The Next Generation of Laboratory Automation Isn't Hardware. It's Cognitive.
How AI-driven data integration is moving laboratories beyond workflow automation toward intelligent operations.
The introduction of laboratory automation and information technology in the early 2000s marked a turning point for clinical laboratories, moving operations beyond a predominantly manual environment. Over the past two decades, automation and software have become integral to routine laboratory operations across most disciplines. Workflows that once depended on labor-intensive specimen loading, processing, analysis, and unloading are now increasingly managed by robotics, automated track systems, and instrument-based technologies. These advances have improved throughput, reduced manual intervention, and brought greater consistency to laboratory processes.
Instrument manufacturers and laboratory IT companies have invested heavily in moving the laboratory into a new era of sample management and digital data processing. Hardware and software innovations have delivered substantial operational efficiencies, helping laboratories manage increasing workloads amid a declining workforce. Instrument middleware and successive generations of laboratory information systems (LIS) have further connected testing disciplines and operational workflows across organizations. Results that were once printed, reviewed, and manually distributed can now flow electronically through integrated systems and reach patients directly through portals. Although data silos and fragmented platforms remain, the industry has made significant progress in moving laboratory information from paper-based processes into the digital environment.
As laboratory automation has assumed many of the manual tasks once central to daily operations, the workforce has adapted to a different relationship with the testing process. Specimens move through preanalytical processing and analytical testing, then into postanalytical storage and archiving, with far fewer hands-on interventions. Alongside these changes, the role of the laboratory professional has shifted from primarily performing and managing physical processes to monitoring workflows, reviewing and validating results, and acting on the data that drives laboratory operations.
Artificial intelligence is extending this evolution beyond physical workflow automation into the analysis and interpretation of laboratory data. AI and machine learning applications are emerging across all three phases of testing, from specimen identification, sample quality assessment, and clot detection to automated cell classification, digital image analysis, interpretation of complex laboratory results, and longitudinal monitoring of biomarkers (Dodig et al., 2025). These capabilities introduce a different dimension of laboratory intelligence, one in which technology does not simply perform tasks or move specimens but increasingly analyzes information and supports decisions.
Shift from Process Automation to Cognitive Automation
The next stage in laboratory automation represents a fundamental shift in how technology interacts with laboratory processes and data. Traditional automation is designed to move specimens from point A to point B and execute standardized operations based on predefined rules, thresholds, and decision points. It follows an established sequence of actions, responding to conditions that have already been anticipated and programmed into the system.
Cognitive automation takes a different approach. Rather than simply executing predefined instructions, it connects information from multiple sources, recognizes patterns, and evaluates variables in parallel to support more informed decisions. Artificial intelligence, including machine learning and natural language processing, expands the ability to interpret information in context. Instrument parameters, specimen characteristics, patient demographics, diagnosis, and the patient's status at a particular point in time can be considered together rather than as isolated data points.
The distinction is important. The traditional model relies on predefined rules governing individual actions along a largely linear workflow. Cognitive automation introduces the ability to evaluate changing conditions, recognize relationships across datasets, and identify patterns that may not be apparent when information is viewed separately. As systems incorporate additional validated data and feedback, they can become more responsive to changing laboratory and patient conditions.
Engel, Ebel, and Leimeister (2022) distinguish cognitive automation from traditional process automation by emphasizing the application of machine learning to knowledge work. Rather than simply eliminating repetitive tasks, cognitive automation enables systems to process information, recognize patterns, and generate outputs that support or extend human decision-making.
What Do We Mean by Cognitive Automation?
At its simplest, cognitive automation is the integration of physical laboratory automation with AI-driven data integration, analysis, and decision support. It brings together information generated throughout the testing process and uses that information to help determine the most appropriate next action. Rather than treating each specimen, instrument, or result as an isolated event, it considers the broader context in which the data was generated.
Several capabilities illustrate this evolution:
1. Specimen Movement Becomes Specimen Intelligence
Traditional automation tracks where a specimen is located and moves it through established processing steps. Cognitive automation expands that view by incorporating additional information about the specimen, including environmental temperature, time from collection to receipt and analysis, specimen integrity indicators, and sample volume. When connected with collection date, time, and relevant patient information, these variables can help establish a more complete picture of specimen suitability and identify conditions that may affect downstream testing.
2. Predefined Rules Become Contextual Decision Support
Traditional automation applies established rules to expected decision points. Cognitive automation can evaluate additional information as it becomes available, placing a result or operational event into a broader context. A data point that might otherwise trigger a standard response can be assessed alongside instrument performance, specimen characteristics, patient information, and other relevant variables. This creates opportunities to identify exceptions, recognize patterns, and support decisions that account for conditions at that particular moment in time. These clinical decision pathways once validated become the next building block of patient care.
3. Repetitive Tasks Become Intelligent Monitoring
Every movement and processing step generates information. Specimen volume, sample type, tube integrity, and the condition of the biological material are examples of variables that traditionally require manual inspection or predefined automated checks. Software-enabled recognition, RFID technology, and machine vision can expand these capabilities by identifying specimen characteristics and integrating that information with downstream processing requirements. Instead of relying exclusively on manual, tube-by-tube checks, laboratories can move toward automated monitoring that evaluates multiple specimen attributes and helps determine whether additional action is needed.
4. Operational Feedback Becomes a Continuous Learning Loop
Each processing action, instrument interaction, and result generates data that can provide insight into laboratory performance. Cognitive automation can connect these data points to identify emerging patterns involving specimen quality, instrument performance, result exceptions, and environmental conditions. This feedback loop creates an opportunity to move beyond retrospective reporting toward ongoing evaluation of what is happening across the laboratory and why.
5. Problem Identification Becomes Proactive
As systems incorporate more connected data and validated patterns, they can help identify potential problems earlier and with greater precision. Specimen integrity concerns, recurring processing errors, instrument performance changes, and emerging workflow bottlenecks may become detectable before they create significant downstream consequences. The objective is to anticipate issues, support timely intervention, and minimize their impact on testing operations, connected systems, and ultimately the patient.
The Bottom Line
Cognitive automation moves laboratory technology beyond executing predefined processes. Its value lies in connecting disparate systems and datasets, interpreting information in context, and translating that information into actionable intelligence. The opportunity is not simply to automate more tasks, but to make the entire laboratory workflow more responsive, informed, and efficient while supporting the accuracy and reliability of laboratory results.
From Data Visibility to Operational Intelligence
Imagine a laboratory dashboard that brings together specimen movement, instrument performance, quality indicators, and result analytics in a single operational view. The specimen dashboard, tracking incoming and outgoing samples and turnaround times, is integrated with instrument processing timelines, instrument status, QC performance, and predictive indicators of operating conditions. From specimen receipt through testing, result reporting, and eventual archiving, the laboratory can see how individual events connect and influence overall performance.
This is the next generation of the laboratory operations cockpit that many envisioned during the early development of automated specimen transport. What once required multiple screens, separate reports, and manual interpretation is moving toward an integrated view of laboratory operations. Cognitive automation makes it possible to connect these information sources, identify relationships, and translate operational data into intelligence that can support timely decisions.
The economic implications are significant. In an increasingly complex reimbursement environment, laboratories must find ways to protect operating margins while maintaining quality, managing workforce constraints, and meeting growing demands for testing. Better use of connected data creates opportunities to improve resource utilization, reduce avoidable costs, increase throughput, and identify operational problems before they affect performance.
The potential benefits include:
1. Error Reduction and Mitigation
Information accumulated across systems and specimens can be evaluated to identify conditions associated with potential errors. Rather than relying exclusively on a single instrument flag or predefined threshold, connected data can reveal patterns that may indicate an emerging problem. The system can alert laboratory personnel to the issue, provide relevant information, and support corrective action before the condition affects additional specimens or results.
2. Throughput and Operational Efficiency
Cognitive automation can support more sophisticated prioritization of specimens based on urgency, complexity, processing requirements, and available instrument capacity. By evaluating cumulative workflow and instrument data, systems can help identify the most efficient processing sequence, direct priority specimens appropriately, and reduce avoidable delays. The result is an opportunity to improve throughput without simply adding more instrumentation or personnel.
3. Quality and Patient Safety
AI-enabled analysis can evaluate patterns across QC, calibration, reagent performance, instrument behavior, and other quality indicators. By considering multiple inputs together, these systems may identify subtle changes or developing trends that warrant investigation before they become significant quality events. These capabilities can strengthen existing quality monitoring, while established QC requirements, validated acceptance criteria, and professional review remain essential.
4. Decision Support Across the Laboratory Enterprise
Today's decision support systems have established an important foundation, but cognitive automation creates an opportunity to expand their reach. By integrating operational, analytical, and relevant clinical information, systems can generate data-driven recommendations tailored to different levels of the organization. Technologists may receive alerts related to specimen or instrument conditions, laboratory managers may gain insight into workflow and resource utilization, and laboratory directors may have a broader view of quality, performance, and financial indicators. Where appropriate, relevant information can also support communication with clinicians.
The larger opportunity is to move from dashboards that report what has happened to intelligent systems that help explain why it happened, identify what may happen next, and support decisions about what to do.
The Human Element Doesn't Disappear
How should laboratory professionals respond to a transformation that is changing the nature of laboratory operations and the responsibilities of the people who manage them?
Cognitive automation does not diminish the importance of laboratory expertise. In fact, it increases the need for professionals who understand the science, technology, workflows, and quality systems behind laboratory testing. AI systems must operate within defined parameters that support the laboratory's clinical, operational, and regulatory requirements. Their outputs must be evaluated, validated, monitored, and governed.
The adoption of AI does not replace the quality systems that laboratories have spent decades developing. Those systems become even more important as additional layers of automation and data integration are introduced. A sound governance framework must include:
Standardized processes that define how automated systems interact with laboratory workflows.
Validation and verification protocols to establish that systems perform as intended and produce reliable outputs.
Traceable system documentation covering data sources, algorithms, configuration changes, system performance, and decision pathways.
Regulatory compliance aligned with applicable laboratory requirements and accreditation standards.
Strong data security practices to protect patient information and maintain the integrity and availability of connected systems.
Human oversight to ensure that AI-generated recommendations and automated actions remain appropriate, explainable, and subject to professional review.
Laboratory professionals have an integral role in advancing the next generation of data-driven automation. A structured, human-led approach that combines AI's analytical capabilities with laboratory expertise in testing, process management, and quality oversight is what will make this transformation successful.
Laboratory leaders must be prepared to participate in defining how these systems are designed, implemented, and governed. The future laboratory will need professionals who can work across the boundaries of laboratory science, informatics, operations, and technology. The objective is not to remove people from the process, but to give them better information and more powerful tools to make decisions that improve operational performance and patient care.
The Question for Laboratory Leaders
Are you investing in the next generation of laboratory automation, or continuing to automate yesterday's workflows?
Do you have a strategic roadmap for building an AI-enabled laboratory? Are your data systems positioned to communicate with one another? Are your laboratory teams involved in defining the opportunities, evaluating the technology, and establishing the governance required to use it responsibly?
The question is no longer simply when AI will become part of laboratory operations. It is how laboratory leaders will prepare for it, where they will apply it, and how they will ensure it delivers measurable value.
The next era of laboratory automation will not be defined by how many specimens a system can move or how many manual steps an instrument can eliminate. It will be defined by how effectively laboratories connect their data, interpret information, and turn operational intelligence into better decisions.
Reference
Dodig, S., Čepelak, I., & Dodig, M. (2025). Are we ready to integrate advanced artificial intelligence models in clinical laboratory? Biochemia Medica, 35(1), Article 010501. https://doi.org/10.11613/BM.2025.010501