.