Laboratories face growing pressure to analyze more samples, cover more compounds, and deliver results in less time. These demands span pharmaceutical development, clinical research, environmental testing, and other mass spectrometry applications.
Higher instrument speed can help, but throughput represents only part of the challenge. Laboratories must also produce consistent quantitative results, prevent batch failures, manage limited staff, and process growing volumes of data.
At ASMS 2026, Separation Science discussed these pressures with Chris Hagen, President of SCIEX; Chris Lock, Chief Technology Officer and Vice President of Global Research and Development; and Jose Castro-Perez, Vice President of Product Management. Their insights highlighted five priorities shaping modern mass spectrometry workflows.
Throughput Depends on Data Quality
Shorter analytical cycles enable laboratories to process more samples, but faster methods can introduce trade-offs. Narrower chromatographic peaks leave less time to collect sufficient data. Complex matrices can also increase interference and make compounds more difficult to distinguish.
Laboratories therefore need acquisition methods that balance speed, sensitivity, and selectivity. “You don’t need to compromise quality for throughput,” Castro-Perez asserts. This challenge extends beyond identifying large numbers of compounds. Laboratories need to quantify those compounds with enough precision to support meaningful comparisons.
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Reproducibility becomes especially important in biological research. If repeated measurements produce different sets of identifications, researchers may struggle to determine whether an observed change reflects biology or analytical variation.
Lock points to increasing consistency across repeated analyses. “They see the same IDs every time they run the sample, they see the same number quantified, and there’s a low degree of variation from run to run,” he notes.
Reliable quantitation gives researchers greater confidence that differences between samples reflect genuine biological variation. It also supports large studies in which laboratories must maintain comparable performance across hundreds or thousands of injections.
Instrument Monitoring Can Protect Valuable Batches
Greater throughput raises the cost of unexpected instrument failure. An undetected performance problem can compromise an entire batch before an analyst recognizes it.
The effects may include lost instrument time, repeated sample preparation, additional solvent consumption, and delayed results. Laboratories working with scarce biological material face a greater risk because they may have no opportunity to repeat the analysis.
“You want to know that before you run a batch,” Castro-Perez stresses. Performance-monitoring tools can track changes in instrument or assay behavior and alert users when a system requires cleaning, maintenance, or service. This information enables laboratories to intervene before performance falls outside acceptable limits.
Monitoring also supports instrument selection. If a laboratory operates several systems, staff can direct a critical batch toward an instrument with stable performance rather than risk using one that shows signs of decline.
The operational value increases with laboratory size. Hagen notes that monitoring can help a facility with one instrument, but laboratories managing 10, 50, or 100 systems face a more complex problem. They must coordinate preventive maintenance, manage downtime, and maintain enough capacity to meet testing schedules.
Instrument-health data can support those decisions. It can also help laboratories distinguish an instrument problem from an issue involving liquid chromatography, the column, sample preparation, or the assay itself.
Guided Workflows Can Ease Staffing Pressure
Many laboratories struggle to recruit and retain scientists with extensive mass spectrometry experience. At the same time, instruments increasingly support users whose primary expertise lies in biology, chemistry, or another scientific discipline.
This shift creates demand for systems that guide routine operation and make problems easier to diagnose. Scientists should not need specialist knowledge to recognize every source of declining performance.
Lock describes a laboratory model in which a bench scientist can operate the mass spectrometer as part of a wider experiment. The organization can then focus its specialists on method development, complex troubleshooting, and interpretation instead of routine instrument operation.
This approach does not remove the need for expertise. It directs that expertise toward work where it adds the most value.
Clear diagnostics remain essential. A warning that performance has declined offers limited help unless the user can identify the likely cause and the next action. Useful guidance should help determine whether the problem originates in the mass spectrometer, LC system, column, sample preparation, or analytical method.
Automation Must Cover the Complete Workflow
Faster acquisition can move a bottleneck downstream. Laboratories may collect data faster than analysts can process, review, and report it.
Rules-based automation offers one response. Analysts can define how software should acquire data, process results, apply review criteria, and generate reports. The workflow then proceeds without requiring a user to initiate each stage.
“You can set the entirety of that workflow,” Lock explains. “You don’t have to intervene unless something gets flagged.” Review by exception can further reduce the manual burden. Instead of inspecting every peak or result, analysts focus on data that fail predefined criteria or require scientific judgment.
This model could prove valuable in regulated laboratories, where staff may spend hours reviewing integrations across numerous analytes and samples. Automated screening can direct attention toward questionable results while keeping scientists responsible for final decisions.
The quality of the rules will determine the value of this approach. Laboratories must establish suitable thresholds, validate automated processes, and confirm that the software identifies the types of errors relevant to each method.
AI Needs a Defined Laboratory Role
Artificial intelligence attracted significant attention at ASMS 2026. Yet broad discussions about its potential often provide little guidance on how laboratories should use it.
“We do believe it’s important. We do believe it’ll be transformational,” Hagen notes. He also emphasizes the need to connect AI with specific customer problems.
In mass spectrometry, practical applications could include guiding users through software, flagging unusual performance, supporting troubleshooting, or identifying results that require review. Embedding these functions within existing workflows may prove more useful than requiring scientists to move data into a separate AI platform.
Lock argues that the industry must focus on the practical customer implications rather than discuss AI as an abstract future capability.
Laboratories should apply the same scrutiny to AI tools that they use for other analytical technologies. They need to understand the intended task, required data, decision criteria, limitations, and level of human oversight. Regulated laboratories must also consider validation, traceability, and data integrity.
From Faster Instruments to Stronger Workflows
The next phase of mass spectrometry development will involve more than sensitivity and acquisition speed. Laboratories need technologies that produce repeatable quantitative data, identify performance problems before batches fail, and reduce the time required to move from sample to result.
These capabilities must work together. Faster acquisition provides limited benefit if analysts face a growing data-review backlog. Automation creates risk if laboratories cannot validate its decisions. Instrument monitoring adds little value if alerts fail to guide users toward the correct response.
The central question has therefore shifted. Laboratories no longer need to ask only how quickly an instrument can collect data. They must determine whether the full workflow can convert higher throughput into reliable, defensible results.
