Lab Automation in Practice

Typical challenges include unclear objectives, non-scalable technical designs, late stakeholder involvement, missing standardization and difficulties in justifying ROI. In QC and GMP environments, additional challenges include regulatory impact, validation, space constraints, budget, vendor support and container or labware variability. The discussion also highlights that automation projects can become overly complex if teams try to automate too much too early.

The experts recommend starting with a clearly defined problem, preferably a capacity bottleneck where value and ROI are easier to track. Rather than beginning with the largest or most complex project, teams should choose a manageable use case that is likely to succeed and can build credibility. The discussion also emphasizes going into the lab, observing current workflows and challenging requirements before designing the automated process.

Lab Automation can create value through capacity gains, throughput improvements, reduced manual effort, better data quality, improved compliance, fewer deviations, reduced paper-based work, better turnaround times and improved ergonomics. The experts note that cost savings are often the easiest value to put into a business case, but they are not the only relevant KPI.

Standardization is one of the main prerequisites for scalable Lab Automation. Differences in tube types, containers, labware, methods, devices and data formats can limit transferability between labs or sites. The experts describe standardization as especially difficult in brownfield labs, but also point out that automation can help enforce more consistent processes over time.

Many lab automation projects are ultimately also data projects. Automated systems generate data that needs to be transferred into established software environments such as LIMS or chromatography data systems. The experts discuss the importance of open interfaces, APIs, software development kits and non-proprietary data formats to support integration and reduce repeated custom driver development.

In QC and GMP environments, validation and compliance must be addressed from the beginning. Jeff van Doren explains that global validation and compliance teams are involved in user requirements before purchase. The discussion also touches on audit trails, comparability studies and the need to understand whether a robot performing a task changes the regulatory impact compared with a human performing the same task.

The experts point to industrial automation principles, modularity, open interfaces, standardization and stronger data integration as important future directions. They are more cautious about highly visible technologies such as humanoid robots or mobile robots where the practical problem is unclear. Agentic AI for robotic automation is mentioned as an experimental area in R&D, while QC remains constrained by validation and compliance requirements.