Large-scale automation is a priority for labs that face mounting pressure to increase throughput while managing greater operational complexity and ongoing staffing challenges. Too often, automation is approached as a technology upgrade, with the assumption that the right purchase can meet the organization’s needs. In practice, full automation extends beyond technology, changing how the lab operates, influencing workflows, decision-making, and more—not just how individual tasks are completed.
To ensure success when approaching automation, lab leaders must take a systems-level approach to address key non-technical factors, including change management, workforce adaptation and reskilling, and data integration and interoperability across instruments and information systems.
What is full automation at scale?
Automating an entire lab is very different from automating individual tasks. At scale, automation involves interconnected instruments, automated sample handling, scheduling, and integrated data streams potentially spanning multiple workflows. The goal of full automation is to streamline how tasks flow through the laboratory as a system.
Full automation spans various teams and processes, and operational complexity increases with more handoffs and decision points. It is important for lab managers to understand that this changes how the work is completed as well as how responsibilities are distributed. This complexity also increases the risk of bottlenecks if the workflows are not carefully designed.
Automation requires change management
The impact of organizational change is often underestimated and can pose challenges for labs implementing large-scale automation. A common mistake is to apply automation to existing workflows without pausing to reevaluate processes. This can preserve inefficiencies rather than eliminate them—opposite to the goals of automation. Other issues, such as outdated standard operating procedures (SOPs), unclear ownership, and more, can blunt the benefits of automation. Effective change management is key to overcoming these challenges.
Lab managers should begin by aligning automation goals with the lab’s operational strategy. To do so, leaders should engage stakeholders across scientific, technical, quality, and IT teams early in the process before decisions have been made. A phased approach to implementation can also help manage disruption and give staff time to adapt gradually and identify issues before they scale.
Lab leaders have the very important role of delivering clear and ongoing communication throughout the transition. This can include setting and sharing realistic expectations and the associated challenges (like temporarily reduced productivity), as well as the long-term goals of automation. This ongoing communication must continue after a system is deployed, and include opportunities for feedback and workflow refinements.
Implications for the laboratory team
There may understandably be some uncertainty among lab staff when implementing large-scale automation, especially when these changes impact familiar tasks and protocols. Fears surrounding job security and loss of autonomy can contribute to resistance and an unwillingness to embrace change. It is important that lab leaders address these concerns proactively, emphasizing that in most cases, automation changes but does not eliminate roles. It can also be helpful to emphasize automation’s beneficial role in oversight and optimization.
While these fears are often the worst-case scenarios that are unlikely to occur, they are not entirely unfounded. As automation scales, challenges may present as skills gaps in the form of systems-level thinking, troubleshooting new workflows, data interpretation, and collaboration across teams and disciplines. Lab managers can support their teams by redefining roles early in the process, including outlining change, and more importantly, demonstrating where new opportunities exist.
To support these opportunities, reskilling and upskilling pathways need to be in place. Training should go beyond how to operate new instruments and software and include data literacy, process optimization, troubleshooting, and more. As team members’ roles evolve, performance metrics should be adjusted accordingly. For example, shifting from task completion to throughput or quality outcomes.
Data integration and interoperability
In addition to physical systems, successful large-scale automation depends on how well data flows through the lab. Disconnected instruments, siloed software, and more can limit efficiency and introduce risk into even highly automated environments. Data integration and interoperability are key to enabling automation to function as a cohesive system.
Poor integration can undermine automation efforts by introducing bottlenecks, data inconsistencies, and compliance challenges. It can also limit data visibility, making it difficult for lab leaders to assess performance or identify inefficiencies.
By contrast, seamless data flow helps labs access the full benefits of automation. Data that moves automatically and consistently across instruments and systems reduces the need for manual data transfers, which are time-consuming and error-prone. Integrated data flows also support traceability, data integrity, and decision-making.
When planning for interoperability, lab managers should carefully consider system compatibility, data standards, and governance. It is essential for lab managers to collaborate with IT and quality teams to ensure that data infrastructure can scale with automation. Addressing this early is another key to successful automation.
Establishing an automation-ready lab
Ensuring an automation-ready lab is as important as the technology. Signs the lab is ready include clearly defined and standardized workflows and trained staff ready to manage and optimize automated systems. Clear decision-making frameworks should support automation across interconnected workflows and processes, not just individual tasks.
Another key characteristic of an automation-ready lab is understanding that implementation is not the endpoint. Ongoing monitoring and improvement are needed as workloads change, new technologies become available, and the lab’s priorities evolve. Lab managers should regularly review workflows and performance metrics to ensure automation is meeting the lab’s needs. Treating automation as an ongoing effort also allows lab leaders to build more resilient and scalable systems.
Large-scale lab automation is a significant undertaking for lab leadership, not just a technology upgrade. Advanced systems and instruments can help increase efficiency and throughput, but successful implementation depends on how well people, process, and data are aligned. Lab managers are responsible for guiding their teams through this transition and ensuring the lab is ready to adopt significant change. By prioritizing change management, workforce planning (reskilling and upskilling), and data integration, lab managers can achieve greater efficiency and resilient operations.
