Do The Math: The Unrealized ROI In Lab Automation

November 14, 2025
Jana Hersch

The lab automation market is expected to surpass $8.36 billion globally in 2025 and reach $14.78 billion by 2034. Pharmaceutical and biotech companies are making huge investments in lab automation technologies, ranging from robotics to digital record-keeping and traceability to cloud infrastructure scalability. But what’s the ROI?

Where ROI Can Get Murky Quickly

ROI in biopharma automation varies widely. Many drug discovery projects automate physical processes but often stop short after the lab bench. The result: Data analysis and decision-making are not automated, and bottlenecks start to thwart workflows.

Take high-throughput screening, a method used to quickly test many compounds for biological activity. A single run can generate anywhere from thousands to millions of data points. Yet many labs still export results to spreadsheets for cleansing and reformatting. This process is time-consuming and error-prone, which delays decision-making and limits the speed at which insights can be translated into innovation. Here’s where ROI starts to get murky.

Let's Do The Math

In many high-throughput lab environments, scientists spend up to 10 hours per week manually processing experimental data. Saving just 15 minutes a day per scientist can generate significant savings in hard and soft dollars.

For an organization with 1,000 scientists, more than 62,000 hours can be recovered annually, which can result in a significant reduction in repetitive manual work. To streamline these manual processes, pharmaceutical and biotech companies should create automated data pipelines by integrating digital infrastructures (including platforms specifically designed to support complex scientific workflows), automating data analysis and scaling experimental throughput.

Lab automation includes integrating instruments with digital systems, automating experiment design and execution, and connecting upstream and downstream data workflows. When these elements are unified within a robust scientific data backbone, automation becomes truly end-to-end from experiment to insight, resulting in reduced turnaround times.

In contrast, general-purpose tools such as electronic lab notebooks or cloud-based R&D collaboration platforms often lack the capabilities needed for seamless end-to-end automation, including automated experiment execution, integrated data analysis and interoperable data workflows across the R&D life cycle. This ultimately limits their ability to deliver comparable efficiency gains.

Beyond Speed: Data Quality And Compliance

The ROI of automating data analysis goes beyond time savings. Standardized data pipelines reduce human error, enforce metadata integrity and ensure reproducibility, all of which are critical—especially in regulated environments. Automation also embeds compliance into the workflow. Every action is logged, access is controlled, and audit trails are generated automatically. This reduces the burden of documentation and lowers the risk of data integrity violations.

As organizations scale, automation often becomes the foundation for collaboration. Structured, interoperable data enables seamless integration across teams, sites and external partners. Scientists can work from a shared source of truth, eliminate duplication, reduce rework and accelerate alignment.

From Automation To AI/ML

Data automation also lays the foundation for artificial intelligence (AI) and machine learning (ML). While labs are starting to use AI/ML to support real-time decision-making, we expect to become more dependent on these tools. AI systems can be embedded directly into automated workflows, enabling continuous feedback loops where experiments are designed, executed and refined dynamically.

This lab-in-the-loop model enables scientists to remain actively involved in guiding AI models, validating outcomes and applying domain expertise to ensure decisions align with broader research goals. It creates AI-ready data pipelines that AI systems depend on to function successfully.

The Human Dividend

One of the most overlooked benefits of automation is its impact on people and innovation. When scientists are free from repetitive, low-value tasks, they become more efficient and more engaged. This allows them to think more creatively and collaborate more effectively.

In my experience working with the top 25 pharmaceutical companies, automating data workflows enables a fundamental shift in how R&D operates. It enables scientists to effortlessly initiate specific experiments, automatically monitor the progress and outcomes of analyses, and accelerate decision-making by allowing them to focus only on samples and results that require human expertise. It also enables seamless integration of all results with other findings in a linked and traceable information system.

The bottom line: Scientists have greater opportunities to discover new candidates for novel drug therapies.

ROI By Design

High-performance laboratories treat data automation as a core infrastructure. They build data architectures that enable interoperability, forming cross-functional teams that bridge scientific and IT expertise. Governance models, vital to infrastructure, align digital strategies with scientific priorities, ensuring that compliance is integrated into the system by design rather than as an afterthought. Most importantly, research labs do not wait for ROI to appear. They design for it by identifying bottlenecks in the data automation process.

Common bottlenecks include slow sample processing, manual data handling and data analysis delays. Analyzing workflows and metrics such as throughput and cycle times can help target specific issues that data automation can resolve.

To identify bottlenecks in lab data automation, map your current workflows and collect process data (e.g., timestamps, throughput) from event logs. Analyze these data to find stages with low throughput or excessive wait times, which will pinpoint areas of constraint/bottlenecks. The success of this exercise requires buy-in and support from all stakeholders who can help identify and measure what truly matters.

Moreover, even in these environments, success requires operational discipline and process maturity. An organization with process maturity has processes that produce predictable, consistent and repeatable results. These processes are standardized across the organization and are continually measured and analyzed for optimization.

Getting The Biggest Bang For Your Buck: Rethinking ROI

While data automation saves money and improves productivity, its most transformative impact lies beyond the experiment. When data flows seamlessly from generation to insight, innovation is advanced by improved efficiencies and scientific clarity. To realize new ROI, we must reimagine how science is conducted to deliver innovative life-saving therapies.

This article originally appeared on Forbes.com