Transforming Clinical Lab Workflows with AI and Automation

Laboratories of all types have been a source of innovation, usually for scientific development, but the pace of change today, like in many areas, is unprecedented. Artificial intelligence (AI) and automation are no longer futuristic concepts—they are practical tools reshaping how labs operate. These technologies are helping labs meet growing demands for speed, accuracy, and efficiency while addressing workforce shortages and cost pressures.

Sample processing is one of the most time-consuming steps in the laboratory workflow. Traditionally, this involves manual pipetting, labeling, and preparation—this is error-prone work that generally scientists do not enjoy doing. A colleague of mine likes to say labs are “where the magic happens”—we want our scientists to be making magic, not labels. AI-driven automation systems now integrate robotic arms, smart sensors, and machine vision to streamline these processes.

For example, AI-powered visual inspection can verify sample integrity and detect anomalies before analysis begins. Automated liquid handling systems, guided by AI algorithms, adjust pipetting volumes based on sample characteristics, reducing variability, and improving reproducibility. These innovations accelerate turnaround times and improve quality.

Beyond individual tasks, AI and automation can also transform workflows. Predictive analytics can forecast sample volumes based on historical trends and seasonal patterns—leveraging supply chain technology from other industries. 

One of the biggest changes I have seen in industry over the past five years is life sciences and other regulated fields wanting to adopt technologies from other industries instead of being scared by it. By utilizing advanced analytics or machine learning algorithms on sample volumes, resources—both people and equipment—can be scheduled intelligently to optimize throughput or prioritize higher value assays.

While AI and automation bring efficiencies, they are not a substitute for human expertise. Instead, these technologies should empower scientists, allowing them to focus on “the magic”—interpretation, troubleshooting, and patient-centered decision making. According to CRB Group’s 2025 Horizons Life Sciences Report, only 33 percent of life sciences organizations are currently exploring the use of AI to improve employee wellness or job satisfaction, but this number is expected to grow rapidly as companies recognize the value of autonomy and fulfillment at work.

Training and upskilling remain essential as labs adopt these tools. Professionals who understand both the science and the technology will be key to unlocking the full potential of AI.  This also needs to be a cultural focus of the company—embrace AI use and measure it. 

Labs that embrace these innovations today will be better positioned to meet tomorrow’s challenges—aligning with the general idiom of doing more with less—delivering faster results, improving patient outcomes, and driving the next wave of digital transformation.